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1. Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding

Author: Jiang, He, Wang, Yutong, Veerapaneni, Rishi, Duhan, Tanishq, Sartoretti, Guillaume, and Li, Jiaoyang
Subjects: Computer Science - Multiagent Systems, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Robotics
Abstract: Lifelong Multi-Agent Path Finding (LMAPF) is a variant of MAPF where agents are continually assigned new goals, necessitating frequent re-planning to accommodate these dynamic changes. Recently, this field has embraced learning-based methods, which reactively generate single-step actions based on individual local observations. However, it is still challenging for them to match the performance of the best search-based algorithms, especially in large-scale settings. This work proposes an imitation-learning-based LMAPF solver that introduces a novel communication module and systematic single-step collision resolution and global guidance techniques. Our proposed solver, Scalable Imitation Learning for LMAPF (SILLM), inherits the fast reasoning speed of learning-based methods and the high solution quality of search-based methods with the help of modern GPUs. Across six large-scale maps with up to 10,000 agents and varying obstacle structures, SILLM surpasses the best learning- and search-based baselines, achieving average throughput improvements of 137.7% and 16.0%, respectively. Furthermore, SILLM also beats the winning solution of the 2023 League of Robot Runners, an international LMAPF competition sponsored by Amazon Robotics. Finally, we validated SILLM with 10 real robots and 100 virtual robots in a mockup warehouse environment., Comment: Submitted to ICRA 2025
Published: 2024

2. DARE: Diffusion Policy for Autonomous Robot Exploration

Author: Cao, Yuhong, Lew, Jeric, Liang, Jingsong, Cheng, Jin, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: Autonomous robot exploration requires a robot to efficiently explore and map unknown environments. Compared to conventional methods that can only optimize paths based on the current robot belief, learning-based methods show the potential to achieve improved performance by drawing on past experiences to reason about unknown areas. In this paper, we propose DARE, a novel generative approach that leverages diffusion models trained on expert demonstrations, which can explicitly generate an exploration path through one-time inference. We build DARE upon an attention-based encoder and a diffusion policy model, and introduce ground truth optimal demonstrations for training to learn better patterns for exploration. The trained planner can reason about the partial belief to recognize the potential structure in unknown areas and consider these areas during path planning. Our experiments demonstrate that DARE achieves on-par performance with both conventional and learning-based state-of-the-art exploration planners, as well as good generalizability in both simulations and real-life scenarios.
Published: 2024

3. IR2: Implicit Rendezvous for Robotic Exploration Teams under Sparse Intermittent Connectivity

Author: Tan, Derek Ming Siang, Ma, Yixiao, Liang, Jingsong, Chng, Yi Cheng, Cao, Yuhong, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: Information sharing is critical in time-sensitive and realistic multi-robot exploration, especially for smaller robotic teams in large-scale environments where connectivity may be sparse and intermittent. Existing methods often overlook such communication constraints by assuming unrealistic global connectivity. Other works account for communication constraints (by maintaining close proximity or line of sight during information exchange), but are often inefficient. For instance, preplanned rendezvous approaches typically involve unnecessary detours resulting from poorly timed rendezvous, while pursuit-based approaches often result in short-sighted decisions due to their greedy nature. We present IR2, a deep reinforcement learning approach to information sharing for multi-robot exploration. Leveraging attention-based neural networks trained via reinforcement and curriculum learning, IR2 allows robots to effectively reason about the longer-term trade-offs between disconnecting for solo exploration and reconnecting for information sharing. In addition, we propose a hierarchical graph formulation to maintain a sparse yet informative graph, enabling our approach to scale to large-scale environments. We present simulation results in three large-scale Gazebo environments, which show that our approach yields 6.6-34.1% shorter exploration paths and significantly improved mapped area consistency among robots when compared to state-of-the-art baselines. Our simulation training and testing code is available at https://github.com/marmotlab/IR2., Comment: \c{opyright} 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Published: 2024

4. Hybrid Training for Enhanced Multi-task Generalization in Multi-agent Reinforcement Learning

Author: Zhang, Mingliang, Su, Sichang, He, Chengyang, and Sartoretti, Guillaume
Subjects: Computer Science - Machine Learning, Computer Science - Multiagent Systems
Abstract: In multi-agent reinforcement learning (MARL), achieving multi-task generalization to diverse agents and objectives presents significant challenges. Existing online MARL algorithms primarily focus on single-task performance, but their lack of multi-task generalization capabilities typically results in substantial computational waste and limited real-life applicability. Meanwhile, existing offline multi-task MARL approaches are heavily dependent on data quality, often resulting in poor performance on unseen tasks. In this paper, we introduce HyGen, a novel hybrid MARL framework, Hybrid Training for Enhanced Multi-Task Generalization, which integrates online and offline learning to ensure both multi-task generalization and training efficiency. Specifically, our framework extracts potential general skills from offline multi-task datasets. We then train policies to select the optimal skills under the centralized training and decentralized execution paradigm (CTDE). During this stage, we utilize a replay buffer that integrates both offline data and online interactions. We empirically demonstrate that our framework effectively extracts and refines general skills, yielding impressive generalization to unseen tasks. Comparative analyses on the StarCraft multi-agent challenge show that HyGen outperforms a wide range of existing solely online and offline methods.
Published: 2024

5. HDPlanner: Advancing Autonomous Deployments in Unknown Environments through Hierarchical Decision Networks

Author: Liang, Jingsong, Cao, Yuhong, Ma, Yixiao, Zhao, Hanqi, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: In this paper, we introduce HDPlanner, a deep reinforcement learning (DRL) based framework designed to tackle two core and challenging tasks for mobile robots: autonomous exploration and navigation, where the robot must optimize its trajectory adaptively to achieve the task objective through continuous interactions in unknown environments. Specifically, HDPlanner relies on novel hierarchical attention networks to empower the robot to reason about its belief across multiple spatial scales and sequence collaborative decisions, where our networks decompose long-term objectives into short-term informative task assignments and informative path plannings. We further propose a contrastive learning-based joint optimization to enhance the robustness of HDPlanner. We empirically demonstrate that HDPlanner significantly outperforms state-of-the-art conventional and learning-based baselines on an extensive set of simulations, including hundreds of test maps and large-scale, complex Gazebo environments. Notably, HDPlanner achieves real-time planning with travel distances reduced by up to 35.7% compared to exploration benchmarks and by up to 16.5% than navigation benchmarks. Furthermore, we validate our approach on hardware, where it generates high-quality, adaptive trajectories in both indoor and outdoor environments, highlighting its real-world applicability without additional training., Comment: Submitted to RA-L
Published: 2024

6. Social Behavior as a Key to Learning-based Multi-Agent Pathfinding Dilemmas

Author: He, Chengyang, Duhan, Tanishq, Tulsyan, Parth, Kim, Patrick, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: The Multi-agent Path Finding (MAPF) problem involves finding collision-free paths for a team of agents in a known, static environment, with important applications in warehouse automation, logistics, or last-mile delivery. To meet the needs of these large-scale applications, current learning-based methods often deploy the same fully trained, decentralized network to all agents to improve scalability. However, such parameter sharing typically results in homogeneous behaviors among agents, which may prevent agents from breaking ties around symmetric conflict (e.g., bottlenecks) and might lead to live-/deadlocks. In this paper, we propose SYLPH, a novel learning-based MAPF framework aimed to mitigate the adverse effects of homogeneity by allowing agents to learn and dynamically select different social behaviors (akin to individual, dynamic roles), without affecting the scalability offered by parameter sharing. Specifically, SYLPH agents learn to select their Social Value Orientation (SVO) given the situation at hand, quantifying their own level of selfishness/altruism, as well as an SVO-conditioned MAPF policy dictating their movement actions. To these ends, each agent first determines the most influential other agent in the system by predicting future conflicts/interactions with other agents. Each agent selects its own SVO towards that agent, and trains its decentralized MAPF policy to enact this SVO until another agent becomes more influential. To further allow agents to consider each others' social preferences, each agent gets access to the SVO value of their neighbors. As a result of this hierarchical decision-making and exchange of social preferences, SYLPH endows agents with the ability to reason about the MAPF task through more latent spaces and nuanced contexts, leading to varied responses that can help break ties around symmetric conflicts. [...], Comment: Submitted to Springer's Artificial Intelligence Journal
Published: 2024

7. Privileged Reinforcement and Communication Learning for Distributed, Bandwidth-limited Multi-robot Exploration

Author: Ma, Yixiao, Liang, Jingsong, Cao, Yuhong, Tan, Derek Ming Siang, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: Communication bandwidth is an important consideration in multi-robot exploration, where information exchange among robots is critical. While existing methods typically aim to reduce communication throughput, they either require significant computation or significantly compromise exploration efficiency. In this work, we propose a deep reinforcement learning framework based on communication and privileged reinforcement learning to achieve a significant reduction in bandwidth consumption, while minimally sacrificing exploration efficiency. Specifically, our approach allows robots to learn to embed the most salient information from their individual belief (partial map) over the environment into fixed-sized messages. Robots then reason about their own belief as well as received messages to distributedly explore the environment while avoiding redundant work. In doing so, we employ privileged learning and learned attention mechanisms to endow the critic (i.e., teacher) network with ground truth map knowledge to effectively guide the policy (i.e., student) network during training. Compared to relevant baselines, our model allows the team to reduce communication by up to two orders of magnitude, while only sacrificing a marginal 2.4\% in total travel distance, paving the way for efficient, distributed multi-robot exploration in bandwidth-limited scenarios., Comment: Accepted by DARS2024
Published: 2024

8. STAR: Swarm Technology for Aerial Robotics Research

Author: Chiun, Jimmy, Tan, Yan Rui, Cao, Yuhong, Tan, John, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: In recent years, the field of aerial robotics has witnessed significant progress, finding applications in diverse domains, including post-disaster search and rescue operations. Despite these strides, the prohibitive acquisition costs associated with deploying physical multi-UAV systems have posed challenges, impeding their widespread utilization in research endeavors. To overcome these challenges, we present STAR (Swarm Technology for Aerial Robotics Research), a framework developed explicitly to improve the accessibility of aerial swarm research experiments. Our framework introduces a swarm architecture based on the Crazyflie, a low-cost, open-source, palm-sized aerial platform, well suited for experimental swarm algorithms. To augment cost-effectiveness and mitigate the limitations of employing low-cost robots in experiments, we propose a landmark-based localization module leveraging fiducial markers. This module, also serving as a target detection module, enhances the adaptability and versatility of the framework. Additionally, collision and obstacle avoidance are implemented through velocity obstacles. The presented work strives to bridge the gap between theoretical advances and tangible implementations, thus fostering progress in the field.
Published: 2024

9. LNS2+RL: Combining Multi-agent Reinforcement Learning with Large Neighborhood Search in Multi-agent Path Finding

Author: Wang, Yutong, Duhan, Tanishq, Li, Jiaoyang, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: Multi-Agent Path Finding (MAPF) is a critical component of logistics and warehouse management, which focuses on planning collision-free paths for a team of robots in a known environment. Recent work introduced a novel MAPF approach, LNS2, which proposed to repair a quickly-obtainable set of infeasible paths via iterative re-planning, by relying on a fast, yet lower-quality, priority-based planner. At the same time, there has been a recent push for Multi-Agent Reinforcement Learning (MARL) based MAPF algorithms, which let agents learn decentralized policies that exhibit improved cooperation over such priority planning, although inevitably remaining slower. In this paper, we introduce a new MAPF algorithm, LNS2+RL, which combines the distinct yet complementary characteristics of LNS2 and MARL to effectively balance their individual limitations and get the best from both worlds. During early iterations, LNS2+RL relies on MARL for low-level re-planning, which we show eliminates collisions much more than a priority-based planner. There, our MARL-based planner allows agents to reason about past and future/predicted information to gradually learn cooperative decision-making through a finely designed curriculum learning. At later stages of planning, LNS2+RL adaptively switches to priority-based planning to quickly resolve the remaining collisions, naturally trading-off solution quality and computational efficiency. Our comprehensive experiments on challenging tasks across various team sizes, world sizes, and map structures consistently demonstrate the superior performance of LNS2+RL compared to many MAPF algorithms, including LNS2, LaCAM, and EECBS, where LNS2+RL shows significantly better performance in complex scenarios. We finally experimentally validate our algorithm in a hybrid simulation of a warehouse mockup involving a team of 100 (real-world and simulated) robots.
Published: 2024

10. Learning-based Hierarchical Control: Emulating the Central Nervous System for Bio-Inspired Legged Robot Locomotion

Author: Sun, Ge, Shafiee, Milad, Li, Peizhuo, Bellegarda, Guillaume, Ijspeert, Auke, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: Animals possess a remarkable ability to navigate challenging terrains, achieved through the interplay of various pathways between the brain, central pattern generators (CPGs) in the spinal cord, and musculoskeletal system. Traditional bioinspired control frameworks often rely on a singular control policy that models both higher (supraspinal) and spinal cord functions. In this work, we build upon our previous research by introducing two distinct neural networks: one tasked with modulating the frequency and amplitude of CPGs to generate the basic locomotor rhythm (referred to as the spinal policy, SCP), and the other responsible for receiving environmental perception data and directly modulating the rhythmic output from the SCP to execute precise movements on challenging terrains (referred to as the descending modulation policy). This division of labor more closely mimics the hierarchical locomotor control systems observed in legged animals, thereby enhancing the robot's ability to navigate various uneven surfaces, including steps, high obstacles, and terrains with gaps. Additionally, we investigate the impact of sensorimotor delays within our framework, validating several biological assumptions about animal locomotion systems. Specifically, we demonstrate that spinal circuits play a crucial role in generating the basic locomotor rhythm, while descending pathways are essential for enabling appropriate gait modifications to accommodate uneven terrain. Notably, our findings also reveal that the multi-layered control inherent in animals exhibits remarkable robustness against time delays. Through these investigations, this paper contributes to a deeper understanding of the fundamental principles of interplay between spinal and supraspinal mechanisms in biological locomotion. It also supports the development of locomotion controllers in parallel to biological structures which are ..., Comment: Submitted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)
Published: 2024

