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1. Agent-Based Modelling Meets Generative AI in Social Network Simulations

Author

Ferraro, Antonino, Galli, Antonio, La Gatta, Valerio, Postiglione, Marco, Orlando, Gian Marco, Russo, Diego, Riccio, Giuseppe, Romano, Antonio, and Moscato, Vincenzo

Subjects
Computer Science - Social and Information Networks, Computer Science - Multiagent Systems
Abstract

Agent-Based Modelling (ABM) has emerged as an essential tool for simulating social networks, encompassing diverse phenomena such as information dissemination, influence dynamics, and community formation. However, manually configuring varied agent interactions and information flow dynamics poses challenges, often resulting in oversimplified models that lack real-world generalizability. Integrating modern Large Language Models (LLMs) with ABM presents a promising avenue to address these challenges and enhance simulation fidelity, leveraging LLMs' human-like capabilities in sensing, reasoning, and behavior. In this paper, we propose a novel framework utilizing LLM-empowered agents to simulate social network users based on their interests and personality traits. The framework allows for customizable agent interactions resembling various social network platforms, including mechanisms for content resharing and personalized recommendations. We validate our framework using a comprehensive Twitter dataset from the 2020 US election, demonstrating that LLM-agents accurately replicate real users' behaviors, including linguistic patterns and political inclinations. These agents form homogeneous ideological clusters and retain the main themes of their community. Notably, preference-based recommendations significantly influence agent behavior, promoting increased engagement, network homophily and the formation of echo chambers. Overall, our findings underscore the potential of LLM-agents in advancing social media simulations and unraveling intricate online dynamics., Comment: Accepted at ASONAM2024

Published
2024

2. Assessment of Sars-Cov-2 airborne infection transmission risk in public buses

Author

Bertone, Michele, Mikszewski, Alex, Stabile, Luca, Riccio, Giuseppe, Cortellessa, Gino, Ambrosio, Francesca d, Papa, Veronica, Morawska, Lidia, and Buonanno, Giorgio

Subjects
Physics - Physics and Society, Quantitative Biology - Populations and Evolution, 76Axx, A.m
Abstract

In this study, we apply a novel combination of close proximity and room-scale risk assessment approaches for people sharing public transport environments to predict their contagion risk due to SARS-CoV-2 respiratory infection. In particular, the individual infection risk of susceptible subjects and the transmissibility of SARS-CoV-2 (expressed through the reproduction number) are evaluated for two types of buses, differing in terms of exposure time and crowding index: urban and long-distance buses. Infection risk and reproduction number are calculated for different scenarios as a function of the ventilation rates (both measured and estimated according to standards), crowding indexes, and travel times. The results show that for urban buses, the close proximity contribution significantly affects the maximum occupancy to maintain a reproductive number of < 1. In particular, full occupancy of the bus would be permitted only for an infected subject breathing, whereas for an infected subject speaking, masking would be required. For long-distance buses, full occupancy of the bus can be maintained only if specific mitigation solutions are simultaneously applied. For example, for an infected person speaking for 1 h, appropriate filtration of the recirculated air and simultaneous use of FFP2 masks would permit full occupancy of the bus for a period of almost 8 h. Otherwise, a high percentage of immunized persons (> 80%) would be needed., Comment: 22 pages, 5 figures, 6 tables

Published
2021

3. Improving the reliability of photometric redshift with machine learning

Author

Razim, Oleksandra, Cavuoti, Stefano, Brescia, Massimo, Riccio, Giuseppe, Salvato, Mara, and Longo, Giuseppe

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

In order to answer the open questions of modern cosmology and galaxy evolution theory, robust algorithms for calculating photometric redshifts (photo-z) for very large samples of galaxies are needed. Correct estimation of the various photo-z algorithms' performance requires attention to both the performance metrics and the data used for the estimation. In this work, we use the supervised machine learning algorithm MLPQNA to calculate photometric redshifts for the galaxies in the COSMOS2015 catalogue and the unsupervised Self-Organizing Maps (SOM) to determine the reliability of the resulting estimates. We find that for spec-z<1.2, photo-z predictions are on the same level of quality as SED fitting photo-z. We show that the SOM successfully detects unreliable spec-z that cause biases in the estimation of the photo-z algorithms' performance. Additionally, we use SOM to select the objects with reliable photo-z predictions. Our cleaning procedures allow to extract the subset of objects for which the quality of the final photo-z catalogs is improved by a factor of two, compared to the overall statistics., Comment: 26 pages, 15 figures, accepted for publication in MNRAS

Published
2021
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4. Photometric redshifts with machine learning, lights and shadows on a complex data science use case

Author

Brescia, Massimo, Cavuoti, Stefano, Razim, Oleksandra, Amaro, Valeria, Riccio, Giuseppe, and Longo, Giuseppe

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The current role of data-driven science is constantly increasing its importance within Astrophysics, due to the huge amount of multi-wavelength data collected every day, characterized by complex and high-volume information requiring efficient and as much as possible automated exploration tools. Furthermore, to accomplish main and legacy science objectives of future or incoming large and deep survey projects, such as JWST, LSST and Euclid, a crucial role is played by an accurate estimation of photometric redshifts, whose knowledge would permit the detection and analysis of extended and peculiar sources by disentangling low-z from high-z sources and would contribute to solve the modern cosmological discrepancies. The recent photometric redshift data challenges, organized within several survey projects, like LSST and Euclid, pushed the exploitation of multi-wavelength and multi-dimensional data observed or ad hoc simulated to improve and optimize the photometric redshifts prediction and statistical characterization based on both SED template fitting and machine learning methodologies. But they also provided a new impetus in the investigation on hybrid and deep learning techniques, aimed at conjugating the positive peculiarities of different methodologies, thus optimizing the estimation accuracy and maximizing the photometric range coverage, particularly important in the high-z regime, where the spectroscopic ground truth is poorly available. In such a context we summarize what learned and proposed in more than a decade of research., Comment: Preprint version of the manuscript to appear in the Volume: "The Physical Challenges of Astrostatistics", special issue of "Frontiers in Astronomy and Space Sciences"

Published
2021
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5. A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on Solar System bodies

Author

Donadio, Carlo, Brescia, Massimo, Riccardo, Alessia, Angora, Giuseppe, Veneri, Michele Delli, and Riccio, Giuseppe

Subjects
Physics - Geophysics, Astrophysics - Earth and Planetary Astrophysics, Computer Science - Machine Learning, Physics - Data Analysis, Statistics and Probability
Abstract

Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields., Comment: Accepted, To be published on Scientific Reports (Nature Research Journal), 22 pages, 3 figures, 4 tables

Published
2021
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6. Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning

Author

Amaro, Valeria, Cavuoti, Stefano, Brescia, Massimo, Riccio, Giuseppe, Tortora, Crescenzo, D'Addona, Maurizio, Veneri, Michele Delli, Napolitano, Nicola R., Radovich, Mario, and Longo, Giuseppe

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The Probability Density Function (PDF) provides an estimate of the photometric redshift (zphot) prediction error. It is crucial for current and future sky surveys, characterized by strict requirements on the zphot precision, reliability and completeness. The present work stands on the assumption that properly defined rejection criteria, capable of identifying and rejecting potential outliers, can increase the precision of zphot estimates and of their cumulative PDF, without sacrificing much in terms of completeness of the sample. We provide a way to assess rejection through proper cuts on the shape descriptors of a PDF, such as the width and the height of the maximum PDF's peak. In this work we tested these rejection criteria to galaxies with photometry extracted from the Kilo Degree Survey (KiDS) ESO Data Release 4, proving that such approach could lead to significant improvements to the zphot quality: e.g., for the clipped sample showing the best trade-off between precision and completeness, we achieve a reduction in outliers fraction of $\simeq 75\%$ and an improvement of $\simeq 6\%$ for NMAD, with respect to the original data set, preserving the $\simeq 93\%$ of its content., Comment: Preprint version of the manuscript to appear in the Volume "Intelligent Astrophysics" of the series "Emergence, Complexity and Computation", Book eds. I. Zelinka, D. Baron, M. Brescia, Springer Nature Switzerland, ISSN: 2194-7287

Published
2020
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7. Anomaly detection in Astrophysics: a comparison between unsupervised Deep and Machine Learning on KiDS data