11. Discovery of a dormant 33 solar-mass black hole in pre-release Gaia astrometry

Author: Gaia Collaboration, Panuzzo, P., Mazeh, T., Arenou, F., Holl, B., Caffau, E., Jorissen, A., Babusiaux, C., Gavras, P., Sahlmann, J., Bastian, U., Wyrzykowski, Ł., Eyer, L., Leclerc, N., Bauchet, N., Bombrun, A., Mowlavi, N., Seabroke, G. M., Teyssier, D., Balbinot, E., Helmi, A., Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Barbier, A., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U., Lindegren, L., Luri, X., Mignard, F., Nicolas, C., Randich, S., Sartoretti, P., Smiljanic, R., Tanga, P., Walton, N. A., Aerts, C., Bailer-Jones, C. A. L., Cropper, M., Drimmel, R., Jansen, F., Katz, D., Lattanzi, M. G., Soubiran, C., Thévenin, F., van Leeuwen, F., Andrae, R., Audard, M., Bakker, J., Blomme, R., Castañeda, J., De Angeli, F., Fabricius, C., Fouesneau, M., Frémat, Y., Galluccio, L., Guerrier, A., Heiter, U., Masana, E., Messineo, R., Nienartowicz, K., Pailler, F., Riclet, F., Roux, W., Sordo, R., Gracia-Abril, G., Portell, J., Altmann, M., Benson, K., Berthier, J., Burgess, P. W., Busonero, D., Busso, G., Cacciari, C., Cánovas, H., Carrasco, J. M., Carry, B., Cellino, A., Cheek, N., Clementini, G., Damerdji, Y., Davidson, M., de Teodoro, P., Delchambre, L., Dell'Oro, A., Garcia, E. Fraile, Garabato, D., García-Lario, P., Haigron, R., Hambly, N. C., Harrison, D. L., Hatzidimitriou, D., Hernández, J., Hestroffer, D., Hodgkin, S. T., Jamal, S., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Lorca, A., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Campos, M. Nuñez, Oreshina-Slezak, I., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Riello, M., Rimoldini, L., Robin, A. C., Roegiers, T., Sarro, L. M., Schultheis, M., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Weingrill, K., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Ahmed, S., Altavilla, G., Álvarez, M. A., Anders, F., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baig, S., Baines, D., Baker, S. G., Balaguer-Núñez, L., Balog, Z., Barache, C., Barros, M., Barstow, M. A., Bartolomé, S., Bashi, D., Bassilana, J. -L., Baudeau, N., Becciani, U., Bedin, L. R., Bellas-Velidis, I., Bellazzini, M., Beordo, W., Bernet, M., Bertolotto, C., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Bland-Hawthorn, J., Blazere, A., Boch, T., Bossini, D., Bouquillon, S., Bragaglia, A., Braine, J., Bratsolis, E., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Butkevich, A. G., Buzzi, R., Camut, A., Cancelliere, R., Cantat-Gaudin, T., Guilarte, D. Capilla, Carballo, R., Carlucci, T., Carnerero, M. I., Carretero, J., Carton, S., Casamiquela, L., Casey, A., Castellani, M., Castro-Ginard, A., Ceraj, L., Cesare, V., Charlot, P., Chaudet, C., Chemin, L., Chiavassa, A., Chornay, N., Chosson, D., Cooper, W. J., Cornez, T., Cowell, S., Crosta, M., Crowley, C., Reyes, M. Cruz, Dafonte, C., Ponte, M. Dal, David, M., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Torres, A., del Peloso, E. F., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Denis, E., Dharmawardena, T. E., Di Giacomo, F., Diener, C., Distefano, E., Dolding, C., Dsilva, K., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fatović, M., Fedorets, G., Fernández-Hernández, J., Fernique, P., Figueras, F., Fouron, C., Fragkoudi, F., Gai, M., Galinier, M., Garcia-Serrano, A., García-Torres, M., Garofalo, A., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomboc, A., Gomez, A., González-Santamaría, I., Gosset, E., Granvik, M., Barrera, V. Gregori, Gutiérrez-Sánchez, R., Haywood, M., Helmer, A., Hidalgo, S. L., Hilger, T., Hobbs, D., Hottier, C., Huckle, H. E., Jiménez-Arranz, Ó., Campillo, J. Juaristi, Kaczmarek, Z., Kervella, P., Khanna, S., Kontizas, M., Kordopatis, G., Korn, A. J., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Lambert, S., Lanza, A. F., Lebreton, Y., Lebzelter, T., Leccia, S., Lecoutre, G., Liao, S., Liberato, L., Licata, E., Livanou, E., Lobel, A., López-Miralles, J., Loup, C., Madarász, M., Mahy, L., Mann, R. G., Manteiga, M., Marinoni, S., Marcellino, C. P., Marshall, D. J., Mascarenhas, D., Marchant, J. M., Lozano, J. Martín, Masip, A., Marconi, M., Pina, D. Marín, Polo, L. Martin, Martín-Fleitas, J. M., Mastrobuono-Battisti, A., McMillan, P. J., Meichsner, J. G. Marton, Merc, J., Messina, S., Millar, N. R., Mints, A., Mohamed, D., Molina, D., Molinaro, R., Monguió, M., Montegriffo, P., Monti, L., Mora, A., Morbidelli, R., Morris, D., Mudimadugula, R., Muraveva, T., Musella, I., Nagy, Z., Nardetto, N., Navarrete, C., Oh, S., Ordenovic, C., Orenstein, O., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Pawlak, M., Penttilä, A., Pesciullesi, P., Pinamonti, M., Plachy, E., Planquart, L., Plum, G., Poggio, E., Pourbaix, D., Price-Whelan, A. M., Pulone, L., Rabin, V., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Ratajczak, M., Fiorentin, P. Re, Regibo, S., Reylé, C., Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robert, G., Robichon, N., Robin, C., Romero-Gómez, M., Rowell, N., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Sellés, A. Sagristà, Sanna, N., Santoveña, R., Sarasso, M., Sarmiento, M. H., Riera, C. Sarrate, Sciacca, E., Ségransan, D., Semczuk, M., Shahaf, S., Siebert, A., Slezak18, E., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spitoni, E., Spoto, F., Squillante, L. A., Steele, I. A., Steidelmüller, H., Surdej, J., Szabados, L., Taris, F., Taylor, M. B., Teixeira, R., Tepper-Garcia, T., Thuillot, W., Tolomei, L., Tonello, N., Torra, F., Elipe, G. Torralba, Trabucchi, M., Trentin, E., Tsantaki, M., Turon, C., Ulla, A., Unger, N., Valtchanov, I., Vanel, O., Vecchiato, A., Vicente, D., Villar, E., Weiler, M., Zhao, H., Zorec, J., Zucker, S., Župić, A., and Zwitter, T.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Solar and Stellar Astrophysics
Abstract: Gravitational waves from black-hole merging events have revealed a population of extra-galactic BHs residing in short-period binaries with masses that are higher than expected based on most stellar evolution models - and also higher than known stellar-origin black holes in our Galaxy. It has been proposed that those high-mass BHs are the remnants of massive metal-poor stars. Gaia astrometry is expected to uncover many Galactic wide-binary systems containing dormant BHs, which may not have been detected before. The study of this population will provide new information on the BH-mass distribution in binaries and shed light on their formation mechanisms and progenitors. As part of the validation efforts in preparation for the fourth Gaia data release (DR4), we analysed the preliminary astrometric binary solutions, obtained by the Gaia Non-Single Star pipeline, to verify their significance and to minimise false-detection rates in high-mass-function orbital solutions. The astrometric binary solution of one source, Gaia BH3, implies the presence of a 32.70 \pm 0.82 M\odot BH in a binary system with a period of 11.6 yr. Gaia radial velocities independently validate the astrometric orbit. Broad-band photometric and spectroscopic data show that the visible component is an old, very metal-poor giant of the Galactic halo, at a distance of 590 pc. The BH in the Gaia BH3 system is more massive than any other Galactic stellar-origin BH known thus far. The low metallicity of the star companion supports the scenario that metal-poor massive stars are progenitors of the high-mass BHs detected by gravitational-wave telescopes. The Galactic orbit of the system and its metallicity indicate that it might belong to the Sequoia halo substructure. Alternatively, and more plausibly, it could belong to the ED-2 stream, which likely originated from a globular cluster that had been disrupted by the Milky Way., Comment: 23 pages, accepted fro publication in A&A Letters. New version with small fixes
Published: 2024
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12. High speed stars: III. Detailed abundances and binary nature of the extreme speed star GHS143

Author: Caffau, E., Monaco, P. Bonifacio L., Sbordone, L., Spite, M., Francois, P., Panuzzo, P., Sartoretti, P., Chemin, L., Thevenin, F., and Mucciarelli, A.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: The Gaia satellite has provided the community with three releases containing astrometrical and photometric data as well as by products, such as stellar parameters and variability indicators. By selecting in the Gaia database, one can select stars with the requested characteristics, such as high speed. At present any selection is based on available Gaia releases including a subset of the observations. This, for some stars, can show some limitations, for example there is still not a sufficient number of observations to detect binarity. We investigated a star selected in Gaia EDR3 for its high speed that appears unbound to the Galaxy. We requested high-quality spectra to derive more information on the star. {From the spectroscopic investigation we confirm the low metallicity content of the star, and we derive a detailed chemical composition. The star is poor in carbon and very rich in oxygen: [(C+N+O)/Fe]=+0.65. From the two spectra observed we conclude that the star is in a binary system and from the investigation of the ionisation balance we derive that the star is closer than implied by the Gaia DR3 parallax, and thus has a a lower intrinsic luminosity. The star is probably still unbound, but there is the possibility that it is bound to the Galaxy. Its low carbon abundance suggests that the star was formed in a dwarf galaxy., Comment: A&A Volume 684, April 2024
Published: 2024
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13. Long-term trends in total administered radiation dose from brain [18F]FDG-PET in children with drug-resistant epilepsy

Author: Gennari, Antonio G., Waelti, Stephan, Schwyzer, Moritz, Treyer, Valerie, Rossi, Alexia, Sartoretti, Thomas, Maurer, Alexander, Ramantani, Georgia, Tuura O’Gorman, Ruth, Kellenberger, Christian J., Hüllner, Martin W., and Messerli, Michael
Published: 2024
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14. Deep Reinforcement Learning-based Large-scale Robot Exploration

Author: Cao, Yuhong, Zhao, Rui, Wang, Yizhuo, Xiang, Bairan, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: In this work, we propose a deep reinforcement learning (DRL) based reactive planner to solve large-scale Lidar-based autonomous robot exploration problems in 2D action space. Our DRL-based planner allows the agent to reactively plan its exploration path by making implicit predictions about unknown areas, based on a learned estimation of the underlying transition model of the environment. To this end, our approach relies on learned attention mechanisms for their powerful ability to capture long-term dependencies at different spatial scales to reason about the robot's entire belief over known areas. Our approach relies on ground truth information (i.e., privileged learning) to guide the environment estimation during training, as well as on a graph rarefaction algorithm, which allows models trained in small-scale environments to scale to large-scale ones. Simulation results show that our model exhibits better exploration efficiency (12% in path length, 6% in makespan) and lower planning time (60%) than the state-of-the-art planners in a 130m x 100m benchmark scenario. We also validate our learned model on hardware., Comment: \c{opyright} 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Published: 2024

15. The Gaia RVS benchmark stars II. A sample of stars selected for their Gaia high radial velocity

Author: Caffau, E., Katz, D., Gómez, A., Bonifacio, P., Lallement, R., Sartoretti, P., Sbordone, L., Spite, M., Mucciarelli, A., Ibata, R., Chemin, L., Thévenin, F., Panuzzo, P., Leclerc, N., François, P., Ludwig, H. -G., Monaco, L., Haywood, M., and Soubiran, C.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The Gaia satellite has already provided the astronomical community with three data releases, and the Radial Velocity Spectrometer (RVS) on board Gaia has provided the radial velocity for 33 million stars. When deriving the radial velocity from the RVS spectra, several stars are measured to have large values. To verify the credibility of these measurements, we selected some bright stars with the modulus of radial velocity in excess of 500\ to be observed with SOPHIE at OHP and UVES at VLT. This paper is devoted to investigating the chemical composition of the stars observed with UVES. We derived atmospheric parameters using Gaia photometry and parallaxes, and we performed a chemical analysis using the code. We find that the sample consists of metal-poor stars, although none have extremely low metallicities. The abundance patterns match what has been found in other samples of metal-poor stars selected irrespective of their radial velocities. We highlight the presence of three stars with low Cu and Zn abundances that are likely descendants of pair-instability supernovae. Two stars are apparently younger than 1\,Ga, and their masses exceed twice the turn-off mass of metal-poor populations. This makes it unlikely that they are blue stragglers because it would imply they formed from triple or multiple systems. We suggest instead that they are young metal-poor stars accreted from a dwarf galaxy. Finally, we find that the star RVS721 is associated with the Gjoll stream, which itself is associated with the Globular Cluster NGC\,3201., Comment: Astronomy and Astrophysics - A\&A, In press
Published: 2024
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16. ALPHA: Attention-based Long-horizon Pathfinding in Highly-structured Areas

Author: He, Chengyang, Yang, Tianze, Duhan, Tanishq, Wang, Yutong, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: The multi-agent pathfinding (MAPF) problem seeks collision-free paths for a team of agents from their current positions to their pre-set goals in a known environment, and is an essential problem found at the core of many logistics, transportation, and general robotics applications. Existing learning-based MAPF approaches typically only let each agent make decisions based on a limited field-of-view (FOV) around its position, as a natural means to fix the input dimensions of its policy network. However, this often makes policies short-sighted, since agents lack the ability to perceive and plan for obstacles/agents beyond their FOV. To address this challenge, we propose ALPHA, a new framework combining the use of ground truth proximal (local) information and fuzzy distal (global) information to let agents sequence local decisions based on the full current state of the system, and avoid such myopicity. We further allow agents to make short-term predictions about each others' paths, as a means to reason about each others' path intentions, thereby enhancing the level of cooperation among agents at the whole system level. Our neural structure relies on a Graph Transformer architecture to allow agents to selectively combine these different sources of information and reason about their inter-dependencies at different spatial scales. Our simulation experiments demonstrate that ALPHA outperforms both globally-guided MAPF solvers and communication-learning based ones, showcasing its potential towards scalability in realistic deployments., Comment: Submitted to the IEEE International Conference on Robotics and Automation (ICRA 2024)
Published: 2023

17. Gaia Focused Product Release: Sources from Service Interface Function image analysis -- Half a million new sources in omega Centauri

Author: Gaia Collaboration, Weingrill, K., Mints, A., Castañeda, J., Kostrzewa-Rutkowska, Z., Davidson, M., De Angeli, F., Hernández, J., Torra, F., Ramos-Lerate, M., Babusiaux, C., Biermann, M., Crowley, C., Evans, D. W., Lindegren, L., Martín-Fleitas, J. M., Palaversa, L., Mieres, D. Ruz, Tisanić, K., Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Barbier, A., Creevey, O. L., Ducourant, C., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U., Luri, X., Mignard, F., Randich, S., Sartoretti, P., Smiljanic, R., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Cropper, M., Drimmel, R., Katz, D., Soubiran, C., van Leeuwen, F., Audard, M., Bakker, J., Blomme, R., Fabricius, C., Fouesneau, M., Frémat, Y., Galluccio, L., Guerrier, A., Masana, E., Messineo, R., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Sordo, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Benson, K., Berthier, J., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cheek, N., Clementini, G., Damerdji, Y., de Teodoro, P., Delchambre, L., DellÓro, A., Garcia, E. Fraile, Garabato, D., García-Lario, P., Torres, N. Garralda, Gavras, P., Haigron, R., Hambly, N. C., Harrison, D. L., Hatzidimitriou, D., Hodgkin, S. T., Holl, B., Jamal, S., Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Lorca, A., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Campos, M. Nuñez, Oreshina-Slezak, I., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Riello, M., Rimoldini, L., Robin, A. C., Roegiers, T., Sarro, L. M., Schultheis, M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Antoja, T., Baines, D., Baker, S. G., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bashi, D., Bauchet, N., Baudeau, N., Becciani, U., Bedin, L. R., Bellas-Velidis, I., Bellazzini, M., Beordo, W., Berihuete, A., Bernet, M., Bertolotto, C., Bertone, S., Bianchi, L., Binnenfeld, A., Blazere, A., Boch, T., Bombrun, A., Bouquillon, S., Bragaglia, A., Braine, J., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cannizzo, S., Cantat-Gaudin, T., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Carretero, J., Carton, S., Casamiquela, L., Castellani, M., Castro-Ginard, A., Cesare, V., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Collins, R., Contursi, G., Cooper, W. J., Cornez, T., Crosta, M., Dafonte, C., de Laverny, P., De Luise, F., De March, R., de Souza, R., de Torres, A., del Peloso, E. F., Delbo, M., Delgado, A., Dharmawardena, T. E., Diakite, S., Diener, C., Distefano, E., Dolding, C., Dsilva, K., Durán, J., Enke, H., Esquej, P., Fabre, C., Fabrizio, M., Faigler, S., Fatović, M., Fedorets, G., Fernández-Hernández, J., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Gai, M., Galinier, M., Garcia-Gutierrez, A., García-Torres, M., Garofalo, A., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., Gosset, E., Granvik, M., Barrera, V. Gregori, Gutiérrez-Sánchez, R., Haywood, M., Helmer, A., Helmi, A., Henares, K., Hidalgo, S. L., Hilger, T., Hobbs, D., Hottier, C., Huckle, H. E., Jabłońska, M., Jansen, F., Jiménez-Arranz, Ó., Campillo, J. Juaristi, Khanna, S., Kordopatis, G., Kóspál, Á, Kun, M., Lambert, S., Lanza, A. F., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Lecoeur-Taibi, I., Lecoutre, G., Liao, S., Liberato, L., Licata, E., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Loup, C., Mahy, L., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Pina, D. Marín, Marinoni, S., Marshall, D. J., Lozano, J. Martín, Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Meichsner, J., Messina, S., Michalik, D., Millar, N. R., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Mowlavi, N., Munoz, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Nieto, S., Noval, L., Ogden, A., Ordenovic, C., Pagani, C., Pagano, I., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Panem, C., Payne-Wardenaar, S., Pegoraro, L., Penttilä, A., Pesciullesi, P., Piersimoni, A. M., Pinamonti, M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Pourbaix, D., Prša, A., Pulone, L., Racero, E., Rainer, M., Raiteri, C. M., Ramos, P., Ratajczak, M., Fiorentin, P. Re, Regibo, S., Reylé, C., Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, C., Romero-Gómez, M., Rowell, N., Royer, F., Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Riera, C. Sarrate, Sciacca, E., Segovia, J. C., Ségransan, D., Shahaf, S., Siebert, A., Siltala, L., Slezak, E., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spina, L., Spitoni, E., Spoto, F., Squillante, L. A., Steele, I. A., Steidelmüller, H., Surdej, J., Szabados, L., Taris, F., Taylor, M. B., Teixeira, R., Tolomei, L., Elipe, G. Torralba, Trabucchi, M., Tsantaki, M., Ulla, A., Unger, N., Vanel, O., Vecchiato, A., Vicente, D., Voutsinas, S., Weiler, M., Wyrzykowski, Ł., Zhao, H., Zorec, J., Zwitter, T., Balaguer-Núñez, L., Leclerc, N., Morgenthaler, S., Robert, G., and Zucker, S.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: Gaia's readout window strategy is challenged by very dense fields in the sky. Therefore, in addition to standard Gaia observations, full Sky Mapper (SM) images were recorded for nine selected regions in the sky. A new software pipeline exploits these Service Interface Function (SIF) images of crowded fields (CFs), making use of the availability of the full two-dimensional (2D) information. This new pipeline produced half a million additional Gaia sources in the region of the omega Centauri ($\omega$ Cen) cluster, which are published with this Focused Product Release. We discuss the dedicated SIF CF data reduction pipeline, validate its data products, and introduce their Gaia archive table. Our aim is to improve the completeness of the {\it Gaia} source inventory in a very dense region in the sky, $\omega$ Cen. An adapted version of {\it Gaia}'s Source Detection and Image Parameter Determination software located sources in the 2D SIF CF images. We validated the results by comparing them to the public {\it Gaia} DR3 catalogue and external Hubble Space Telescope data. With this Focused Product Release, 526\,587 new sources have been added to the {\it Gaia} catalogue in $\omega$ Cen. Apart from positions and brightnesses, the additional catalogue contains parallaxes and proper motions, but no meaningful colour information. While SIF CF source parameters generally have a lower precision than nominal {\it Gaia} sources, in the cluster centre they increase the depth of the combined catalogue by three magnitudes and improve the source density by a factor of ten. This first SIF CF data publication already adds great value to the {\it Gaia} catalogue. It demonstrates what to expect for the fourth {\it Gaia} catalogue, which will contain additional sources for all nine SIF CF regions.
Published: 2023
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18. Gaia Focused Product Release: A catalogue of sources around quasars to search for strongly lensed quasars

Author: Gaia Collaboration, Krone-Martins, A., Ducourant, C., Galluccio, L., Delchambre, L., Oreshina-Slezak, I., Teixeira, R., Braine, J., Campion, J. -F. Le, Mignard, F., Roux, W., Blazere, A., Pegoraro, L., Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Barbier, A., Biermann, M., Creevey, O. L., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U., Lindegren, L., Luri, X., Randich, S., Sartoretti, P., Smiljanic, R., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Cropper, M., Drimmel, R., Katz, D., Soubiran, C., van Leeuwen, F., Audard, M., Bakker, J., Blomme, R., Castaneda, J., De Angeli, F., Fabricius, C., Fouesneau, M., Fremat, Y., Guerrier, A., Masana, E., Messineo, R., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Seabroke, G. M., Sordo, R., Thevenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Benson, K., Berthier, J., Burgess, P. W., Busonero, D., Busso, G., Canovas, H., Carry, B., Cheek, N., Clementini, G., Damerdji, Y., Davidson, M., de Teodoro, P., Dell'Oro, A., Garcia, E. Fraile, Garabato, D., Garcia-Lario, P., Torres, N. Garralda, Gavras, P., Haigron, R., Hambly, N. C., Harrison, D. L., Hatzidimitriou, D., Hernandez, J., Hodgkin, S. T., Holl, B., Jamal, S., Jordan, S., Lanzafame, A. C., Loffler, W., Lorca, A., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Campos, M. Nunez, Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Riello, M., Rimoldini, L., Robin, A. C., Roegiers, T., Sarro, L. M., Schultheis, M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Weingrill, K., Abbas, U., Abraham, P., Aramburu, A. Abreu, Aerts, C., Altavilla, G., Alvarez, M. A., Alves, J., Anderson, R. I., Antoja, T., Baines, D., Baker, S. G., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolome, S., Bashi, D., Bauchet, N., Baudeau, N., Becciani, U., Bedin, L. R., Bellas-Velidis, I., Bellazzini, M., Beordo, W., Berihuete, A., Bernet, M., Bertolotto, C., Bertone, S., Bianchi, L., Binnenfeld, A., Boch, T., Bombrun, A., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cannizzo, S., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Carretero, J., Carton, S., Casamiquela, L., Castellani, M., Castro-Ginard, A., Cesare, V., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Collins, R., Contursi, G., Cooper, W. J., Cornez, T., Crosta, M., Crowley, C., Dafonte, C., de Laverny, P., De Luise, F., De March, R., de Souza, R., de Torres, A., del Peloso, E. F., Delbo, M., Delgado, A., Dharmawardena, T. E., Diakite, S., Diener, C., Distefano, E., Dolding, C., Dsilva, K., Duran, J., Enke, H., Esquej, P., Fabre, C., Fabrizio, M., Faigler, S., Fatovic, M., Fedorets, G., Fernandez-Hernandez, J., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Gai, M., Galinier, M., Garcia-Gutierrez, A., Garcia-Torres, M., Garofalo, A., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., Gonzalez-Nunez, J., Gonzalez-Santamaria, I., Gosset, E., Granvik, M., Barrera, V. Gregori, Gutierrez-Sanchez, R., Haywood, M., Helmer, A., Helmi, A., Henares, K., Hidalgo, S. L., Hilger, T., Hobbs, D., Hottier, C., Huckle, H. E., Jablonska, M., Jansen, F., Jimenez-Arranz, O., Campillo, J. Juaristi, Khanna, S., Kordopatis, G., Kospal, A., Kostrzewa-Rutkowska, Z., Kun, M., Lambert, S., Lanza, A. F., Lebreton, Y., Lebzelter, T., Leccia, S., Lecoeur-Taibi, I., Lecoutre, G., Liao, S., Liberato, L., Licata, E., Lindstrom, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Loup, C., Mahy, L., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Pina, D. Marin, Marinoni, S., Marshall, D. J., Lozano, J. Martin, Martin-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Meichsner, J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnar, L., Monari, G., Monguio, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Mowlavi, N., Munoz, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Nieto, S., Noval, L., Ogden, A., Ordenovic, C., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Panem, C., Payne-Wardenaar, S., Penttila, A., Pesciullesi, P., Piersimoni, A. M., Pinamonti, M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Pourbaix, D., Prsa, A., Pulone, L., Racero, E., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Ratajczak, M., Fiorentin, P. Re, Regibo, S., Reyle, C., Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, C., Romero-Gomez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Nunez, A. Saez, Selles, A. Sagrista, Sahlmann, J., Gimenez, V. Sanchez, Sanna, N., Santovena, R., Sarasso, M., Riera, C. Sarrate, Sciacca, E., Segovia, J. C., Segransan, D., Shahaf, S., Siebert, A., Siltala, L., Slezak, E., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spina, L., Spitoni, E., Spoto, F., Squillante, L. A., Steele, I. A., Steidelmuller, H., Surdej, J., Szabados, L., Taris, F., Taylor, M. B., Tisanic, K., Tolomei, L., Torra, F., Elipe, G. Torralba, Trabucchi, M., Tsantaki, M., Ulla, A., Unger, N., Vanel, O., Vecchiato, A., Vicente, D., Voutsinas, S., Weiler, M., Wyrzykowski, L., Zhao, H., Zorec, J., Zwitter, T., Balaguer-Nunez, L., Leclerc, N., Morgenthaler, S., Robert, G., and Zucker, S.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Context. Strongly lensed quasars are fundamental sources for cosmology. The Gaia space mission covers the entire sky with the unprecedented resolution of $0.18$" in the optical, making it an ideal instrument to search for gravitational lenses down to the limiting magnitude of 21. Nevertheless, the previous Gaia Data Releases are known to be incomplete for small angular separations such as those expected for most lenses. Aims. We present the Data Processing and Analysis Consortium GravLens pipeline, which was built to analyse all Gaia detections around quasars and to cluster them into sources, thus producing a catalogue of secondary sources around each quasar. We analysed the resulting catalogue to produce scores that indicate source configurations that are compatible with strongly lensed quasars. Methods. GravLens uses the DBSCAN unsupervised clustering algorithm to detect sources around quasars. The resulting catalogue of multiplets is then analysed with several methods to identify potential gravitational lenses. We developed and applied an outlier scoring method, a comparison between the average BP and RP spectra of the components, and we also used an extremely randomised tree algorithm. These methods produce scores to identify the most probable configurations and to establish a list of lens candidates. Results. We analysed the environment of 3 760 032 quasars. A total of 4 760 920 sources, including the quasars, were found within 6" of the quasar positions. This list is given in the Gaia archive. In 87\% of cases, the quasar remains a single source, and in 501 385 cases neighbouring sources were detected. We propose a list of 381 lensed candidates, of which we identified 49 as the most promising. Beyond these candidates, the associate tables in this Focused Product Release allow the entire community to explore the unique Gaia data for strong lensing studies further., Comment: 35 pages, 60 figures, accepted for publication by Astronomy and Astrophysics
Published: 2023
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19. DecAP: Decaying Action Priors for Accelerated Imitation Learning of Torque-Based Legged Locomotion Policies