Author

D'Addona, Maurizio, Riccio, Giuseppe, Cavuoti, Stefano, Tortora, Crescenzo, and Brescia, Massimo

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Every field of Science is undergoing unprecedented changes in the discovery process, and Astronomy has been a main player in this transition since the beginning. The ongoing and future large and complex multi-messenger sky surveys impose a wide exploiting of robust and efficient automated methods to classify the observed structures and to detect and characterize peculiar and unexpected sources. We performed a preliminary experiment on KiDS DR4 data, by applying to the problem of anomaly detection two different unsupervised machine learning algorithms, considered as potentially promising methods to detect peculiar sources, a Disentangled Convolutional Autoencoder and an Unsupervised Random Forest. The former method, working directly on images, is considered potentially able to identify peculiar objects like interacting galaxies and gravitational lenses. The latter instead, working on catalogue data, could identify objects with unusual values of magnitudes and colours, which in turn could indicate the presence of singularities., Comment: Preprint version of the manuscript to appear in the Volume "Intelligent Astrophysics" of the series "Emergence, Complexity and Computation", Book eds. I. Zelinka, D. Baron, M. Brescia, Springer Nature Switzerland, ISSN: 2194-7287

Published
2020
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8. Astroinformatics based search for globular clusters in the Fornax Deep Survey

Author

Angora, Giuseppe, Brescia, Massimo, Cavuoti, Stefano, Paolillo, Maurizio, Longo, Giuseppe, Cantiello, Michele, Capaccioli, Massimo, D'Abrusco, Raffaele, D'Ago, Giuseppe, Hilker, Michael, Iodice, Enrica, Mieske, Steffen, Napolitano, Nicola, Peletier, Reynier, Pota, Vincenzo, Puzia, Thomas, Riccio, Giuseppe, and Spavone, Marilena

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

In the last years, Astroinformatics has become a well defined paradigm for many fields of Astronomy. In this work we demonstrate the potential of a multidisciplinary approach to identify globular clusters (GCs) in the Fornax cluster of galaxies taking advantage of multi-band photometry produced by the VLT Survey Telescope using automatic self-adaptive methodologies. The data analyzed in this work consist of deep, multi-band, partially overlapping images centered on the core of the Fornax cluster. In this work we use a Neural-Gas model, a pure clustering machine learning methodology, to approach the GC detection, while a novel feature selection method ($\Phi$LAB) is exploited to perform the parameter space analysis and optimization. We demonstrate that the use of an Astroinformatics based methodology is able to provide GC samples that are comparable, in terms of purity and completeness with those obtained using single band HST data (Brescia et al. 2012) and two approaches based respectively on a morpho-photometric (Cantiello et al. 2018b) and a PCA analysis (D'Abrusco et al. 2015) using the same data discussed in this work., Comment: 29 pages, 14 figures

Published
2019
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10. Stellar formation rates in galaxies using Machine Learning models

Author

Veneri, Michele Delli, Cavuoti, Stefano, Brescia, Massimo, Riccio, Giuseppe, and Longo, Giuseppe

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

Global Stellar Formation Rates or SFRs are crucial to constrain theories of galaxy formation and evolution. SFR's are usually estimated via spectroscopic observations which require too much previous telescope time and therefore cannot match the needs of modern precision cosmology. We therefore propose a novel method to estimate SFRs for large samples of galaxies using a variety of supervised ML models., Comment: ESANN 2018 - Proceedings, ISBN-13 9782875870483

Published
2018

11. Neural Gas based classification of Globular Clusters

Author

Angora, Giuseppe, Brescia, Massimo, Cavuoti, Stefano, Riccio, Giuseppe, Paolillo, Maurizio, and Puzia, Thomas H.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms, providing self-adaptive and semi-automatic methods, are able to navigate into large volumes of data characterized by a multi-dimensional parameter space, thus representing an ideal method to disentangle classes of objects in a reliable and efficient way. In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band images, is one of such cases where self-adaptive methods demonstrated a high performance and reliability. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning for the classification of Globular Clusters. Main scope of this work was to verify the possibility to improve the computational efficiency of the methods to solve complex data-driven problems, by exploiting the parallel programming with GPU framework. By using the astrophysical playground, the goal was to scientifically validate such kind of models for further applications extended to other contexts., Comment: 15 pages, 3 figures, to appear in the Volume of Springer Communications in Computer and Information Science (CCIS). arXiv admin note: substantial text overlap with arXiv:1710.03900

Published
2018
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12. Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case

Author

Brescia, Massimo, Cavuoti, Stefano, Amaro, Valeria, Riccio, Giuseppe, Angora, Giuseppe, Vellucci, Civita, and Longo, Giuseppe

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Astronomy has entered the big data era and Machine Learning based methods have found widespread use in a large variety of astronomical applications. This is demonstrated by the recent huge increase in the number of publications making use of this new approach. The usage of machine learning methods, however is still far from trivial and many problems still need to be solved. Using the evaluation of photometric redshifts as a case study, we outline the main problems and some ongoing efforts to solve them., Comment: 13 pages, 3 figures, Springer's Communications in Computer and Information Science (CCIS), Vol. 822

Published
2018
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13. Astrophysical Data Analytics based on Neural Gas Models, using the Classification of Globular Clusters as Playground

Author

Angora, Giuseppe, Brescia, Massimo, Riccio, Giuseppe, Cavuoti, Stefano, Paolillo, Maurizio, and Puzia, Thomas H.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and reliability, demonstrating the capability to improve the traditional approaches. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning, on the classification of Globular Clusters, extracted from the NGC1399 HST data. Main focus of this work was to use a well-tested playground to scientifically validate such kind of models for further extended experiments in astrophysics and using other standard Machine Learning methods (for instance Random Forest and Multi Layer Perceptron neural network) for a comparison of performances in terms of purity and completeness., Comment: Proceedings of the XIX International Conference "Data Analytics and Management in Data Intensive Domains" (DAMDID/RCDL 2017), Moscow, Russia, October 10-13, 2017, 8 pages, 4 figures

Published
2017

14. $C^{3}$ : A Command-line Catalogue Cross-matching tool for modern astrophysical survey data

Author

Riccio, Giuseppe, Brescia, Massimo, Cavuoti, Stefano, Mercurio, Amata, Di Giorgio, Anna Maria, and Molinari, Sergio

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

In the current data-driven science era, it is needed that data analysis techniques has to quickly evolve to face with data whose dimensions has increased up to the Petabyte scale. In particular, being modern astrophysics based on multi-wavelength data organized into large catalogues, it is crucial that the astronomical catalog cross-matching methods, strongly dependant from the catalogues size, must ensure efficiency, reliability and scalability. Furthermore, multi-band data are archived and reduced in different ways, so that the resulting catalogues may differ each other in formats, resolution, data structure, etc, thus requiring the highest generality of cross-matching features. We present $C^{3}$ (Command-line Catalogue Cross-match), a multi-platform application designed to efficiently cross-match massive catalogues from modern surveys. Conceived as a stand-alone command-line process or a module within generic data reduction/analysis pipeline, it provides the maximum flexibility, in terms of portability, configuration, coordinates and cross-matching types, ensuring high performance capabilities by using a multi-core parallel processing paradigm and a sky partitioning algorithm., Comment: 6 pages, 4 figures, proceedings of the IAU-325 symposium on Astroinformatics, Cambridge University press

Published
2017
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15. Statistical Characterization and Classification of Astronomical Transients with Machine Learning in the era of the Vera C. Rubin Observatory

Author

Vicedomini, Marco, Brescia, Massimo, Cavuoti, Stefano, Riccio, Giuseppe, Longo, Giuseppe, Zelinka, Ivan, Series Editor, Adamatzky, Andrew, Series Editor, Chen, Guanrong, Series Editor, Abraham, Ajith, Editorial Board Member, Lucia, Ana, Editorial Board Member, Burguillo, Juan C., Editorial Board Member, Čelikovský, Sergej, Editorial Board Member, Chadli, Mohammed, Editorial Board Member, Corchado, Emilio, Editorial Board Member, Davendra, Donald, Editorial Board Member, Ilachinski, Andrew, Editorial Board Member, Lampinen, Jouni, Editorial Board Member, Middendorf, Martin, Editorial Board Member, Ott, Edward, Editorial Board Member, Pan, Linqiang, Editorial Board Member, Păun, Gheorghe, Editorial Board Member, Richter, Hendrik, Editorial Board Member, Rodriguez-Aguilar, Juan A., Editorial Board Member, Rössler, Otto, Editorial Board Member, Snasel, Vaclav, Editorial Board Member, Vondrák, Ivo, Editorial Board Member, Zenil, Hector, Editorial Board Member, Brescia, Massimo, editor, and Baron, Dalya, editor