Author: Sood, Shivam, Sun, Ge, Li, Peizhuo, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: Optimal Control for legged robots has gone through a paradigm shift from position-based to torque-based control, owing to the latter's compliant and robust nature. In parallel to this shift, the community has also turned to Deep Reinforcement Learning (DRL) as a promising approach to directly learn locomotion policies for complex real-life tasks. However, most end-to-end DRL approaches still operate in position space, mainly because learning in torque space is often sample-inefficient and does not consistently converge to natural gaits. To address these challenges, we propose a two-stage framework. In the first stage, we generate our own imitation data by training a position-based policy, eliminating the need for expert knowledge to design optimal controllers. The second stage incorporates decaying action priors, a novel method to enhance the exploration of torque-based policies aided by imitation rewards. We show that our approach consistently outperforms imitation learning alone and is robust to scaling these rewards from 0.1x to 10x. We further validate the benefits of torque control by comparing the robustness of a position-based policy to a position-assisted torque-based policy on a quadruped (Unitree Go1) without any domain randomization in the form of external disturbances during training., Comment: \c{opyright} 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Published: 2023

20. Gaia Focused Product Release: Radial velocity time series of long-period variables

Author: Gaia Collaboration, Trabucchi, M., Mowlavi, N., Lebzelter, T., Lecoeur-Taibi, I., Audard, M., Eyer, L., García-Lario, P., Gavras, P., Holl, B., de Fombelle, G. Jevardat, Nienartowicz, K., Rimoldini, L., Sartoretti, P., Blomme, R., Frémat, Y., Marchal, O., Damerdji, Y., Brown, A. G. A., Guerrier, A., Panuzzo, P., Katz, D., Seabroke, G. M., Benson, K., Haigron, R., Smith, M., Lobel, A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Barbier, A., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U., Lindegren, L., Luri, X., Mignard, F., Randich, S., Smiljanic, R., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Cropper, M., Drimmel, R., Lattanzi, M. G., Soubiran, C., van Leeuwen, F., Bakker, J., Castañeda, J., De Angeli, F., Fabricius, C., Fouesneau, M., Galluccio, L., Masana, E., Messineo, R., Nicolas, C., Pailler, F., Riclet, F., Roux, W., Sordo, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Berthier, J., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cheek, N., Clementini, G., Davidson, M., de Teodoro, P., Delchambre, L., Dell'Oro, A., Garcia, E. Fraile, Garabato, D., Torres, N. Garralda, Hambly, N. C., Harrison, D. L., Hatzidimitriou, D., Hernández, J., Hodgkin, S. T., Jamal, S., Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Lorca, A., Marrese, P. M., Moitinho, A., Muinonen, K., Campos, M. Nuñez, Oreshina-Slezak, I., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Riello, M., Robin, A. C., Roegiers, T., Sarro, L. M., Schultheis, M., Siopis, C., Sozzetti, A., Utrilla, E., van Leeuwen, M., Weingrill, K., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Antoja, T., Baines, D., Baker, S. G., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bashi, D., Bauchet, N., Baudeau, N., Becciani, U., Bedin, L. R., Bellas-Velidis, I., Bellazzini, M., Beordo, W., Berihuete, A., Bernet, M., Bertolotto, C., Bertone, S., Bianchi, L., Binnenfeld, A., Blazere, A., Boch, T., Bombrun, A., Bouquillon, S., Bragaglia, A., Braine, J., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cannizzo, S., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Carretero, J., Carton, S., Casamiquela, L., Castellani, M., Castro-Ginard, A., Cesare, V., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Collins, R., Contursi, G., Cooper, W. J., Cornez, T., Crosta, M., Crowley, C., Dafonte, C., David, M., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., Delbo, M., Delgado, A., Dharmawardena, T. E., Diakite, S., Diener, C., Distefano, E., Dolding, C., Dsilva, K., Durán, J., Enke, H., Esquej, P., Fabre, C., Fabrizio, M., Faigler, S., Fatović, M., Fedorets, G., Fernández-Hernández, J., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Gai, M., Galinier, M., Garcia-Gutierrez, A., García-Torres, M., Garofalo, A., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., Gosset, E., Granvik, M., Barrera, V. Gregori, Gutiérrez-Sánchez, R., Haywood, M., Helmer, A., Helmi, A., Henares, K., Hidalgo, S. L., Hilger, T., Hobbs, D., Hottier, C., Huckle, H. E., Jabłońska, M., Jansen, F., Jiménez-Arranz, Ó., Campillo, J. Juaristi, Khanna, S., Kordopatis, G., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kun, M., Lambert, S., Lanza, A. F., Campion, J. -F. Le, Lebreton, Y., Leccia, S., Lecoutre, G., Liao, S., Liberato, L., Licata, E., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Loup, C., Mahy, L., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Pina, D. Marín, Marinoni, S., Marshall, D. J., Lozano, J. Martín, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Meichsner, J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Munoz, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Nieto, S., Noval, L., Ogden, A., Ordenovic, C., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Panem, C., Payne-Wardenaar, S., Pegoraro, L., Penttilä, A., Pesciullesi, P., Piersimoni, A. M., Pinamonti, M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Pourbaix, D., Prša, A., Pulone, L., Racero, E., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Ratajczak, M., Fiorentin, P. Re, Regibo, S., Reylé, C., Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, C., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Riera, C. Sarrate, Sciacca, E., Segovia, J. C., Ségransan, D., Shahaf, S., Siebert, A., Siltala, L., Slezak, E., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spina, L., Spitoni, E., Spoto, F., Squillante, L. A., Steele, I. A., Steidelmüller, H., Surdej, J., Szabados, L., Taris, F., Taylor, M. B., Teixeira, R., Tisanić, K., Tolomei, L., Torra, F., Elipe, G. Torralba, Tsantaki, M., Ulla, A., Unger, N., Vanel, O., Vecchiato, A., Vicente, D., Voutsinas, S., Weiler, M., Wyrzykowski, Ł., Zhao, H., Zorec, J., Zwitter, T., Balaguer-Núñez, L., Leclerc, N., Morgenthaler, S., Robert, G., and Zucker, S.
Subjects: Astrophysics - Solar and Stellar Astrophysics
Abstract: The third Gaia Data Release (DR3) provided photometric time series of more than 2 million long-period variable (LPV) candidates. Anticipating the publication of full radial-velocity (RV) in DR4, this Focused Product Release (FPR) provides RV time series for a selection of LPVs with high-quality observations. We describe the production and content of the Gaia catalog of LPV RV time series, and the methods used to compute variability parameters published in the Gaia FPR. Starting from the DR3 LPVs catalog, we applied filters to construct a sample of sources with high-quality RV measurements. We modeled their RV and photometric time series to derive their periods and amplitudes, and further refined the sample by requiring compatibility between the RV period and at least one of the $G$, $G_{\rm BP}$, or $G_{\rm RP}$ photometric periods. The catalog includes RV time series and variability parameters for 9\,614 sources in the magnitude range $6\lesssim G/{\rm mag}\lesssim 14$, including a flagged top-quality subsample of 6\,093 stars whose RV periods are fully compatible with the values derived from the $G$, $G_{\rm BP}$, and $G_{\rm RP}$ photometric time series. The RV time series contain a mean of 24 measurements per source taken unevenly over a duration of about three years. We identify the great most sources (88%) as genuine LPVs, with about half of them showing a pulsation period and the other half displaying a long secondary period. The remaining 12% consists of candidate ellipsoidal binaries. Quality checks against RVs available in the literature show excellent agreement. We provide illustrative examples and cautionary remarks. The publication of RV time series for almost 10\,000 LPVs constitutes, by far, the largest such database available to date in the literature. The availability of simultaneous photometric measurements gives a unique added value to the Gaia catalog (abridged), Comment: 36 pages, 38 figures
Published: 2023

21. Author Correction: Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks

Author: Erdil, Ertunc, Becker, Anton S., Schwyzer, Moritz, Martinez-Tellez, Borja, Ruiz, Jonatan R., Sartoretti, Thomas, Vargas, H. Alberto, Burger, A. Irene, Chirindel, Alin, Wild, Damian, Zamboni, Nicola, Deplancke, Bart, Gardeux, Vincent, Maushart, Claudia Irene, Betz, Matthias Johannes, Wolfrum, Christian, and Konukoglu, Ender
Published: 2024
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22. Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks

Author: Erdil, Ertunc, Becker, Anton S., Schwyzer, Moritz, Martinez-Tellez, Borja, Ruiz, Jonatan R., Sartoretti, Thomas, Vargas, H. Alberto, Burger, A. Irene, Chirindel, Alin, Wild, Damian, Zamboni, Nicola, Deplancke, Bart, Gardeux, Vincent, Maushart, Claudia Irene, Betz, Matthias Johannes, Wolfrum, Christian, and Konukoglu, Ender
Published: 2024
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23. Evaluating the biodistribution for [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET/CT with an inter- and intrapatient based analysis

Author: Popescu, Cristina E., Zhang, Boya, Sartoretti, Thomas, Spielhofer, Noel, Skawran, Stephan, Heimer, Jakob, Messerli, Michael, Sauter, Alexander, Huellner, Martin W., Kaufmann, Philipp A., Burger, Irene A., and Maurer, Alexander
Published: 2024
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24. Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes

Author: Gennari, Antonio G., Rossi, Alexia, De Cecco, Carlo N., van Assen, Marly, Sartoretti, Thomas, Giannopoulos, Andreas A., Schwyzer, Moritz, Huellner, Martin W., and Messerli, Michael
Published: 2024
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25. A third of the radiotracer dose: two decades of progress in pediatric [18F]fluorodeoxyglucose PET/CT and PET/MR imaging

Author: Waelti, Stephan, Skawran, Stephan, Sartoretti, Thomas, Schwyzer, Moritz, Gennari, Antonio G., Mader, Cäcilia, Treyer, Valerie, Kellenberger, Christian J., Burger, Irene A., Hany, Thomas, Maurer, Alexander, Huellner, Martin W., and Messerli, Michael
Published: 2024
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26. Fully automated computational measurement of noise in positron emission tomography

Author: Sartoretti, Thomas, Skawran, Stephan, Gennari, Antonio G., Maurer, Alexander, Euler, André, Treyer, Valerie, Sartoretti, Elisabeth, Waelti, Stephan, Schwyzer, Moritz, von Schulthess, Gustav K., Burger, Irene A., Huellner, Martin W., and Messerli, Michael
Published: 2024
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27. SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control

Author: Goel, Harsh, Zhang, Yifeng, Damani, Mehul, and Sartoretti, Guillaume
Subjects: Computer Science - Machine Learning
Abstract: Many recent works have turned to multi-agent reinforcement learning (MARL) for adaptive traffic signal control to optimize the travel time of vehicles over large urban networks. However, achieving effective and scalable cooperation among junctions (agents) remains an open challenge, as existing methods often rely on extensive, non-generalizable reward shaping or on non-scalable centralized learning. To address these problems, we propose a new MARL method for traffic signal control, SocialLight, which learns cooperative traffic control policies by distributedly estimating the individual marginal contribution of agents on their local neighborhood. SocialLight relies on the Asynchronous Actor Critic (A3C) framework, and makes learning scalable by learning a locally-centralized critic conditioned over the states and actions of neighboring agents, used by agents to estimate individual contributions by counterfactual reasoning. We further introduce important modifications to the advantage calculation that help stabilize policy updates. These modifications decouple the impact of the neighbors' actions on the computed advantages, thereby reducing the variance in the gradient updates. We benchmark our trained network against state-of-the-art traffic signal control methods on standard benchmarks in two traffic simulators, SUMO and CityFlow. Our results show that SocialLight exhibits improved scalability to larger road networks and better performance across usual traffic metrics., Comment: To appear in the International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023)
Published: 2023

28. Predicting standardized uptake value of brown adipose tissue from CT scans using convolutional neural networks

Author: Ertunc Erdil, Anton S. Becker, Moritz Schwyzer, Borja Martinez-Tellez, Jonatan R. Ruiz, Thomas Sartoretti, H. Alberto Vargas, A. Irene Burger, Alin Chirindel, Damian Wild, Nicola Zamboni, Bart Deplancke, Vincent Gardeux, Claudia Irene Maushart, Matthias Johannes Betz, Christian Wolfrum, and Ender Konukoglu
Subjects: Science
Abstract: Abstract The standard method for identifying active Brown Adipose Tissue (BAT) is [18F]-Fluorodeoxyglucose ([18F]-FDG) PET/CT imaging, which is costly and exposes patients to radiation, making it impractical for population studies. These issues can be addressed with computational methods that predict [18F]-FDG uptake by BAT from CT; earlier population studies pave the way for developing such methods by showing some correlation between the Hounsfield Unit (HU) of BAT in CT and the corresponding [18F]-FDG uptake in PET. In this study, we propose training convolutional neural networks (CNNs) to predict [18F]-FDG uptake by BAT from unenhanced CT scans in the restricted regions that are likely to contain BAT. Using the Attention U-Net architecture, we perform experiments on datasets from four different cohorts, the largest study to date. We segment BAT regions using predicted [18F]-FDG uptake values, achieving 23% to 40% better accuracy than conventional CT thresholding. Additionally, BAT volumes computed from the segmentations distinguish the subjects with and without active BAT with an AUC of 0.8, compared to 0.6 for CT thresholding. These findings suggest CNNs can facilitate large-scale imaging studies more efficiently and cost-effectively using only CT.
Published: 2024
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29. Legged Robots for Object Manipulation: A Review

Author: Gong, Yifeng, Sun, Ge, Nair, Aditya, Bidwai, Aditya, CS, Raghuram, Grezmak, John, Sartoretti, Guillaume, and Daltorio, Kathryn A.
Subjects: Computer Science - Robotics
Abstract: Legged robots can have a unique role in manipulating objects in dynamic, human-centric, or otherwise inaccessible environments. Although most legged robotics research to date typically focuses on traversing these challenging environments, many legged platform demonstrations have also included "moving an object" as a way of doing tangible work. Legged robots can be designed to manipulate a particular type of object (e.g., a cardboard box, a soccer ball, or a larger piece of furniture), by themselves or collaboratively. The objective of this review is to collect and learn from these examples, to both organize the work done so far in the community and highlight interesting open avenues for future work. This review categorizes existing works into four main manipulation methods: object interactions without grasping, manipulation with walking legs, dedicated non-locomotive arms, and legged teams. Each method has different design and autonomy features, which are illustrated by available examples in the literature. Based on a few simplifying assumptions, we further provide quantitative comparisons for the range of possible relative sizes of the manipulated object with respect to the robot. Taken together, these examples suggest new directions for research in legged robot manipulation, such as multifunctional limbs, terrain modeling, or learning-based control, to support a number of new deployments in challenging indoor/outdoor scenarios in warehouses/construction sites, preserved natural areas, and especially for home robotics., Comment: Preprint of the paper submitted to Frontiers in Mechanical Engineering
Published: 2023

30. Spatio-Temporal Attention Network for Persistent Monitoring of Multiple Mobile Targets

Author: Wang, Yizhuo, Wang, Yutong, Cao, Yuhong, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: This work focuses on the persistent monitoring problem, where a set of targets moving based on an unknown model must be monitored by an autonomous mobile robot with a limited sensing range. To keep each target's position estimate as accurate as possible, the robot needs to adaptively plan its path to (re-)visit all the targets and update its belief from measurements collected along the way. In doing so, the main challenge is to strike a balance between exploitation, i.e., re-visiting previously-located targets, and exploration, i.e., finding new targets or re-acquiring lost ones. Encouraged by recent advances in deep reinforcement learning, we introduce an attention-based neural solution to the persistent monitoring problem, where the agent can learn the inter-dependencies between targets, i.e., their spatial and temporal correlations, conditioned on past measurements. This endows the agent with the ability to determine which target, time, and location to attend to across multiple scales, which we show also helps relax the usual limitations of a finite target set. We experimentally demonstrate that our method outperforms other baselines in terms of number of targets visits and average estimation error in complex environments. Finally, we implement and validate our model in a drone-based simulation experiment to monitor mobile ground targets in a high-fidelity simulator., Comment: Submitted to the IEEE/RSJ International Conference on Intelligent Robots (IROS 2023)
Published: 2023

31. Intent-based Deep Reinforcement Learning for Multi-agent Informative Path Planning

Author: Yang, Tianze, Cao, Yuhong, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: In multi-agent informative path planning (MAIPP), agents must collectively construct a global belief map of an underlying distribution of interest (e.g., gas concentration, light intensity, or pollution levels) over a given domain, based on measurements taken along their trajectory. They must frequently replan their path to balance the exploration of new areas with the exploitation of known high-interest areas, to maximize information gain within a predefined budget. Traditional approaches rely on reactive path planning conditioned on other agents' predicted future actions. However, as the belief is continuously updated, the predicted actions may not match the executed actions, introducing noise and reducing performance. We propose a decentralized, deep reinforcement learning (DRL) approach using an attention-based neural network, where agents optimize long-term individual and cooperative objectives by sharing their intent, represented as a distribution of medium-/long-term future positions obtained from their own policy. Intent sharing enables agents to learn to claim or avoid broader areas, while the use of attention mechanisms allows them to identify useful portions of imperfect predictions, maximizing cooperation even based on imperfect information. Our experiments compare the performance of our approach, its variants, and high-quality baselines across various MAIPP scenarios. We finally demonstrate the effectiveness of our approach under limited communication ranges, towards deployments under realistic communication constraints., Comment: \c{opyright} 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Published: 2023

32. SCRIMP: Scalable Communication for Reinforcement- and Imitation-Learning-Based Multi-Agent Pathfinding

Author: Wang, Yutong, Xiang, Bairan, Huang, Shinan, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: Trading off performance guarantees in favor of scalability, the Multi-Agent Path Finding (MAPF) community has recently started to embrace Multi-Agent Reinforcement Learning (MARL), where agents learn to collaboratively generate individual, collision-free (but often suboptimal) paths. Scalability is usually achieved by assuming a local field of view (FOV) around the agents, helping scale to arbitrary world sizes. However, this assumption significantly limits the amount of information available to the agents, making it difficult for them to enact the type of joint maneuvers needed in denser MAPF tasks. In this paper, we propose SCRIMP, where agents learn individual policies from even very small (down to 3x3) FOVs, by relying on a highly-scalable global/local communication mechanism based on a modified transformer. We further equip agents with a state-value-based tie-breaking strategy to further improve performance in symmetric situations, and introduce intrinsic rewards to encourage exploration while mitigating the long-term credit assignment problem. Empirical evaluations on a set of experiments indicate that SCRIMP can achieve higher performance with improved scalability compared to other state-of-the-art learning-based MAPF planners with larger FOVs, and even yields similar performance as a classical centralized planner in many cases. Ablation studies further validate the effectiveness of our proposed techniques. Finally, we show that our trained model can be directly implemented on real robots for online MAPF through high-fidelity simulations in gazebo., Comment: \c{opyright} 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Published: 2023

33. ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration

Author: Cao, Yuhong, Hou, Tianxiang, Wang, Yizhuo, Yi, Xian, and Sartoretti, Guillaume
Subjects: Computer Science - Robotics
Abstract: In autonomous robot exploration tasks, a mobile robot needs to actively explore and map an unknown environment as fast as possible. Since the environment is being revealed during exploration, the robot needs to frequently re-plan its path online, as new information is acquired by onboard sensors and used to update its partial map. While state-of-the-art exploration planners are frontier- and sampling-based, encouraged by the recent development in deep reinforcement learning (DRL), we propose ARiADNE, an attention-based neural approach to obtain real-time, non-myopic path planning for autonomous exploration. ARiADNE is able to learn dependencies at multiple spatial scales between areas of the agent's partial map, and implicitly predict potential gains associated with exploring those areas. This allows the agent to sequence movement actions that balance the natural trade-off between exploitation/refinement of the map in known areas and exploration of new areas. We experimentally demonstrate that our method outperforms both learning and non-learning state-of-the-art baselines in terms of average trajectory length to complete exploration in hundreds of simplified 2D indoor scenarios. We further validate our approach in high-fidelity Robot Operating System (ROS) simulations, where we consider a real sensor model and a realistic low-level motion controller, toward deployment on real robots., Comment: \c{opyright} 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Published: 2023

34. Perugia's Digital Twin - An Innovative Model of Urban Governance

Author: Velia Sartoretti and Mariana Minascurta
Subjects: smart city, GIS, digital twin, digitization, Cartography, GA101-1776, Cadastral mapping, GA109.5
Abstract: The Digital Twin of Perugia, an innovative model of urban governance. The Municipality of Perugia adopts an innovative approach to respond to current urban challenges. The digital twin of the city is a cutting-edge solution for the transition of the city towards a digital, sustainable and participatory environment.
Published: 2024

35. Effect of temporal resolution on calcium scoring: insights from photon-counting detector CT

Author: Sartoretti, Thomas, Mergen, Victor, Dzaferi, Amina, Allmendinger, Thomas, Manka, Robert, Alkadhi, Hatem, and Eberhard, Matthias
Published: 2024
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36. Evaluating the biodistribution for [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007 PET/CT with an inter- and intrapatient based analysis

Author: Cristina E. Popescu, Boya Zhang, Thomas Sartoretti, Noel Spielhofer, Stephan Skawran, Jakob Heimer, Michael Messerli, Alexander Sauter, Martin W. Huellner, Philipp A. Kaufmann, Irene A. Burger, and Alexander Maurer
Subjects: Radioligand therapy, PSMA-PET/CT, Therapy selection, Liver uptake, F18-PSMA-1007, Ga68-PSMA-11, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract: Abstract Background Liver uptake in [68Ga]Ga-PSMA-11 PET is used as an internal reference in addition to clinical parameters to select patients for [177Lu]Lu-PSMA-617 radioligand therapy (RLT). Due to increased demand, [68Ga]Ga-PSMA-11 was replaced by [18F]F-PSMA-1007, a more lipophilic tracer with different biodistribution and splenic uptake was suggested as a new internal reference. We compared the intra-patient tracer distribution between [68Ga]Ga-PSMA-11 and [18F]F-PSMA-1007. Methods Fifty patients who underwent PET examinations in two centers with both [18F]F-PSMA-1007 and [68Ga]Ga-PSMA-11 within one year were included. Mean standardized uptake values (SUVmean) were obtained for liver, spleen, salivary glands, blood pool, and bone. Primary tumor, local recurrence, lymph node, bone or visceral metastasis were also assessed for intra- and inter-individual comparison. Results Liver SUVmean was significantly higher with [18F]F-PSMA-1007 (11.7 ± 3.9) compared to [68Ga]Ga-PSMA-11 (5.4 ± 1.7, p
Published: 2024
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37. Gaia Data Release 3: Summary of the content and survey properties

Author: Gaia Collaboration, Vallenari, A., Brown, A. G. A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Drimmel, R., Jansen, F., Katz, D., Lattanzi, M. G., van Leeuwen, F., Bakker, J., Cacciari, C., Castañeda, J., De Angeli, F., Fabricius, C., Fouesneau, M., Frémat, Y., Galluccio, L., Guerrier, A., Heiter, U., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Sordoørcit, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Andrae, R., Audard, M., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Clementini, G., Damerdji, Y., Davidson, M., de Teodoro, P., Campos, M. Nuñez, Delchambre, L., Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hernández, J., Hestroffer, D., Hodgkin, S. T., Holl, B., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Reylé, C., Riello, M., Rimoldini, L., Roegiers, T., Rybizki, J., Sarro, L. M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Blazere, A., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cantat-Gaudin, T., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Casamiquela, L., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, M., David, P., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Di Matteo, P., Diakite, S., Diener, C., Distefano, E., Dolding, C., Edvardsson, B., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Fienga, A., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gavras, P., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hilger, T., Hładczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Jorissen, A., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Khanna, S., Kontizas, M., Kordopatis, G., Korn, A. J., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Rambaux, N., Ramos, P., Ramos-Lerate, M., Fiorentin, P. Re, Regibo, S., Richards, P. J., Diaz, C. Rios, Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylo, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, L., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: We present the third data release of the European Space Agency's Gaia mission, GDR3. The GDR3 catalogue is the outcome of the processing of raw data collected with the Gaia instruments during the first 34 months of the mission by the Gaia Data Processing and Analysis Consortium. The GDR3 catalogue contains the same source list, celestial positions, proper motions, parallaxes, and broad band photometry in the G, G$_{BP}$, and G$_{RP}$ pass-bands already present in the Early Third Data Release. GDR3 introduces an impressive wealth of new data products. More than 33 million objects in the ranges $G_{rvs} < 14$ and $3100
Published: 2022
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38. Solid confirmation of the broad DIB around 864.8 nm using stacked Gaia-RVS spectra

Author: Zhao, H., Schultheis, M., Zwitter, T., Bailer-Jones, C. A. L., Panuzzo, P., Sartoretti, P., Seabroke, G. M., Recio-Blanco, A., de Laverny, P., Kordopatis, G., Creevey, O. L., Dharmawardena, T. E., Frémat, Y., Sordo, R., Drimmel, R., Marshall, D. J., Palicio, P. A., Contursi, G., Álvarez, M. A., Baker, S., Benson, K., Cropper, M., Dolding, C., Huckle, H. E., Smith, M., Marchal, O., Ordenovic, C., Pailler, F., and Slezak, I.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Studies of the correlation between different diffuse interstellar bands (DIBs) are important for exploring their origins. However, the Gaia-RVS spectral window between 846 and 870 nm contains few DIBs, the strong DIB at 862 nm being the only convincingly confirmed one. Here we attempt to confirm the existence of a broad DIB around 864.8 nm and estimate its characteristics using the stacked Gaia-RVS spectra of a large number of stars. We study the correlations between the two DIBs at 862 nm and 864.8 nm, as well as the interstellar extinction. We obtained spectra of the interstellar medium absorption by subtracting the stellar components using templates constructed from real spectra at high Galactic latitudes with low extinctions. We then stacked the ISM spectra in Galactic coordinates, pixelized by the HEALPix scheme, to measure the DIBs. The stacked spectrum is modeled by the profiles of the two DIBs, Gaussian for $\lambda$862 and Lorentzian for $\lambda$864.8, and a linear continuum. We obtain 8458 stacked spectra in total, of which 1103 (13%) have reliable fitting results after applying numerous conservative filters. This work is the first of its kind to fit and measure $\lambda$862 and $\lambda$864.8 simultaneously in cool-star spectra. We find that the EWs and CDs of the two DIBs are well correlated with each other. The full width at half maximum (FWHM) of $\lambda$864.8 is estimated as $1.62 \pm 0.33$ nm which compares to $0.55 \pm 0.06$ nm for $\lambda$862. We also measure the vacuum rest-frame wavelength of $\lambda$864.8 to be $\lambda_0 = 864.53 \pm 0.14$ nm, smaller than previous estimates. We find a solid confirmation of the existence of the DIB around 864.8 nm based on an exploration of its correlation with $\lambda$862 and estimation of its FWHM. $\lambda$862 correlates better with E(BP-RP) than $\lambda$864.8., Comment: 7 pages, 4 figures, accepted by A&A Letters
Published: 2022
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39. Gaia Data Release 3: Reflectance spectra of Solar System small bodies