Published
2021
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16. A Command-line Cross-matching tool for modern astrophysical pipelines

Author

Riccio, Giuseppe, Brescia, Massimo, Cavuoti, Stefano, Mercurio, Amata, Di Giorgio, Anna Maria, and Molinari, Sergio

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The emerging need for efficient, reliable and scalable astronomical catalog cross-matching is becoming more pressing in the current data-driven science era, where the size of data has rapidly increased up to the Petabyte scale. C3 (Command-line Catalogue Cross-matching) is a multi-platform tool designed to efficiently cross-match massive catalogues from modern astronomical surveys, ensuring high-performance capabilities through the use of a multi-core parallel processing paradigm. The tool has been conceived to be executed as a stand-alone command-line process or integrated within any generic data reduction/analysis pipeline, providing the maximum flexibility to the end user, in terms of parameter configuration, coordinates and cross-matching types. In this work we present the architecture and the features of the tool. Moreover, since the modular design of the tool enables an easy customization to specific use cases and requirements, we present also an example of a customized C3 version designed and used in the FP7 project ViaLactea, dedicated to cross-correlate Hi-GAL clumps with multi-band compact sources., Comment: 4 pages, to appear in the Proceedings of ADASS 2016, Astronomical Society of the Pacific (ASP) Conference Series

Published
2016

17. C3, A Command-line Catalogue Cross-match tool for large astrophysical catalogues

Author

Riccio, Giuseppe, Brescia, Massimo, Cavuoti, Stefano, Mercurio, Amata, di Giorgio, Anna Maria, and Molinari, Sergio

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

Modern Astrophysics is based on multi-wavelength data organized into large and heterogeneous catalogues. Hence, the need for efficient, reliable and scalable catalogue cross-matching methods plays a crucial role in the era of the petabyte scale. Furthermore, multi-band data have often very different angular resolution, requiring the highest generality of cross-matching features, mainly in terms of region shape and resolution. In this work we present $C^{3}$ (Command-line Catalogue Cross-match), a multi-platform application designed to efficiently cross-match massive catalogues. It is based on a multi-core parallel processing paradigm and conceived to be executed as a stand-alone command-line process or integrated within any generic data reduction/analysis pipeline, providing the maximum flexibility to the end-user, in terms of portability, parameter configuration, catalogue formats, angular resolution, region shapes, coordinate units and cross-matching types. Using real data, extracted from public surveys, we discuss the cross-matching capabilities and computing time efficiency also through a direct comparison with some publicly available tools, chosen among the most used within the community, and representative of different interface paradigms. We verified that the $C^{3}$ tool has excellent capabilities to perform an efficient and reliable cross-matching between large datasets. Although the elliptical cross-match and the parametric handling of angular orientation and offset are known concepts in the astrophysical context, their availability in the presented command-line tool makes $C^{3}$ competitive in the context of public astronomical tools., Comment: 18 pages, 9 figures, Accepted for publication on PASP

Published
2016
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18. VIALACTEA knowledge base homogenizing access to Milky Way data

Author

Molinaro, Marco, Butora, Robert, Bandieramonte, Marilena, Becciani, Ugo, Brescia, Massimo, Cavuoti, Stefano, Costa, Alessandro, Di Giorgio, Anna M., Elia, Davide, Hajnal, Akos, Gabor, Hermann, Kacsuk, Peter, Liu, Scige J., Molinari, Sergio, Riccio, Giuseppe, Schisano, Eugenio, Sciacca, Eva, Smareglia, Riccardo, and Vitello, Fabio

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The VIALACTEA project has a work package dedicated to Tools and Infrastructure and, inside it, a task for the Database and Virtual Observatory Infrastructure. This task aims at providing an infrastructure to store all the resources needed by the, more purposely, scientific work packages of the project itself. This infrastructure includes a combination of: storage facilities, relational databases and web services on top of them, and has taken, as a whole, the name of VIALACTEA Knowledge Base (VLKB). This contribution illustrates the current status of this VLKB. It details the set of data resources put together; describes the database that allows data discovery through VO inspired metadata maintenance; illustrates the discovery, cutout and access services built on top of the former two for the users to exploit the data content., Comment: 11 pages, 1 figure, SPIE Astronomical Telescopes + Instrumentation 2016, Software and Cyberifrastructure for Astronomy IV, Conference Proceedings

Published
2016
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19. An analysis of feature relevance in the classification of astronomical transients with machine learning methods

Author

D'Isanto, Antonio, Cavuoti, Stefano, Brescia, Massimo, Donalek, Ciro, Longo, Giuseppe, Riccio, Giuseppe, and Djorgovski, Stanislav G.

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The exploitation of present and future synoptic (multi-band and multi-epoch) surveys requires an extensive use of automatic methods for data processing and data interpretation. In this work, using data extracted from the Catalina Real Time Transient Survey (CRTS), we investigate the classification performance of some well tested methods: Random Forest, MLPQNA (Multi Layer Perceptron with Quasi Newton Algorithm) and K-Nearest Neighbors, paying special attention to the feature selection phase. In order to do so, several classification experiments were performed. Namely: identification of cataclysmic variables, separation between galactic and extra-galactic objects and identification of supernovae., Comment: Accepted by MNRAS, 11 figures, 18 pages

Published
2016
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20. Advanced Environment for Knowledge Discovery in the VIALACTEA Project

Author

Becciani, Ugo, Bandieramonte, Marilena, Brescia, Massimo, Butora, Robert, Cavuoti, Stefano, Costa, Alessandro, di Giorgio, Anna Maria, Elia, Davide, Hajnal, Akos, Kacsuk, Peter, Liu, Scige John, Mercurio, Amata, Molinari, Sergio, Molinaro, Marco, Riccio, Giuseppe, Schisano, Eugenio, Sciacca, Eva, Smareglia, Riccardo, and Vitello, Fabio

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The VIALACTEA project aims at building a predictive model of star formation in our galaxy. We present the innovative integrated framework and the main technologies and methodologies to reach this ambitious goal., Comment: Astronomical Data Analysis Software and Systems XXV. Proceedings of a Conference held from October 25th to 30th, 2015 at Rydges World Square in Sydney, Australia

Published
2015

21. Machine learning based data mining for Milky Way filamentary structures reconstruction

Author

Riccio, Giuseppe, Cavuoti, Stefano, Schisano, Eugenio, Brescia, Massimo, Mercurio, Amata, Elia, Davide, Benedettini, Milena, Pezzuto, Stefano, Molinari, Sergio, and Di Giorgio, Anna Maria

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Computer Science - Computer Vision and Pattern Recognition
Abstract

We present an innovative method called FilExSeC (Filaments Extraction, Selection and Classification), a data mining tool developed to investigate the possibility to refine and optimize the shape reconstruction of filamentary structures detected with a consolidated method based on the flux derivative analysis, through the column-density maps computed from Herschel infrared Galactic Plane Survey (Hi-GAL) observations of the Galactic plane. The present methodology is based on a feature extraction module followed by a machine learning model (Random Forest) dedicated to select features and to classify the pixels of the input images. From tests on both simulations and real observations the method appears reliable and robust with respect to the variability of shape and distribution of filaments. In the cases of highly defined filament structures, the presented method is able to bridge the gaps among the detected fragments, thus improving their shape reconstruction. From a preliminary "a posteriori" analysis of derived filament physical parameters, the method appears potentially able to add a sufficient contribution to complete and refine the filament reconstruction., Comment: Proceeding of WIRN 2015 Conference, May 20-22, Vietri sul Mare, Salerno, Italy. Published in Smart Innovation, Systems and Technology, Springer, ISSN 2190-3018, 9 pages, 4 figures

Published
2015
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22. Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

Author

Tangaro, Sabina, Amoroso, Nicola, Brescia, Massimo, Cavuoti, Stefano, Chincarini, Andrea, Errico, Rosangela, Inglese, Paolo, Longo, Giuseppe, Maglietta, Rosalia, Tateo, Andrea, Riccio, Giuseppe, and Bellotti, Roberto