Author: Gaia Collaboration, Galluccio, L., Delbo, M., De Angeli, F., Pauwels, T., Tanga, P., Mignard, F., Cellino, A., Brown, A. G. A., Muinonen, K., Penttila, A., Jordan, S., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Drimmel, R., Jansen, F., Katz, D., Lattanzi, M. G., van Leeuwen, F., Bakker, J., Cacciari, C., Castaneda, J., Fabricius, C., Fouesneau, M., Frémat, Y., Guerrier, A., Heiter, U., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Sordo, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Andrae, R., Audard, M., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cheek, N., Clementini, G., Damerdji, Y., Davidson, M., de Teodoro, P., Campos, M. Nunez, Delchambre, L., Oro, A. Dell, Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hernández, J., Hestroffer, D., Hodgkin, S. T., Holl, B., Janssen, K., de Fombelle, G. Jevardat, Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Osborne, P., Pancino, E., Recio-Blanco, A., Reylé, C., Riello, M., Rimoldini, L., Roegiers, T., Rybizki, J., Sarro, L. M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núnez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cantat-Gaudin, T., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Casamiquela, L., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, P., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Diakite, S., Diener, C., Distefano, E., Dolding, C., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gavras, P., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núnez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hadczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Khanna, S., Kordopatis, G., Korn, A. J., Kospál, A, Kostrzewa-Rutkowska, Z., Kruszynska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrom, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocana, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Penalosa, Petit, J. -M., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Prsa, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Fiorentin, P. Re, Regibo, S., Richards, P. J., Diaz, C. Rios, Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núnez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santovena, R., Sarasso, M., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylor, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, L., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.
Subjects: Astrophysics - Earth and Planetary Astrophysics
Abstract: The Gaia mission of the European Space Agency (ESA) has been routinely observing Solar System objects (SSOs) since the beginning of its operations in August 2014. The Gaia data release three (DR3) includes, for the first time, the mean reflectance spectra of a selected sample of 60 518 SSOs, primarily asteroids, observed between August 5, 2014, and May 28, 2017. Each reflectance spectrum was derived from measurements obtained by means of the Blue and Red photometers (BP/RP), which were binned in 16 discrete wavelength bands. We describe the processing of the Gaia spectral data of SSOs, explaining both the criteria used to select the subset of asteroid spectra published in Gaia DR3, and the different steps of our internal validation procedures. In order to further assess the quality of Gaia SSO reflectance spectra, we carried out external validation against SSO reflectance spectra obtained from ground-based and space-borne telescopes and available in the literature. For each selected SSO, an epoch reflectance was computed by dividing the calibrated spectrum observed by the BP/RP at each transit on the focal plane by the mean spectrum of a solar analogue. The latter was obtained by averaging the Gaia spectral measurements of a selected sample of stars known to have very similar spectra to that of the Sun. Finally, a mean of the epoch reflectance spectra was calculated in 16 spectral bands for each SSO. The agreement between Gaia mean reflectance spectra and those available in the literature is good for bright SSOs, regardless of their taxonomic spectral class. We identify an increase in the spectral slope of S-type SSOs with increasing phase angle. Moreover, we show that the spectral slope increases and the depth of the 1 um absorption band decreases for increasing ages of S-type asteroid families., Comment: 30 pages, 26 figures
Published: 2022
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40. Gaia Data Release 3: Properties of the line broadening parameter derived with the Radial Velocity Spectrometer (RVS)

Author: Frémat, Y., Royer, F., Marchal, O., Blomme, R., Sartoretti, P., Guerrier, A., Panuzzo, P., Katz, D., Seabroke, G. M., Thévenin, F., Cropper, M., Benson, K., Damerdji, Y., Haigron, R., Lobel, A., Smith, M., Baker, S. G., Chemin, L., David, M., Dolding, C., Gosset, E., Janßen, K., Jasniewicz, G., Plum, G., Samaras, N., Snaith, O., Soubiran, C., Vanel, O., Zorec, J., Zwitter, T., Brouillet, N., Caffau, E., Crifo, F., Fabre, C., Fragkoudi, F., Huckle, H. E., Lasne, Y., Leclerc, N., Mastrobuono-Battisti, A., Piccolo, A. Jean-Antoine, and Viala, Y.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: The third release of the Gaia catalogue contains the radial velocities for 33,812,183 stars having effective temperatures ranging from 3100 K to 14,500 K. The measurements are based on the comparison of the observed RVS spectrum (wavelength coverage: 846--870 nm, median resolving power: 11,500) to synthetic data broadened to the adequate Along-Scan Line Spread Function. The additional line-broadening, fitted as it would only be due to axial rotation, is also produced by the pipeline and is available in the catalogue (field name gaia_source:vbroad). To describe the properties of the line-broadening information extracted from the RVS and published in the catalogue, as well as to analyse the limitations imposed by the adopted method, wavelength range, and instrument. We use simulations to express the link existing between the line broadening measurement provided in Gaia Data Release 3 and Vsin(i). We then compare the observed values to the measurements published by various catalogues and surveys (GALAH, APOGEE, LAMOST, ...). While we recommend being cautious in the interpretation of the vbroad measurement, we also find a reasonable global agreement between the Gaia Data Release 3 line broadening values and those found in the other catalogues. We discuss and establish the validity domain of the published vbroad values. The estimate tends to be overestimated at the lower vsini end, and at $T_\mathrm{eff}>7500\,\mathrm{K}$ its quality and significance degrade rapidly when $G_\mathrm{RVS}>10$. Despite all the known and reported limitations, the Gaia Data Release 3 line broadening catalogue contains the measurements obtained for 3,524,677 stars with $T_\mathrm{eff}$\ ranging from 3500 to 14,500 K, and $G_\mathrm{RVS}<12$. It gathers the largest stellar sample ever considered for the purpose, and allows a first mapping of the \Gaia\ line broadening parameter across the HR diagram., Comment: 19 pages, 17 figures, see https://www.cosmos.esa.int/web/gaia/dr3-papers Paper accepted for publication in Astronomy and Astrophysics on 23th June 2022
Published: 2022
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41. Gaia Data Release 3 Properties and validation of the radial velocities

Author: Katz, D., Sartoretti, P., Guerrier, A., Panuzzo, P., Seabroke, G. M., Thévenin, F., Cropper, M., Benson, K., Blomme, R., Haigron, R., Marchal, O., Smith, M., Baker, S., Chemin, L., Damerdji, Y., David, M., Dolding, C., Frémat, Y., Gosset, E., Janßen, K., Jasniewicz, G., Lobel, A., Plum, G., Samaras, N., Snaith, O., Soubiran, C., Vanel, O., Zwitter, T., Antoja, T., Arenou, F., Babusiaux, C., Brouillet, N., Caffau, E., Di Matteo, P., Fabre, C., Fabricius, C., Frakgoudi, F., Haywood, M., Huckle, H. E., Hottier, C., Lasne, Y., Leclerc, N., Mastrobuono-Battisti, A., Royer, F., Teyssier, D., Zorec, J., Crifo, F., Piccolo, A. Jean-Antoine, Turon, C., and Viala, Y.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Gaia Data Release 3 (Gaia DR3) contains the second release of the combined radial velocities. It is based on the spectra collected during the first 34 months of the nominal mission. The longer time baseline and the improvements of the pipeline made it possible to push the processing limit, from Grvs = 12 in Gaia DR2, to Grvs = 14 mag. In this article, we describe the new functionalities implemented for Gaia DR3, the quality filters applied during processing and post-processing and the properties and performance of the published velocities. For Gaia DR3, several functionalities were upgraded or added. (Abridged) Gaia DR3 contains the combined radial velocities of 33 812 183 stars. With respect to Gaia DR2, the interval of temperature has been expanded from Teff \in [3600, 6750] K to Teff \in [3100, 14500] K for the bright stars ( Grvs \leq 12 mag) and [3100, 6750] K for the fainter stars. The radial velocities sample a significant part of the Milky Way: they reach a few kilo-parsecs beyond the Galactic centre in the disc and up to about 10-15 kpc vertically into the inner halo. The median formal precision of the velocities is of 1.3 km/s at Grvs = 12 and 6.4 km/s at Grvs = 14 mag. The velocity zero point exhibits a small systematic trend with magnitude starting around Grvs = 11 mag and reaching about 400 m/s at Grvs = 14 mag. A correction formula is provided, which can be applied to the published data. The Gaia DR3 velocity scale is in satisfactory agreement with APOGEE, GALAH, GES and RAVE, with systematic differences that mostly do not exceed a few hundreds m/s. The properties of the radial velocities are also illustrated with specific objects: open clusters, globular clusters as well as the Large Magellanic Cloud (LMC). For example, the precision of the data allows to map the line-of-sight rotational velocities of the globular cluster 47 Tuc and of the LMC., Comment: Sumitted to A&A
Published: 2022
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42. Gaia DR3: Specific processing and validation of all-sky RR Lyrae and Cepheid stars -- The RR Lyrae sample

Author: Clementini, G., Ripepi, V., Garofalo, A., Molinaro, R., Muraveva, T., Leccia, S., Rimoldini, L., Holl, B., de Fombelle, G. Jevardat, Sartoretti, P., Marchal, O., Audard, M., Nienartowicz, K., Andrae, R., Marconi, M., Szabados, L., Evans, D. W., Lecoeur-Taibi, I., Mowlavi, N., Musella, I., and Eyer, L.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Gaia DR3 publishes a catalogue of full-sky RR Lyrae stars (RRLs) observed during the initial 34 months of science operations, that were processed through the Specific Object Study (SOS) pipeline for Cepheids and RRLs (SOS Cep&RRL) observed by Gaia. The SOS Cep&RRL validation of DR3 candidate RRLs relies on tools that include the Period (P) G-amplitude diagram and the P-phi21 and -phi31 parameters of the G light curve Fourier decomposition, based on a sample of bona fide known RRLs (Gold Sample). The SOS processing led to a catalogue of 271779 RRLs listed in the vari_rrlyrae table of DR3. By dropping sources that clearly are contaminants, or have an uncertain classification we produce the final catalogue of SOS-confirmed DR3 RRLs containing 270905 sources (174947 fundamental mode, 93952 first overtone and 2006 double-mode RRLs) confirmed and fully characterised by the SOS Cep&RRL pipeline. They are distributed all over the sky, including 95 globular clusters and 25 Milky Way companions. RVS time series radial velocities are also published for 1096 RRLs and 799 Cepheids. Of the 270905 DR3 RRLs, 200294 are already known in the literature and 70611 are, to the best of our knowledge, new discoveries by Gaia. An estimate of the interstellar absorption is published for 142660 fundamental-mode RRLs from a relation based on the G-band amplitude and the pulsation period. Metallicities derived from the Periods and the phi31 Fourier parameters of the G-light curves are also released for 133559 RRLs. The final Gaia DR3 catalogue of confirmed RRLs almost doubles the DR2 RRLs catalogue. An increase of statistical significance, a better characterization of the RRLs pulsational and astrophysical parameters, and the improved astrometry published with Gaia EDR3, make the SOS Cep&RRL DR3 sample, the largest, most homogeneous and parameter-rich catalogue of All-Sky RRLs published so far., Comment: 42 pages, 36 figures, submitted to A&A, revised version after the referee report
Published: 2022
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43. Gaia DR3: Specific processing and validation of all-sky RR Lyrae and Cepheid stars -- The Cepheid sample

Author: Ripepi, V., Clementini, G., Molinaro, R., Leccia, S., Plachy, E., Molnár, L., Rimoldini, L., Musella, I., Marconi, M., Garofalo, A., Audard, M., Holl, B., Evans, D. W., de Fombelle, G. Jevardat, Lecoeur-Taibi, I., Marchal, O., Mowlavi, N., Muraveva, T., Nienartowicz, K., Sartoretti, P., Szabados, L., and Eyer, L.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract: Context. Cepheids are pulsating stars that play a crucial role in several astrophysical contexts. Among the different types, the Classical Cepheids are fundamental tools for the calibration of the extragalactic distance ladder. They are also powerful stellar population tracers in the context of Galactic studies. The Gaia Third Data Release (DR3) publishes improved data on Cepheids collected during the initial 34 months of operations. Aims. We present the Gaia DR3 catalogue of Cepheids of all types, obtained through the analysis carried out with the Specific Object Study (SOS) Cep&RRL pipeline. Methods. We discuss the procedures adopted to clean the Cepheid sample from spurious objects, to validate the results, and to re-classify sources with a wrong outcome from the SOS Cep&RRL pipeline. Results. The Gaia DR3 includes multi-band time-series photometry and characterisation by the SOS Cep&RRL pipeline for a sample of 15,006 Cepheids of all types. The sample includes 4,663, 4,616, 321 and 185 pulsators, distributed in the LMC, SMC, M31 and M33, respectively, as well as 5 221 objects in the remaining All Sky sub-region which includes stars in the MW field/clusters and in a number of small satellites of our Galaxy. Among this sample, 327 objects were known as variable stars in the literature but with a different classification, while, to the best of our knowledge, 474 stars have not been reported before to be variable stars and therefore they likely are new Cepheids discovered by Gaia., Comment: This manuscript is one of the official papers planned to accompany Gaia DR3 in a special issue of Astronomy & Astrophysics. It includes 34 pages and 39 figures. Submitted to Astronomy & Astrophysics on May 10 2022
Published: 2022
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44. Gaia Data Release 3: Mapping the asymmetric disc of the Milky Way

Author: Gaia Collaboration, Drimmel, R., Romero-Gomez, M., Chemin, L., Ramos, P., Poggio, E., Ripepi, V., Andrae, R., Blomme, R., Cantat-Gaudin, T., Castro-Ginard, A., Clementini, G., Figueras, F., Fouesneau, M., Fremat, Y., Jardine, K., Khanna, S., Lobel, A., Marshall, D. J., Muraveva, T., Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Jansen, F., Katz, D., Lattanzi, M. G., van Leeuwen, F., Bakker, J., Cacciari, C., Castañeda, J., De Angeli, F., Fabricius, C., Galluccio, L., Guerrier, A., Heiter, U., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Sordoørcit, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Audard, M., Bellas-Velidis, I., Benson, K., Berthier, J., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Damerdji, Y., Davidson, M., de Teodoro, P., Campos, M. Nuñez, Delchambre, L., Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hernández, J., Hestroffer, D., Hodgkin, S. T., Holl, B., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Reylé, C., Riello, M., Rimoldini, L., Roegiers, T., Rybizki, J., Sarro, L. M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Blazere, A., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Casamiquela, L., Castellani, M., Chaoul, L., Charlot, P., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, M., David, P., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Di Matteo, P., Diakite, S., Diener, C., Distefano, E., Dolding, C., Edvardsson, B., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Fienga, A., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gavras, P., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hilger, T., Hładczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Jorissen, A., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Kontizas, M., Kordopatis, G., Korn, A. J., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Rambaux, N., Ramos-Lerate, M., Fiorentin, P. Re, Regibo, S., Richards, P. J., Diaz, C. Rios, Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylo, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, L., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: With the most recent Gaia data release the number of sources with complete 6D phase space information (position and velocity) has increased to well over 33 million stars, while stellar astrophysical parameters are provided for more than 470 million sources, in addition to the identification of over 11 million variable stars. Using the astrophysical parameters and variability classifications provided in Gaia DR3, we select various stellar populations to explore and identify non-axisymmetric features in the disc of the Milky Way in both configuration and velocity space. Using more about 580 thousand sources identified as hot OB stars, together with 988 known open clusters younger than 100 million years, we map the spiral structure associated with star formation 4-5 kpc from the Sun. We select over 2800 Classical Cepheids younger than 200 million years, which show spiral features extending as far as 10 kpc from the Sun in the outer disc. We also identify more than 8.7 million sources on the red giant branch (RGB), of which 5.7 million have line-of-sight velocities, allowing the velocity field of the Milky Way to be mapped as far as 8 kpc from the Sun, including the inner disc. The spiral structure revealed by the young populations is consistent with recent results using Gaia EDR3 astrometry and source lists based on near infrared photometry, showing the Local (Orion) arm to be at least 8 kpc long, and an outer arm consistent with what is seen in HI surveys, which seems to be a continuation of the Perseus arm into the third quadrant. Meanwhile, the subset of RGB stars with velocities clearly reveals the large scale kinematic signature of the bar in the inner disc, as well as evidence of streaming motions in the outer disc that might be associated with spiral arms or bar resonances. (abridged), Comment: 35 pages, 27 figures, accepted for publication in A&A special Gaia DR3 issue. V2: abstract completed. V3: complete author list and link to data: https://drive.google.com/drive/u/1/folders/1yOJPjYmM7QK5XVsqaiSOTuwDQNti2LlZ
Published: 2022
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45. Gaia Data Release 3: Pulsations in main sequence OBAF-type stars

Author: Gaia Collaboration, De Ridder, J., Ripepi, V., Aerts, C., Palaversa, L., Eyer, L., Holl, B., Audard, M., Rimoldini, L., Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Drimmel, R., Jansen, F., Katz, D., Lattanzi, M. G., van Leeuwen, F., Bakker, J., Cacciari, C., Castañeda, J., De Angeli, F., Fabricius, C., Fouesneau, M., Frémat, Y., Galluccio, L., Guerrier, A., Heiter, U., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Sordo, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Andrae, R., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Clementini, G., Damerdji, Y., Davidson, M., de Teodoro, P., Campos, M. Nuñez, Delchambre, L., Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hernández, J., Hestroffer, D., Hilger, T., Hodgkin, S. T., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Reylé, C., Riello, M., Roegiers, T., Rybizki, J., Sarro, L. M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cantat-Gaudin, T., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Casamiquela, L., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, P., de Laverny, P., De Luise, F., De March, R., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Diakite, S., Diener, C., Distefano, E., Dolding, C., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gavras, P., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hładczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Khanna, S., Kordopatis, G., Korn, A. J., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Fiorentin, P. Re, Regibo, S., Richards, P. J., Diaz, C. Rios, Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylor, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, Ł., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.
Subjects: Astrophysics - Solar and Stellar Astrophysics
Abstract: The third Gaia data release provides photometric time series covering 34 months for about 10 million stars. For many of those stars, a characterisation in Fourier space and their variability classification are also provided. This paper focuses on intermediate- to high-mass (IHM) main sequence pulsators M >= 1.3 Msun) of spectral types O, B, A, or F, known as beta Cep, slowly pulsating B (SPB), delta Sct, and gamma Dor stars. These stars are often multi-periodic and display low amplitudes, making them challenging targets to analyse with sparse time series. All datasets used in this analysis are part of the Gaia DR3 data release. The photometric time series were used to perform a Fourier analysis, while the global astrophysical parameters necessary for the empirical instability strips were taken from the Gaia DR3 gspphot tables, and the vsini data were taken from the Gaia DR3 esphs tables. We show that for nearby OBAF-type pulsators, the Gaia DR3 data are precise and accurate enough to pinpoint them in the Hertzsprung-Russell diagram. We find empirical instability strips covering broader regions than theoretically predicted. In particular, our study reveals the presence of fast rotating gravity-mode pulsators outside the strips, as well as the co-existence of rotationally modulated variables inside the strips as reported before in the literature. We derive an extensive period-luminosity relation for delta Sct stars and provide evidence that the relation features different regimes depending on the oscillation period. Finally, we demonstrate how stellar rotation attenuates the amplitude of the dominant oscillation mode of delta Sct stars.
Published: 2022
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46. Gaia Data Release 3: A Golden Sample of Astrophysical Parameters

Author: Gaia Collaboration, Creevey, O. L., Sarro, L. M., Lobel, A., Pancino, E., Andrae, R., Smart, R. L., Clementini, G., Heiter, U., Korn, A. J., Fouesneau, M., Frémat, Y., De Angeli, F., Vallenari, A., Harrison, D. L., Thévenin, F., Reylé, C., Sordo, R., Garofalo, A., Brown, A. G. A., Eyer, L., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Biermann, M., Ducourant, C., Evans, D. W., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Drimmel, R., Jansen, F., Katz, D., Lattanzi, M. G., van Leeuwen, F., Bakker, J., Cacciari, C., Castañeda, J., Fabricius, C., Galluccio, L., Guerrier, A., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Audard, M., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Damerdji, Y., Davidson, M., de Teodoro, P., Campos, M. Nuñez, Delchambre, L., Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Hernández, J., Hestroffer, D., Hodgkin, S. T., Holl, B., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pauwels, T., Recio-Blanco, A., Riello, M., Rimoldini, L., Roegiers, T., Rybizki, J., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cantat-Gaudin, T., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Casamiquela, L., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, P., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Di Matteo, P., Diakite, S., Diener, C., Distefano, E., Dolding, C., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Gavel, A., Gavras, P., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hładczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Khanna, S., Kordopatis, G., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Fiorentin, P. Re, Regibo, S., Richards, P. J., Diaz, C. Rios, Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylor, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, Ł., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Gaia Data Release 3 (DR3) provides a wealth of new data products for the astronomical community to exploit, including astrophysical parameters for a half billion stars. In this work we demonstrate the high quality of these data products and illustrate their use in different astrophysical contexts. We query the astrophysical parameter tables along with other tables in Gaia DR3 to derive the samples of the stars of interest. We validate our results by using the Gaia catalogue itself and by comparison with external data. We have produced six homogeneous samples of stars with high quality astrophysical parameters across the HR diagram for the community to exploit. We first focus on three samples that span a large parameter space: young massive disk stars (~3M), FGKM spectral type stars (~3M), and UCDs (~20K). We provide these sources along with additional information (either a flag or complementary parameters) as tables that are made available in the Gaia archive. We furthermore identify 15740 bone fide carbon stars, 5863 solar-analogues, and provide the first homogeneous set of stellar parameters of the Spectro Photometric Standard Stars. We use a subset of the OBA sample to illustrate its usefulness to analyse the Milky Way rotation curve. We then use the properties of the FGKM stars to analyse known exoplanet systems. We also analyse the ages of some unseen UCD-companions to the FGKM stars. We additionally predict the colours of the Sun in various passbands (Gaia, 2MASS, WISE) using the solar-analogue sample., Comment: 35 pages, (incl 6 pages references, acknowledgements, affiliations), 37 figures, A&A accepted
Published: 2022
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47. Gaia Data Release 3: G_RVS photometry from the RVS spectra

Author: Sartoretti, P., Marchal, O., Babusiaux, C., Jordi, C., Guerrier, A., Panuzzo, P., Katz, D., Seabroke, G. M., Thévenin, F., Cropper, M., Benson, K., Blomme, R., Haigron, R., Smith, M., Baker, S., Chemin, L., David, M., Dolding, C., Frémat, Y., Janssen, K., Jasniewicz, G., Lobel, A., Plum, G., Samaras, N., Snaith, O., Soubiran, C., Vanel, O., Zwitter, T., Brouillet, N., Caffau, E., Crifo, F., Fabre, C., Frakgoudi, F., Piccolo, A. Jean-Antoine, Huckle, H. E., Lasne, Y., Leclerc, N., Mastrobuono-Battisti, A., Royer, F., Viala, Y., and Zorec, J.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: Gaia Data Release 3 (DR3) contains the first release of magnitudes estimated from the integration of Radial Velocity Spectrometer (RVS) spectra for a sample of about 32.2 million stars brighter than G_RVS~14 mag (or G~15 mag). In this paper, we describe the data used and the approach adopted to derive and validate the G_RVS magnitudes published in DR3. We also provide estimates of the G_RVS passband and associated G_RVS zero-point. We derived G_RVS photometry from the integration of RVS spectra over the wavelength range from 846 to 870 nm. We processed these spectra following a procedure similar to that used for DR2, but incorporating several improvements that allow a better estimation of G_RVS. These improvements pertain to the stray-light background estimation, the line spread function calibration, and the detection of spectra contaminated by nearby relatively bright sources. We calibrated the G_RVS zero-point every 30 hours based on the reference magnitudes of constant stars from the Hipparcos catalogue, and used them to transform the integrated flux of the cleaned and calibrated spectra into epoch magnitudes. The G_RVS magnitude of a star published in DR3 is the median of the epoch magnitudes for that star. We estimated the G_RVS passband by comparing the RVS spectra of 108 bright stars with their flux-calibrated spectra from external spectrophotometric libraries. The G_RVS magnitude provides information that is complementary to that obtained from the G, G_BP, and G_RP magnitudes, which is useful for constraining stellar metallicity and interstellar extinction. The median precision of G_RVS measurements ranges from about 0.006 mag for the brighter stars (i.e. with 3.5 < G_RVS < 6.5 mag) to 0.125 mag at the faint end. The derived G_RVS passband shows that the effective transmittance of the RVS is approximately 1.23 times better than the pre-launch estimate., Comment: 16 pages, 18 figures. Accepted for publication in A&A
Published: 2022
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48. Gaia Data Release 3: The extragalactic content

Author: Gaia Collaboration, Bailer-Jones, C. A. L., Teyssier, D., Delchambre, L., Ducourant, C., Garabato, D., Hatzidimitriou, D., Klioner, S. A., Rimoldini, L., Bellas-Velidis, I., Carballo, R., Carnerero, M. I., Diener, C., Fouesneau, M., Galluccio, L., Gavras, P., Krone-Martins, A., Raiteri, C. M., Teixeira, R., Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Biermann, M., Creevey, O. L., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Lammers, U. L., Lindegren, L., Luri, X., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bastian, U., Drimmel, R., Jansen, F., Katz, D., Lattanzi, M. G., van Leeuwen, F., Bakker, J., Cacciari, C., Castañeda, J., De Angeli, F., Fabricius, C., Frémat, Y., Guerrier, A., Heiter, U., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Seabroke, G. M., Sordo, R., Thévenin, F., Gracia-Abril, G., Portell, J., Altmann, M., Andrae, R., Audard, M., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Clementini, G., Damerdji, Y., Davidson, M., de Teodoro, P., Campos, M. Nuñez, Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hernández, J., Hestroffer, D., Hodgkin, S. T., Holl, B., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Reylé, C., Riello, M., Roegiers, T., Rybizki, J., Sarro, L. M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cantat-Gaudin, T., Carlucci, T., Carrasco, J. M., Casamiquela, L., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, P., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Diakite, S., Distefano, E., Dolding, C., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hładczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Khanna, S., Kontizas, M., Kordopatis, G., Korn, A. J., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Ramos, P., Ramos-Lerate, M., Fiorentin, P. Re, Regibo, S., Richards, P. J., Diaz, C. Rios, Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M. S., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylor, M. B., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, Ł., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.
Subjects: Astrophysics - Astrophysics of Galaxies
Abstract: The Gaia Galactic survey mission is designed and optimized to obtain astrometry, photometry, and spectroscopy of nearly two billion stars in our Galaxy. Yet as an all-sky multi-epoch survey, Gaia also observes several million extragalactic objects down to a magnitude of G~21 mag. Due to the nature of the Gaia onboard selection algorithms, these are mostly point-source-like objects. Using data provided by the satellite, we have identified quasar and galaxy candidates via supervised machine learning methods, and estimate their redshifts using the low resolution BP/RP spectra. We further characterise the surface brightness profiles of host galaxies of quasars and of galaxies from pre-defined input lists. Here we give an overview of the processing of extragalactic objects, describe the data products in Gaia DR3, and analyse their properties. Two integrated tables contain the main results for a high completeness, but low purity (50-70%), set of 6.6 million candidate quasars and 4.8 million candidate galaxies. We provide queries that select purer sub-samples of these containing 1.9 million probable quasars and 2.9 million probable galaxies (both 95% purity). We also use high quality BP/RP spectra of 43 thousand high probability quasars over the redshift range 0.05-4.36 to construct a composite quasar spectrum spanning restframe wavelengths from 72-100 nm., Comment: Accepted to A&A
Published: 2022
Full Text: View/download PDF