Subjects
Physics - Medical Physics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Learning
Abstract

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic Resonance Imaging (MRI) scans can show these variations and therefore be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach, for each voxel a number of local features were calculated. In this paper we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) Sequential Forward Selection and (iii) Sequential Backward Elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 features for each voxel (sequential backward elimination) we obtained comparable state of-the-art performances with respect to the standard tool FreeSurfer., Comment: To appear on "Computational and Mathematical Methods in Medicine", Hindawi Publishing Corporation. 19 pages, 7 figures

Published
2015
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24. VIALACTEA science gateway for Milky Way analysis

Author

Sciacca, Eva, Vitello, Fabio, Becciani, Ugo, Costa, Alessandro, Hajnal, Akos, Kacsuk, Peter, Farkas, Zoltan, Marton, Istvan, Molinari, Sergio, Di Giorgio, Anna Maria, Schisano, Eugenio, Liu, Scige John, Elia, Davide, Cavuoti, Stefano, Riccio, Giuseppe, and Brescia, Massimo

Published
2019
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25. Astroinformatics of galaxies and quasars: a new general method for photometric redshifts estimation

Author

Laurino, Omar, D'Abrusco, Raffaele, Longo, Giuseppe, and Riccio, Giuseppe

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - High Energy Astrophysical Phenomena, Statistics - Machine Learning
Abstract

With the availability of the huge amounts of data produced by current and future large multi-band photometric surveys, photometric redshifts have become a crucial tool for extragalactic astronomy and cosmology. In this paper we present a novel method, called Weak Gated Experts (WGE), which allows to derive photometric redshifts through a combination of data mining techniques. \noindent The WGE, like many other machine learning techniques, is based on the exploitation of a spectroscopic knowledge base composed by sources for which a spectroscopic value of the redshift is available. This method achieves a variance \sigma^2(\Delta z)=2.3x10^{-4} (\sigma^2(\Delta z) =0.08), where \Delta z = z_{phot} - z_{spec}) for the reconstruction of the photometric redshifts for the optical galaxies from the SDSS and for the optical quasars respectively, while the Root Mean Square (RMS) of the \Delta z variable distributions for the two experiments is respectively equal to 0.021 and 0.35. The WGE provides also a mechanism for the estimation of the accuracy of each photometric redshift. We also present and discuss the catalogs obtained for the optical SDSS galaxies, for the optical candidate quasars extracted from the DR7 SDSS photometric dataset {The sample of SDSS sources on which the accuracy of the reconstruction has been assessed is composed of bright sources, for a subset of which spectroscopic redshifts have been measured.}, and for optical SDSS candidate quasars observed by GALEX in the UV range. The WGE method exploits the new technological paradigm provided by the Virtual Observatory and the emerging field of Astroinformatics., Comment: 36 pages, 22 figures and 8 tables

Published
2011
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32. Study on tiered storage algorithm based on heat correlation of astronomical data.

Author

Ye, Xin-Chen, Zhang, Hai-Long, Wang, Jie, Zhang, Ya-Zhou, Du, Xu, Wu, Han, and Riccio, Giuseppe

Subjects
RADIO telescopes, GEODETIC astronomy, PULSAR detection, ELECTRONIC data processing, ALGORITHMS, CLOUD storage
Abstract

With the surge in astronomical data volume, modern astronomical research faces significant challenges in data storage, processing, and access. The I/O bottleneck issue in astronomical data processing is particularly prominent, limiting the efficiency of data processing. To address this issue, this paper proposes a tiered storage algorithm based on the access characteristics of astronomical data. The C4.5 decision tree algorithm is employed as the foundation to implement an astronomical data access correlation algorithm. Additionally, a data copy migration strategy is designed based on tiered storage technology to achieve efficient data access. Preprocessing tests were conducted on 418GB NSRT (Nanshan Radio Telescope) formaldehyde spectral line data, showcasing that tiered storage can potentially reduce data processing time by up to 38.15%. Similarly, utilizing 802.2 GB data from FAST (Five- hundred-meter Aperture Spherical radio Telescope) observations for pulsar search data processing tests, the tiered storage approach demonstrated a maximum reduction of 29.00% in data processing time. In concurrent testing of data processing workflows, the proposed astronomical data heat correlation algorithm in this paper achieved an average reduction of 17.78% in data processing time compared to centralized storage. Furthermore, in comparison to traditional heat algorithms, it reduced data processing time by 5.15%. The effectiveness of the proposed algorithm is positively correlated with the associativity between the algorithm and the processed data. The tiered storage algorithm based on the characteristics of astronomical data proposed in this paper is poised to provide algorithmic references for large-scale data processing in the field of astronomy in the future. [ABSTRACT FROM AUTHOR]

Published
2024
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43. AIDA, a Modular Web Application for Astronomical Data Analysis and Instrument Monitoring Services

Author

Riccio, Giuseppe, Cavuoti, S., Brescia, M., Jose Enrique Ruiz, Francesco Pierfedereci, and Peter Teuben, Riccio, Giuseppe, Cavuoti, S., and Brescia, M.

Abstract

In the last decade, Astronomy has been the scene of the realization of panchromatic surveys, with sophisticated instruments acquiring a huge quantity of exceptional quality data. This poses the need to integrate advanced data-driven science methodologies for the automatic exploration of huge data archives, and the need for efficient short- and long-term monitoring and diagnostics systems. The goal is to keep the quality of the observations under control and to detect and circumscribe anomalies and malfunctions, facilitating rapid and effective corrections, ensuring correct maintenance of all components and the good health of scientific data over time. In particular, this requirement is crucial for space-borne observation systems, both in logistical and economic terms. AIDA (Advanced Infrastructure for Data Analysis) is a portable and modular web application, designed to provide an efficient and intuitive software infrastructure to support monitoring of data acquiring systems over time, diagnostics and both scientific and engineering data quality analysis, particularly suited for astronomical instruments. Given its modular system prerogative, it is possible to extend its functionalities, by integrating and customizing monitoring and diagnostics systems, as well as scientific data analysis solutions, including machine/deep learning and data mining techniques and methods. A specialized version of AIDA has been recently appointed as focal plane instrument operation diagnostics, analytics and monitoring service within the Science Ground Segment of the Euclid space mission.

Published
2022

45. Euclid preparation - XVII. Cosmic Dawn Survey: Spitzer Space Telescope observations of the Euclid deep fields and calibration fields

Author

Euclid Collaboration, Moneti, A., McCracken, H. J., Shuntov, M., Kauffmann, O. B., Capak, P., Davidzon, I., Ilbert, O., Scarlata, C., Toft, S., Weaver, J., Pires, S., Poncet, M., Popa, L., POZZETTI, Lucia, Raison, F., Rebolo, R., Rhodes, J., Rix, H., Roncarelli, M., Rossetti, E., Chary, R., Saglia, R., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Cuby, J., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Wang, Y., Welikala, N., Weller, J., Zamorani, G., Zoubian, J., ANDREON, Stefano, BARDELLI, Sandro, Faisst, A. L., Camera, S., Graciá-Carpio, J., Medinaceli, E., Mei, S., Polenta, G., Romelli, Erik, Sureau, F., Tenti, M., Vassallo, T., ZACCHEI, Andrea, Masters, D. C., ZUCCA, Elena, Baccigalupi, C., Balaguera-Antolínez, A., Bernardeau, F., BIVIANO, ANDREA, BOLZONELLA, MICOL, Bozzo, E., BURIGANA, CARLO, Cabanac, R., CAPPI, Alberto, McPartland, C., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Di Ferdinando, D., FARINA, Maria, FINELLI, FABIO, Flose-Reimberg, P., Mobasher, B., Fotopoulou, S., GALEOTTA, Samuele, Ganga, K., Garcia-Bellido, J., Gaztanaga, E., Gozaliasl, G., Hook, I., Joachimi, B., Kansal, V., Keihanen, E., Sanders, D. B., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maino, D., Maoli, R., Martinelli, M., Martinet, N., Maturi, M., Metcalf, R. B., MORGANTE, GIANLUCA, SCARAMELLA, Roberto, Morisset, N., Nucita, A., Patrizii, L., Potter, D., Renzi, A., RICCIO, GIUSEPPE, Sánchez, A. G., Sapone, D., Schirmer, M., Schultheis, M., Stern, D., Scottez, V., SEFUSATTI, Emiliano, Teyssier, R., Tubio, O., Tutusaus, I., Valiviita, J., VIEL, MATTEO, Hildebrandt, H., Szapudi, I., Teplitz, H., Zalesky, L., Amara, A., AURICCHIO, NATALIA, Bodendorf, C., BONINO, Donata, Branchini, Enzo, Brau-Nogue, S., BRESCIA, Massimo, Brinchmann, J., Capobianco, Vito, CARBONE, Carmelita, Carretero, J., Castander, F. J., CASTELLANO, MARCO, CAVUOTI, STEFANO, Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., CORCIONE, Leonardo, Costille, A., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Fosalba, P., FRAILIS, Marco, FRANCESCHI, ENRICO, FUMANA, Marco, GARILLI, BIANCA MARIA ROSA, Gillis, B., GIOCOLI, Carlo, Granett, B. R., GRAZIAN, Andrea, Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kümmel, M., Kunz, M., Kurki-Suonio, H., LIGORI, Sebastiano, Lilje, P. B., Lloro, I., MAIORANO, Elisabetta, MANSUTTI, Oriana, Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., MENEGHETTI, MASSIMO, MERLIN, Emiliano, Meylan, G., Moresco, M., Moscardini, L., Munari, Emiliano, Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Astrophysique de Marseille (LAM), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Centre National d'Études Spatiales [Toulouse] (CNES), Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Institut d'astrophysique spatiale (IAS), Université Paris-Sud - Paris 11 (UP11)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National d’Études Spatiales [Paris] (CNES), Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Institut de Ciencies de l'Espai [Barcelona] (ICE-CSIC), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Institut d'Estudis Espacials de Catalunya (IEEC-CSIC), Centre de Calcul de l'IN2P3 (CC-IN2P3), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Euclid Collaboration, Moneti, A., Mccracken, H. J., Shuntov, M., Kauffmann, O. B., Capak, P., Davidzon, I., Ilbert, O., Scarlata, C., Toft, S., Weaver, J., Chary, R., Cuby, J., Faisst, A. L., Masters, D. C., Mcpartland, C., Mobasher, B., Sanders, D. B., Scaramella, R., Stern, D., Szapudi, I., Teplitz, H., Zalesky, L., Amara, A., Auricchio, N., Bodendorf, C., Bonino, D., Branchini, E., Brau-Nogue, S., Brescia, M., Brinchmann, J., Capobianco, V., Carbone, C., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Garilli, B., Gillis, B., Giocoli, C., Granett, B. R., Grazian, A., Grupp, F., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., K??mmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Rhodes, J., Rix, H., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Tallada-Cresp??, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Wang, Y., Welikala, N., Weller, J., Zamorani, G., Zoubian, J., Andreon, S., Bardelli, S., Camera, S., Graci??-Carpio, J., Medinaceli, E., Mei, S., Polenta, G., Romelli, E., Sureau, F., Tenti, M., Vassallo, T., Zacchei, A., Zucca, E., Baccigalupi, C., Balaguera-Antol??nez, A., Bernardeau, F., Biviano, A., Bolzonella, M., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Di Ferdinando, D., Farina, M., Finelli, F., Flose-Reimberg, P., Fotopoulou, S., Galeotta, S., Ganga, K., Garcia-Bellido, J., Gaztanaga, E., Gozaliasl, G., Hook, I., Joachimi, B., Kansal, V., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maino, D., Maoli, R., Martinelli, M., Martinet, N., Maturi, M., Metcalf, R. B., Morgante, G., Morisset, N., Nucita, A., Patrizii, L., Potter, D., Renzi, A., Riccio, G., S??nchez, A. G., Sapone, D., Schirmer, M., Schultheis, M., Scottez, V., Sefusatti, E., Teyssier, R., Tubio, O., Tutusaus, I., Valiviita, J., Viel, M., Hildebrandt, H., Department of Physics, Research Program in Systems Oncology, Helsinki Institute of Physics, UAM. Departamento de Física Teórica, Moneti A., Mccracken H.J., Shuntov M., Kauffmann O.B., Capak P., Davidzon I., Ilbert O., Scarlata C., Toft S., Weaver J., Chary R., Cuby J., Faisst A.L., Masters D.C., Mcpartland C., Mobasher B., Sanders D.B., Scaramella R., Stern D., Szapudi I., Teplitz H., Zalesky L., Amara A., Auricchio N., Bodendorf C., Bonino D., Branchini E., Brau-Nogue S., Brescia M., Brinchmann J., Capobianco V., Carbone C., Carretero J., Castander F.J., Castellano M., Cavuoti S., Cimatti A., Cledassou R., Congedo G., Conselice C.J., Conversi L., Copin Y., Corcione L., Costille A., Cropper M., Da Silva A., Degaudenzi H., Douspis M., Dubath F., Duncan C.A.J., Dupac X., Dusini S., Farrens S., Ferriol S., Fosalba P., Frailis M., Franceschi E., Fumana M., Garilli B., Gillis B., Giocoli C., Granett B.R., Grazian A., Grupp F., Haugan S.V.H., Hoekstra H., Holmes W., Hormuth F., Hudelot P., Jahnke K., Kermiche S., Kiessling A., Kilbinger M., Kitching T., Kohley R., Kummel M., Kunz M., Kurki-Suonio H., Ligori S., Lilje P.B., Lloro I., Maiorano E., Mansutti O., Marggraf O., Markovic K., Marulli F., Massey R., Maurogordato S., Meneghetti M., Merlin E., Meylan G., Moresco M., Moscardini L., Munari E., Niemi S.M., Padilla C., Paltani S., Pasian F., Pedersen K., Pires S., Poncet M., Popa L., Pozzetti L., Raison F., Rebolo R., Rhodes J., Rix H., Roncarelli M., Rossetti E., Saglia R., Schneider P., Secroun A., Seidel G., Serrano S., Sirignano C., Sirri G., Stanco L., Tallada-Crespi P., Taylor A.N., Tereno I., Toledo-Moreo R., Torradeflot F., Wang Y., Welikala N., Weller J., Zamorani G., Zoubian J., Andreon S., Bardelli S., Camera S., Gracia-Carpio J., Medinaceli E., Mei S., Polenta G., Romelli E., Sureau F., Tenti M., Vassallo T., Zacchei A., Zucca E., Baccigalupi C., Balaguera-Antolinez A., Bernardeau F., Biviano A., Bolzonella M., Bozzo E., Burigana C., Cabanac R., Cappi A., Carvalho C.S., Casas S., Castignani G., Colodro-Conde C., Coupon J., Courtois H.M., Di Ferdinando D., Farina M., Finelli F., Flose-Reimberg P., Fotopoulou S., Galeotta S., Ganga K., Garcia-Bellido J., Gaztanaga E., Gozaliasl G., Hook I., Joachimi B., Kansal V., Keihanen E., Kirkpatrick C.C., Lindholm V., Mainetti G., Maino D., Maoli R., Martinelli M., Martinet N., Maturi M., Metcalf R.B., Morgante G., Morisset N., Nucita A., Patrizii L., Potter D., Renzi A., Riccio G., Sanchez A.G., Sapone D., Schirmer M., Schultheis M., Scottez V., Sefusatti E., Teyssier R., Tubio O., Tutusaus I., Valiviita J., Viel M., Hildebrandt H., Kummel, M., Tallada-Crespi, P., Gracia-Carpio, J., Balaguera-Antolinez, A., and Sanchez, A. G.