49. Gaia Data Release 3: Stellar multiplicity, a teaser for the hidden treasure

Author: Gaia Collaboration, Arenou, F., Babusiaux, C., Barstow, M. A., Faigler, S., Jorissen, A., Kervella, P., Mazeh, T., Mowlavi, N., Panuzzo, P., Sahlmann, J., Shahaf, S., Sozzetti, A., Bauchet, N., Damerdji, Y., Gavras, P., Giacobbe, P., Gosset, E., Halbwachs, J. -L., Holl, B., Lattanzi, M. G., Leclerc, N., Morel, T., Pourbaix, D., Fiorentin, P. Re, Sadowski, G., Ségransan, D., Siopis, C., Teyssier, D., Zwitter, T., Planquart, L., Brown, A. G. A., Vallenari, A., Prusti, T., de Bruijne, J. H. J., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Mignard, F., Panem, C., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bailer-Jones, C. A. L., Bastian, U., Drimmel, R., Jansen, F., Katz, D., van Leeuwen, F., Bakker, J., Cacciari, C., Castañeda, J., De Angeli, F., Fabricius, C., Fouesneau, M., Frémat, Y., Galluccio, L., Guerrier, A., Heiter, U., Masana, E., Messineo, R., Nicolas, C., Nienartowicz, K., Pailler, F., Riclet, F., Roux, W., Seabroke, G. M., Sordo, R., Thévenin, F., Gracia-Abril, G., Portell, J., Altmann, M., Andrae, R., Audard, M., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Clementini, G., Davidson, M., de Teodoro, P., Campos, M. Nuñez, Delchambre, L., Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Haigron, R., Hambly, N. C., Harrison, D. L., Hernández, J., Hestroffer, D., Hodgkin, S. T., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pancino, E., Pauwels, T., Recio-Blanco, A., Reylé, C., Riello, M., Rimoldini, L., Roegiers, T., Rybizki, J., Sarro, L. M., Smith, M., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Varela, E. Anglada, Antoja, T., Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Bartolomé, S., Bassilana, J. -L., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Blazere, A., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bragaglia, A., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Cantat-Gaudin, T., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Casamiquela, L., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Cropper, M., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, P., de Laverny, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Diakite, S., Diener, C., Distefano, E., Dolding, C., Enke, H., Fabre, C., Fabrizio, M., Fedorets, G., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gerlach, E., Geyer, R., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hładczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Khanna, S., Kordopatis, G., Korn, A. J., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., McMillan, P. J., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Palicio, P. A., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Poggio, E., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Regibo, S., Richards, P. J., Diaz, C. Rios, Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Núñez, A. Sáez, Sellés, A. Sagristà, Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M. S., Sciacca, E., Segol, M., Segovia, J. C., Semeux, D., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spagna, A., Spina, L., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylor, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, Ł., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., and Zucker, S.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - Astrophysics of Galaxies, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: The Gaia DR3 Catalogue contains for the first time about eight hundred thousand solutions with either orbital elements or trend parameters for astrometric, spectroscopic and eclipsing binaries, and combinations of them. This paper aims to illustrate the huge potential of this large non-single star catalogue. Using the orbital solutions together with models of the binaries, a catalogue of tens of thousands of stellar masses, or lower limits, partly together with consistent flux ratios, has been built. Properties concerning the completeness of the binary catalogues are discussed, statistical features of the orbital elements are explained and a comparison with other catalogues is performed. Illustrative applications are proposed for binaries across the H-R diagram. The binarity is studied in the RGB/AGB and a search for genuine SB1 among long-period variables is performed. The discovery of new EL CVn systems illustrates the potential of combining variability and binarity catalogues. Potential compact object companions are presented, mainly white dwarf companions or double degenerates, but one candidate neutron star is also presented. Towards the bottom of the main sequence, the orbits of previously-suspected binary ultracool dwarfs are determined and new candidate binaries are discovered. The long awaited contribution of Gaia to the analysis of the substellar regime shows the brown dwarf desert around solar-type stars using true, rather than minimum, masses, and provides new important constraints on the occurrence rates of substellar companions to M dwarfs. Several dozen new exoplanets are proposed, including two with validated orbital solutions and one super-Jupiter orbiting a white dwarf, all being candidates requiring confirmation. Beside binarity, higher order multiple systems are also found., Comment: 60 pages, 60 figures. Accepted for publication in Astronomy & Astrophysics (2022-06-09). The catalogue of binary masses is available for download from the ESA Gaia DR3 Archive and will be available from the CDS/VizieR service
Published: 2022
Full Text: View/download PDF

50. Gaia Data Release 3: Chemical cartography of the Milky Way

Author: Gaia Collaboration, Recio-Blanco, A., Kordopatis, G., de Laverny, P., Palicio, P. A., Spagna, A., Spina, L., Katz, D., Fiorentin, P. Re, Poggio, E., McMillan, P. J., Vallenari, A., Lattanzi, M. G., Seabroke, G. M., Casamiquela, L., Bragaglia, A., Antoja, T., Bailer-Jones, C. A. L., Andrae, R., Fouesneau, M., Cropper, M., Cantat-Gaudin, T., Heiter, U., Bijaoui, A., Brown, A. G. A., Prusti, T., de Bruijne, J. H. J., Arenou, F., Babusiaux, C., Biermann, M., Creevey, O. L., Ducourant, C., Evans, D. W., Eyer, L., Guerra, R., Hutton, A., Jordi, C., Klioner, S. A., Lammers, U. L., Lindegren, L., Luri, X., Mignard, F., Panem, C., Pourbaix, D., Randich, S., Sartoretti, P., Soubiran, C., Tanga, P., Walton, N. A., Bastian, U., Drimmel, R., Jansen, F., van Leeuwen, F., Bakker, J., Cacciari, C., Castañeda, J., De Angeli, F., Fabricius, C., Frémat, Y., Galluccio, L., Guerrier, A., Masana, E., Messineo, R., Mowlavi, N., Nicolas, C., Nienartowicz, K., Pailler, F., Panuzzo, P., Riclet, F., Roux, W., Sordo, R., Thévenin, F., Gracia-Abril, G., Portell, J., Teyssier, D., Altmann, M., Audard, M., Bellas-Velidis, I., Benson, K., Berthier, J., Blomme, R., Burgess, P. W., Busonero, D., Busso, G., Cánovas, H., Carry, B., Cellino, A., Cheek, N., Clementini, G., Damerdji, Y., Davidson, M., de Teodoro, P., Campos, M. Nuñez, Delchambre, L., Dell'Oro, A., Esquej, P., Fernández-Hernández, J., Fraile, E., Garabato, D., García-Lario, P., Gosset, E., Haigron, R., Halbwachs, J. -L., Hambly, N. C., Harrison, D. L., Hernández, J., Hestroffer, D., Hodgkin, S. T., Holl, B., Janßen, K., de Fombelle, G. Jevardat, Jordan, S., Krone-Martins, A., Lanzafame, A. C., Löffler, W., Marchal, O., Marrese, P. M., Moitinho, A., Muinonen, K., Osborne, P., Pancino, E., Pauwels, T., Reylé, C., Riello, M., Rimoldini, L., Roegiers, T., Rybizki, J., Sarro, L. M., Siopis, C., Smith, M., Sozzetti, A., Utrilla, E., van Leeuwen, M., Abbas, U., Ábrahám, P., Aramburu, A. Abreu, Aerts, C., Aguado, J. J., Ajaj, M., Aldea-Montero, F., Altavilla, G., Álvarez, M. A., Alves, J., Anders, F., Anderson, R. I., Varela, E. Anglada, Baines, D., Baker, S. G., Balaguer-Núñez, L., Balbinot, E., Balog, Z., Barache, C., Barbato, D., Barros, M., Barstow, M. A., Bartolomé, S., Bassilana, J. -L., Bauchet, N., Becciani, U., Bellazzini, M., Berihuete, A., Bernet, M., Bertone, S., Bianchi, L., Binnenfeld, A., Blanco-Cuaresma, S., Boch, T., Bombrun, A., Bossini, D., Bouquillon, S., Bramante, L., Breedt, E., Bressan, A., Brouillet, N., Brugaletta, E., Bucciarelli, B., Burlacu, A., Butkevich, A. G., Buzzi, R., Caffau, E., Cancelliere, R., Carballo, R., Carlucci, T., Carnerero, M. I., Carrasco, J. M., Castellani, M., Castro-Ginard, A., Chaoul, L., Charlot, P., Chemin, L., Chiaramida, V., Chiavassa, A., Chornay, N., Comoretto, G., Contursi, G., Cooper, W. J., Cornez, T., Cowell, S., Crifo, F., Crosta, M., Crowley, C., Dafonte, C., Dapergolas, A., David, P., De Luise, F., De March, R., De Ridder, J., de Souza, R., de Torres, A., del Peloso, E. F., del Pozo, E., Delbo, M., Delgado, A., Delisle, J. -B., Demouchy, C., Dharmawardena, T. E., Di Matteo, P., Diakite, S., Diener, C., Distefano, E., Dolding, C., Edvardsson, B., Enke, H., Fabre, C., Fabrizio, M., Faigler, S., Fedorets, G., Fernique, P., Figueras, F., Fournier, Y., Fouron, C., Fragkoudi, F., Gai, M., Garcia-Gutierrez, A., Garcia-Reinaldos, M., García-Torres, M., Garofalo, A., Gavel, A., Gavras, P., Gerlach, E., Geyer, R., Giacobbe, P., Gilmore, G., Girona, S., Giuffrida, G., Gomel, R., Gomez, A., González-Núñez, J., González-Santamaría, I., González-Vidal, J. J., Granvik, M., Guillout, P., Guiraud, J., Gutiérrez-Sánchez, R., Guy, L. P., Hatzidimitriou, D., Hauser, M., Haywood, M., Helmer, A., Helmi, A., Sarmiento, M. H., Hidalgo, S. L., Hładczuk, N., Hobbs, D., Holland, G., Huckle, H. E., Jardine, K., Jasniewicz, G., Piccolo, A. Jean-Antoine, Jiménez-Arranz, Ó., Campillo, J. Juaristi, Julbe, F., Karbevska, L., Kervella, P., Khanna, S., Korn, A. J., Kóspál, Á, Kostrzewa-Rutkowska, Z., Kruszyńska, K., Kun, M., Laizeau, P., Lambert, S., Lanza, A. F., Lasne, Y., Campion, J. -F. Le, Lebreton, Y., Lebzelter, T., Leccia, S., Leclerc, N., Lecoeur-Taibi, I., Liao, S., Licata, E. L., Lindstrøm, H. E. P., Lister, T. A., Livanou, E., Lobel, A., Lorca, A., Loup, C., Pardo, P. Madrero, Romeo, A. Magdaleno, Managau, S., Mann, R. G., Manteiga, M., Marchant, J. M., Marconi, M., Marcos, J., Santos, M. M. S. Marcos, Pina, D. Marín, Marinoni, S., Marocco, F., Marshall, D. J., Polo, L. Martin, Martín-Fleitas, J. M., Marton, G., Mary, N., Masip, A., Massari, D., Mastrobuono-Battisti, A., Mazeh, T., Messina, S., Michalik, D., Millar, N. R., Mints, A., Molina, D., Molinaro, R., Molnár, L., Monari, G., Monguió, M., Montegriffo, P., Montero, A., Mor, R., Mora, A., Morbidelli, R., Morel, T., Morris, D., Muraveva, T., Murphy, C. P., Musella, I., Nagy, Z., Noval, L., Ocaña, F., Ogden, A., Ordenovic, C., Osinde, J. O., Pagani, C., Pagano, I., Palaversa, L., Pallas-Quintela, L., Panahi, A., Payne-Wardenaar, S., Esteller, X. Peñalosa, Penttilä, A., Pichon, B., Piersimoni, A. M., Pineau, F. -X., Plachy, E., Plum, G., Prša, A., Pulone, L., Racero, E., Ragaini, S., Rainer, M., Raiteri, C. M., Ramos, P., Ramos-Lerate, M., Regibo, S., Richards, P. J., Diaz, C. Rios, Ripepi, V., Riva, A., Rix, H. -W., Rixon, G., Robichon, N., Robin, A. C., Robin, C., Roelens, M., Rogues, H. R. O., Rohrbasser, L., Romero-Gómez, M., Rowell, N., Royer, F., Mieres, D. Ruz, Rybicki, K. A., Sadowski, G., Núñez, A. Sáez, Sellés, A. Sagristà, Sahlmann, J., Salguero, E., Samaras, N., Gimenez, V. Sanchez, Sanna, N., Santoveña, R., Sarasso, M., Schultheis, M., Sciacca, E., Segol, M., Segovia, J. C., Ségransan, D., Semeux, D., Shahaf, S., Siddiqui, H. I., Siebert, A., Siltala, L., Silvelo, A., Slezak, E., Slezak, I., Smart, R. L., Snaith, O. N., Solano, E., Solitro, F., Souami, D., Souchay, J., Spoto, F., Steele, I. A., Steidelmüller, H., Stephenson, C. A., Süveges, M., Surdej, J., Szabados, L., Szegedi-Elek, E., Taris, F., Taylor, M. B., Teixeira, R., Tolomei, L., Tonello, N., Torra, F., Torra, J., Elipe, G. Torralba, Trabucchi, M., Tsounis, A. T., Turon, C., Ulla, A., Unger, N., Vaillant, M. V., van Dillen, E., van Reeven, W., Vanel, O., Vecchiato, A., Viala, Y., Vicente, D., Voutsinas, S., Weiler, M., Wevers, T., Wyrzykowski, Ł., Yoldas, A., Yvard, P., Zhao, H., Zorec, J., Zucker, S., and Zwitter, T.
Subjects: Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Earth and Planetary Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics
Abstract: Gaia DR3 opens a new era of all-sky spectral analysis of stellar populations thanks to the nearly 5.6 million stars observed by the RVS and parametrised by the GSP-spec module. The all-sky Gaia chemical cartography allows a powerful and precise chemo-dynamical view of the Milky Way with unprecedented spatial coverage and statistical robustness. First, it reveals the strong vertical symmetry of the Galaxy and the flared structure of the disc. Second, the observed kinematic disturbances of the disc -- seen as phase space correlations -- and kinematic or orbital substructures are associated with chemical patterns that favour stars with enhanced metallicities and lower [alpha/Fe] abundance ratios compared to the median values in the radial distributions. This is detected both for young objects that trace the spiral arms and older populations. Several alpha, iron-peak elements and at least one heavy element trace the thin and thick disc properties in the solar cylinder. Third, young disc stars show a recent chemical impoverishment in several elements. Fourth, the largest chemo-dynamical sample of open clusters analysed so far shows a steepening of the radial metallicity gradient with age, which is also observed in the young field population. Finally, the Gaia chemical data have the required coverage and precision to unveil galaxy accretion debris and heated disc stars on halo orbits through their [alpha/Fe] ratio, and to allow the study of the chemo-dynamical properties of globular clusters. Gaia DR3 chemo-dynamical diagnostics open new horizons before the era of ground-based wide-field spectroscopic surveys. They unveil a complex Milky Way that is the outcome of an eventful evolution, shaping it to the present day (abridged)., Comment: Astronomy and Astrophysics (accepted, in press)
Published: 2022
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