Subjects
SAMPLE, Large-scale structure of Universe, DATA RELEASE, Formation, Surveys, observations, Dark energy, Dark matter, Galaxy: formation, Large-scale structure of Universe, Surveys [Cosmology], kosmologia, Astrophysics, Cosmology: observation, dark matter, galaksijoukot, pimeä aine, surveys, Dark energy, Dark matter, observations [Cosmology], dark energy, Observations, LEGACY SURVEY, Astrophysics of Galaxies, Física, Astronomy and Astrophysics, 115 Astronomy, Space science, EVOLUTION, Cosmology, galaksit, GALAXIES, Cosmology: observations, Galaxy: formation, Galaxy, formation [Galaxy], Space and Planetary Science, cosmology: observations, large-scale structure of Universe, pimeä energia, Cosmology and Nongalactic Astrophysics, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]
Abstract

Artículo escrito por un elevado núnmero de autores, sólo se referencian el qque aparece en primer lugar, los autores pertenecientes a la UAM y el nombre del grupo de colaboración, si lo hubiere, We present a new infrared survey covering the three Euclid deep fields and four other Euclid calibration fields using Spitzer Space Telescope's Infrared Array Camera (IRAC). We combined these new observations with all relevant IRAC archival data of these fields in order to produce the deepest possible mosaics of these regions. In total, these observations represent nearly 11 % of the total Spitzer Space Telescope mission time. The resulting mosaics cover a total of approximately 71.5 deg2 in the 3.6 and 4.5 μm bands, and approximately 21.8 deg2 in the 5.8 and 8 μm bands. They reach at least 24 AB magnitude (measured to 5σ, in a 2″​​.5 aperture) in the 3.6 μm band and up to ∼5 mag deeper in the deepest regions. The astrometry is tied to the Gaia astrometric reference system, and the typical astrometric uncertainty for sources with 16 "< "[3.6]< 19 is ≲ 0″​​.15. The photometric calibration is in excellent agreement with previous WISE measurements. We extracted source number counts from the 3.6 μm band mosaics, and they are in excellent agreement with previous measurements. Given that the Spitzer Space Telescope has now been decommissioned, these mosaics are likely to be the definitive reduction of these IRAC data. This survey therefore represents an essential first step in assembling multi-wavelength data on the Euclid deep fields, which are set to become some of the premier fields for extragalactic astronomy in the 2020s

Published
2022
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46. Hue-Heat Hypothesis: A Step forward for a Holistic Approach to IEQ

Author

d’Ambrosio Alfano Francesca Romana, Bellia Laura, Fragliasso Francesca, Palella Boris Igor, and Riccio Giuseppe

Subjects
Environmental sciences, GE1-350
Abstract

For many years different human factors contributing to the IEQ have been studied separately. Concerning thermal perception, despite it is almost accepted that thermal comfort can be influenced by concomitant stimulation of non-tactile modalities, relatively few investigations have succeeded in delineating non-tactile stimulations as the visual ones. The hue-heat hypothesis is based on the idea that, when spectral irradiance pattern at the observer’s eye shows a great amount of short wavelengths, the space is perceived as cooler. Conversely, when long wavelengths are predominant, the space is perceived as warmer. This means that operating on light characteristics could help in improving thermal comfort for the occupants with possible energy savings obtained by acting on the set-point temperature of HVAC systems. To verify this hypothesis, this paper will deal with a subjective investigation carried out in a special mechanically conditioned test room equipped with white-tuning LED sources. Investigated subjects have been exposed to two different light scenes consisting of warm (3000 K) and cool light sources (6000 K) at a fixed task illuminance value. Preliminary results seem to demonstrate that cool light is effective in shifting to cool the perceived thermal sensation with a general increase of people under neutral conditions.

Published
2019
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47. Euclid preparation XV. Forecasting cosmological constraints for the Euclid and CMB joint analysis

Author

Euclid Collaboration, Ilić, S., Aghanim, N., Baccigalupi, C., BERMEJO CLIMENT, JOSE RAMON, Fabbian, G., Legrand, L., PAOLETTI, DANIELA, Ballardini, M., Archidiacono, M., Douspis, M., Kubik, B., Kümmel, M., Kurki-Suonio, H., Laureijs, R., LIGORI, Sebastiano, Lilje, P. B., Lloro, I., MANSUTTI, Oriana, Marggraf, O., Marulli, F., FINELLI, FABIO, Massey, R., Maurogordato, S., MENEGHETTI, MASSIMO, MERLIN, Emiliano, Meylan, G., Moresco, M., Morin, B., Moscardini, L., Munari, Emiliano, Niemi, S. M., Ganga, K., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W., Pires, S., Poncet, M., Popa, L., POZZETTI, Lucia, Raison, F., Hernández-Monteagudo, C., Rebolo, R., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., SCARAMELLA, Roberto, Schneider, P., Secroun, A., Seidel, G., Serrano, S., LATTANZI, Mario Gilberto, Sirignano, C., Starck, J. L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., VALENZIANO, LUCA, Verdoes Kleijn, G. A., Marinucci, D., Wang, Y., Welikala, N., Weller, J., Zamorani, G., Zoubian, J., Medinaceli, E., Mei, S., Rosset, C., Sureau, F., Vassallo, T., Migliaccio, M., ZACCHEI, Andrea, ANDREON, Stefano, Balaguera-Antolínez, A., Baldi, M., BARDELLI, Sandro, BIVIANO, ANDREA, BORGANI, STEFANO, Bozzo, E., BURIGANA, CARLO, Cabanac, R., CARBONE, Carmelita, CAPPI, Alberto, Carvalho, C. S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Cuby, J., de la Torre, S., Di Ferdinando, D., Dole, H., Casas, S., FARINA, Maria, Ferreira, P. G., Flose-Reimberg, P., GALEOTTA, Samuele, Gozaliasl, G., Graciá-Carpio, J., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Martinelli, M., Maino, D., Martinet, N., Maturi, M., Metcalf, R. B., MORGANTE, GIANLUCA, Neissner, C., Nightingale, J., Nucita, A. A., Potter, D., RICCIO, GIUSEPPE, Tutusaus, I., Romelli, Erik, Schirmer, M., Schultheis, M., Scottez, V., Teyssier, R., Tramacere, A., Valiviita, J., VIEL, MATTEO, Whittaker, L., ZUCCA, Elena, Natoli, P., Ntelis, P., Pagano, L., Wenzl, L., GRUPPUSO, ALESSANDRO, Kitching, T., Langer, M., Mauri, N., Patrizii, L., Renzi, A., Sirri, G., Stanco, L., Tenti, M., Vielzeuf, P., Lacasa, F., Polenta, G., Yankelevich, V., Blanchard, A., Sakr, Z., Pourtsidou, A., Camera, S., CARDONE, Vincenzo Fabrizio, Kilbinger, M., Kunz, M., Markovic, K., Pettorino, V., Sánchez, A. G., Sapone, D., Amara, A., AURICCHIO, NATALIA, Bender, R., Bodendorf, C., BONINO, Donata, Branchini, Enzo, BRESCIA, Massimo, Brinchmann, J., Capobianco, Vito, Carretero, J., Castander, F. J., CASTELLANO, MARCO, CAVUOTI, STEFANO, Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., CORCIONE, Leonardo, Costille, A., Cropper, M., Da Silva, A., Degaudenzi, H., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Farrens, S., Fosalba, P., FRAILIS, Marco, FRANCESCHI, ENRICO, FRANZETTI, PAOLO, FUMANA, Marco, GARILLI, BIANCA MARIA ROSA, Gillard, W., Gillis, B., GIOCOLI, Carlo, GRAZIAN, Andrea, Grupp, F., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kohley, R., Institut d'astrophysique spatiale (IAS), Université Paris-Sud - Paris 11 (UP11)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National d’Études Spatiales [Paris] (CNES), Scuola Internazionale Superiore di Studi Avanzati / International School for Advanced Studies (SISSA / ISAS), Universidad de La Laguna [Tenerife - SP] (ULL), Instituto de Astrofisica de Canarias (IAC), Istituto di Astrofisica Spaziale e Fisica cosmica - Bologna (IASF-Bo), Istituto Nazionale di Astrofisica (INAF), AstroParticule et Cosmologie (APC (UMR_7164)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), International Center for Relativistic Astrophysics (ICRA), Università degli Studi di Roma, Dipartimento di Matematica [Roma II] (DIPMAT), Università degli Studi di Roma Tor Vergata [Roma], Centre de Physique des Particules de Marseille (CPPM), Aix Marseille Université (AMU)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Centre National d’Études Spatiales [Paris] (CNES), Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Astrophysique Interprétation Modélisation (AIM (UMR7158 / UMR_E_9005 / UM_112)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Sud - Paris 11 (UP11), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Centre National d'Études Spatiales [Toulouse] (CNES), Herschel Science Center [Madrid], European Space Astronomy Centre (ESAC), Agence Spatiale Européenne = European Space Agency (ESA)-Agence Spatiale Européenne = European Space Agency (ESA), Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Astrophysique de Marseille (LAM), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Centre d'étude spatiale des rayonnements (CESR), Université Paris-Saclay, Université Paris Cité (UPCité), CEA- Saclay (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Joseph Louis LAGRANGE (LAGRANGE), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Centre de Calcul de l'IN2P3 (CC-IN2P3), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Euclid Collaboration, Astronomy, Intelligent Systems, Ilic S., Aghanim N., Baccigalupi C., Bermejo-Climent J.R., Fabbian G., Legrand L., Paoletti D., Ballardini M., Archidiacono M., Douspis M., Finelli F., Ganga K., Hernandez-Monteagudo C., Lattanzi M., Marinucci D., Migliaccio M., Carbone C., Casas S., Martinelli M., Tutusaus I., Natoli P., Ntelis P., Pagano L., Wenzl L., Gruppuso A., Kitching T., Langer M., Mauri N., Patrizii L., Renzi A., Sirri G., Stanco L., Tenti M., Vielzeuf P., Lacasa F., Polenta G., Yankelevich V., Blanchard A., Sakr Z., Pourtsidou A., Camera S., Cardone V.F., Kilbinger M., Kunz M., Markovic K., Pettorino V., Sanchez A.G., Sapone D., Amara A., Auricchio N., Bender R., Bodendorf C., Bonino D., Branchini E., Brescia M., Brinchmann J., Capobianco V., Carretero J., Castander F.J., Castellano M., Cavuoti S., Cimatti A., Cledassou R., Congedo G., Conselice C.J., Conversi L., Copin Y., Corcione L., Costille A., Cropper M., Da Silva A., Degaudenzi H., Dubath F., Duncan C.A.J., Dupac X., Dusini S., Ealet A., Farrens S., Fosalba P., Frailis M., Franceschi E., Franzetti P., Fumana M., Garilli B., Gillard W., Gillis B., Giocoli C., Grazian A., Grupp F., Guzzo L., Haugan S.V.H., Hoekstra H., Holmes W., Hormuth F., Hudelot P., Jahnke K., Kermiche S., Kiessling A., Kohley R., Kubik B., Kummel M., Kurki-Suonio H., Laureijs R., Ligori S., Lilje P.B., Lloro I., Mansutti O., Marggraf O., Marulli F., Massey R., Maurogordato S., Meneghetti M., Merlin E., Meylan G., Moresco M., Morin B., Moscardini L., Munari E., Niemi S.M., Padilla C., Paltani S., Pasian F., Pedersen K., Percival W., Pires S., Poncet M., Popa L., Pozzetti L., Raison F., Rebolo R., Rhodes J., Roncarelli M., Rossetti E., Saglia R., Scaramella R., Schneider P., Secroun A., Seidel G., Serrano S., Sirignano C., Starck J.L., Tallada-Crespi P., Taylor A.N., Tereno I., Toledo-Moreo R., Torradeflot F., Valentijn E.A., Valenziano L., Verdoes Kleijn G.A., Wang Y., Welikala N., Weller J., Zamorani G., Zoubian J., Medinaceli E., Mei S., Rosset C., Sureau F., Vassallo T., Zacchei A., Andreon S., Balaguera-Antolinez A., Baldi M., Bardelli S., Biviano A., Borgani S., Bozzo E., Burigana C., Cabanac R., Cappi A., Carvalho C.S., Castignani G., Colodro-Conde C., Coupon J., Courtois H.M., Cuby J., De La Torre S., Di Ferdinando D., Dole H., Farina M., Ferreira P.G., Flose-Reimberg P., Galeotta S., Gozaliasl G., Gracia-Carpio J., Keihanen E., Kirkpatrick C.C., Lindholm V., Mainetti G., Maino D., Martinet N., Maturi M., Metcalf R.B., Morgante G., Neissner C., Nightingale J., Nucita A.A., Potter D., Riccio G., Romelli E., Schirmer M., Schultheis M., Scottez V., Teyssier R., Tramacere A., Valiviita J., Viel M., Whittaker L., Zucca E., Euclid, Collaboration, Ilić, S., Aghanim, N., Baccigalupi, C., Bermejo-Climent, J. R., Fabbian, G., Legrand, L., Paoletti, D., Ballardini, M., Archidiacono, M., Douspis, M., Finelli, F., Ganga, K., Hernández-Monteagudo, C., Lattanzi, M., Marinucci, D., Migliaccio, M., Carbone, C., Casas, S., Martinelli, M., Tutusaus, I., Natoli, P., Ntelis, P., Pagano, L., Wenzl, L., Gruppuso, A., Kitching, T., Langer, M., Mauri, N., Patrizii, L., Renzi, A., Sirri, G., Stanco, L., Tenti, M., Vielzeuf, P., Lacasa, F., Polenta, G., Yankelevich, V., Blanchard, A., Sakr, Z., Pourtsidou, A., Camera, S., Cardone, V. F., Kilbinger, M., Kunz, M., Markovic, K., Pettorino, V., Sánchez, A. G., Sapone, D., Amara, A., Auricchio, N., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Carretero, J., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Cropper, M., Da Silva, A., Degaudenzi, H., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Farrens, S., Fosalba, P., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kohley, R., Kubik, B., Kümmel, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lloro, I., Mansutti, O., Marggraf, O., Marulli, F., Massey, R., Maurogordato, S., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Morin, B., Moscardini, L., Munari, E., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., Scaramella, R., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Starck, J. L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., Valenziano, L., Verdoes Kleijn, G. A., Wang, Y., Welikala, N., Weller, J., Zamorani, G., Zoubian, J., Medinaceli, E., Mei, S., Rosset, C., Sureau, F., Vassallo, T., Zacchei, A., Andreon, S., Balaguera-Antolínez, A., Baldi, M., Bardelli, S., Biviano, A., Borgani, S., Bozzo, E., Burigana, C., Cabanac, R., Cappi, A., Carvalho, C. S., Castignani, G., Colodro-Conde, C., Coupon, J., Courtois, H. M., Cuby, J., de la Torre, S., Di Ferdinando, D., Dole, H., Farina, M., Ferreira, P. G., Flose-Reimberg, P., Galeotta, S., Gozaliasl, G., Graciá-Carpio, J., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maino, D., Martinet, N., Maturi, M., Metcalf, R. B., Morgante, G., Neissner, C., Nightingale, J., Nucita, A. A., Potter, D., Riccio, G., Romelli, E., Schirmer, M., Schultheis, M., Scottez, V., Teyssier, R., Tramacere, A., Valiviita, J., Viel, M., Whittaker, L., Zucca, E., Department of Physics, Research Program in Systems Oncology, Helsinki Institute of Physics, Ilic, S., Hernandez-Monteagudo, C., Sanchez, A. G., Kummel, M., Tallada-Crespi, P., Balaguera-Antolinez, A., De La Torre, S., Gracia-Carpio, J., Ili??, S., Hern??ndez-Monteagudo, C., S??nchez, A. G., K??mmel, M., Tallada-Cresp??, P., Balaguera-Antol??nez, A., and Graci??-Carpio, J.

Subjects
Cosmology and Nongalactic Astrophysics (astro-ph.CO), Cosmic microwave background, statistical [methods], FOS: Physical sciences, Astrophysics, cosmic background radiation, Astrophysics::Cosmology and Extragalactic Astrophysics, Joint analysis, kosmologia, 01 natural sciences, methods, NO, pimeä aine, [PHYS.ASTR.CO]Physics [physics]/Astrophysics [astro-ph]/Cosmology and Extra-Galactic Astrophysics [astro-ph.CO], mikroaallot, Settore FIS/05 - Astronomia e Astrofisica, surveys, 0103 physical sciences, tsz, survey, 010303 astronomy & astrophysics, Physics, methods: statistical, 010308 nuclear & particles physics, Computer Science::Information Retrieval, maailmankaikkeus, Astrophysics::Instrumentation and Methods for Astrophysics, Astronomy and Astrophysics, cross-correlation, 115 Astronomy, Space science, Cosmic background radiation, Large-scale structure of Universe, Methods: statistical, Surveys, kosminen taustasäteily, Space and Planetary Science, method, large-scale structure of Universe, pimeä energia, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], statistical, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The combination and cross-correlation of the upcoming $Euclid$ data with cosmic microwave background (CMB) measurements is a source of great expectation since it will provide the largest lever arm of epochs, ranging from recombination to structure formation across the entire past light cone. In this work, we present forecasts for the joint analysis of $Euclid$ and CMB data on the cosmological parameters of the standard cosmological model and some of its extensions. This work expands and complements the recently published forecasts based on $Euclid$-specific probes, namely galaxy clustering, weak lensing, and their cross-correlation. With some assumptions on the specifications of current and future CMB experiments, the predicted constraints are obtained from both a standard Fisher formalism and a posterior-fitting approach based on actual CMB data. Compared to a $Euclid$-only analysis, the addition of CMB data leads to a substantial impact on constraints for all cosmological parameters of the standard $\Lambda$-cold-dark-matter model, with improvements reaching up to a factor of ten. For the parameters of extended models, which include a redshift-dependent dark energy equation of state, non-zero curvature, and a phenomenological modification of gravity, improvements can be of the order of two to three, reaching higher than ten in some cases. The results highlight the crucial importance for cosmological constraints of the combination and cross-correlation of $Euclid$ probes with CMB data., Comment: 20 pages, 8 figures, 5 tables, 1 appendix; updated to match version accepted by journal

Published
2022
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48. Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models

Author

Euclid Collaboration, Bretonnière, H., Huertas-Company, M., Boucaud, A., Lanusse, F., Jullo, E., MERLIN, Emiliano, Tuccillo, D., CASTELLANO, MARCO, Brinchmann, J., Conselice, C. J., Poncet, M., Popa, L., POZZETTI, Lucia, Raison, F., Rebolo, R., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Dole, H., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Cabanac, R., Torradeflot, F., Valentijn, E. A., VALENZIANO, LUCA, Wang, Y., Welikala, N., Weller, J., Zamorani, G., Zoubian, J., Baldi, M., BARDELLI, Sandro, Courtois, H. M., Camera, S., FARINELLI, Ruben, Medinaceli, E., Mei, S., Polenta, G., Romelli, Erik, Tenti, M., Vassallo, T., ZACCHEI, Andrea, ZUCCA, Elena, Castander, F. J., Baccigalupi, C., Balaguera-Antolínez, A., BIVIANO, ANDREA, BORGANI, STEFANO, Bozzo, E., BURIGANA, CARLO, CAPPI, Alberto, Carvalho, C. S., Casas, S., Castignani, G., Duc, P. A., Colodro-Conde, C., Coupon, J., de la Torre, S., Fabricius, M., FARINA, Maria, Ferreira, P. G., Flose-Reimberg, P., Fotopoulou, S., GALEOTTA, Samuele, Ganga, K., Fosalba, P., Garcia-Bellido, J., Gaztanaga, E., Gozaliasl, G., Hook, I. M., Joachimi, B., Kansal, V., Kashlinsky, A., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Guinet, D., Mainetti, G., Maino, D., Maoli, R., Martinelli, M., Martinet, N., McCracken, H. J., Metcalf, R. B., MORGANTE, GIANLUCA, Morisset, N., Nightingale, J., Kruk, S., Nucita, A., Patrizii, L., Potter, D., Renzi, A., RICCIO, GIUSEPPE, Sánchez, A. G., Sapone, D., Schirmer, M., Schultheis, M., Scottez, V., Kuchner, U., SEFUSATTI, Emiliano, Teyssier, R., Tutusaus, I., Valiviita, J., VIEL, MATTEO, Whittaker, L., Knapen, J. H., Serrano, S., Soubrie, E., Tramacere, A., Wang, L., Amara, A., AURICCHIO, NATALIA, Bender, R., Bodendorf, C., BONINO, Donata, Branchini, Enzo, Brau-Nogue, S., BRESCIA, Massimo, Capobianco, Vito, CARBONE, Carmelita, Carretero, J., CAVUOTI, STEFANO, Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., CORCIONE, Leonardo, Costille, A., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., FRAILIS, Marco, FRANCESCHI, ENRICO, FUMANA, Marco, GARILLI, BIANCA MARIA ROSA, Gillard, W., Gillis, B., GIOCOLI, Carlo, GRAZIAN, Andrea, Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kümmel, M., Kunz, M., Kurki-Suonio, H., LIGORI, Sebastiano, Lilje, P. B., Lloro, I., MAIORANO, Elisabetta, MANSUTTI, Oriana, Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Melchior, M., MENEGHETTI, MASSIMO, Meylan, G., Moresco, M., Morin, B., Moscardini, L., Munari, Emiliano, Nakajima, R., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Astrophysique Interprétation Modélisation (AIM (UMR_7158 / UMR_E_9005 / UM_112)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Laboratoire de Cosmologie et Statistiques (LCS - COSMOSTAT), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Lanusse, Francois, UAM. Departamento de Física Teórica, Bretonniere, H., Huertas-Company, M., Boucaud, A., Lanusse, F., Jullo, E., Merlin, E., Tuccillo, D., Castellano, M., Brinchmann, J., Conselice, C. J., Dole, H., Cabanac, R., Courtois, H. M., Castander, F. J., Duc, P. A., Fosalba, P., Guinet, D., Kruk, S., Kuchner, U., Serrano, S., Soubrie, E., Tramacere, A., Wang, L., Amara, A., Auricchio, N., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brau-Nogue, S., Brescia, M., Capobianco, V., Carbone, C., Carretero, J., Cavuoti, S., Cimatti, A., Cledassou, R., Congedo, G., Conversi, L., Copin, Y., Corcione, L., Costille, A., Cropper, M., Da Silva, A., Degaudenzi, H., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hormuth, F., Hudelot, P., Jahnke, K., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kummel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Marulli, F., Massey, R., Maurogordato, S., Melchior, M., Meneghetti, M., Meylan, G., Moresco, M., Morin, B., Moscardini, L., Munari, E., Nakajima, R., Niemi, S. M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Poncet, M., Popa, L., Pozzetti, L., Raison, F., Rebolo, R., Rhodes, J., Roncarelli, M., Rossetti, E., Saglia, R., Schneider, P., Secroun, A., Seidel, G., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespi, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., Valenziano, L., Wang, Y., Welikala, N., Weller, J., Zamorani, G., Zoubian, J., Baldi, M., Bardelli, S., Camera, S., Farinelli, R., Medinaceli, E., Mei, S., Polenta, G., Romelli, E., Tenti, M., Vassallo, T., Zacchei, A., Zucca, E., Baccigalupi, C., Balaguera-Antolinez, A., Biviano, A., Borgani, S., Bozzo, E., Burigana, C., Cappi, A., Carvalho, C. S., Casas, S., Castignani, G., Colodro-Conde, C., Coupon, J., De La Torre, S., Fabricius, M., Farina, M., Ferreira, P. G., Flose-Reimberg, P., Fotopoulou, S., Galeotta, S., Ganga, K., Garcia-Bellido, J., Gaztanaga, E., Gozaliasl, G., Hook, I. M., Joachimi, B., Kansal, V., Kashlinsky, A., Keihanen, E., Kirkpatrick, C. C., Lindholm, V., Mainetti, G., Maino, D., Maoli, R., Martinelli, M., Martinet, N., Mccracken, H. J., Metcalf, R. B., Morgante, G., Morisset, N., Nightingale, J., Nucita, A., Patrizii, L., Potter, D., Renzi, A., Riccio, G., Sanchez, A. G., Sapone, D., Schirmer, M., Schultheis, M., Scottez, V., Sefusatti, E., Teyssier, R., Tutusaus, I., Valiviita, J., Viel, M., Whittaker, L., Knapen, J. H., Bretonniere H., Huertas-Company M., Boucaud A., Lanusse F., Jullo E., Merlin E., Tuccillo D., Castellano M., Brinchmann J., Conselice C.J., Dole H., Cabanac R., Courtois H.M., Castander F.J., Duc P.A., Fosalba P., Guinet D., Kruk S., Kuchner U., Serrano S., Soubrie E., Tramacere A., Wang L., Amara A., Auricchio N., Bender R., Bodendorf C., Bonino D., Branchini E., Brau-Nogue S., Brescia M., Capobianco V., Carbone C., Carretero J., Cavuoti S., Cimatti A., Cledassou R., Congedo G., Conversi L., Copin Y., Corcione L., Costille A., Cropper M., Da Silva A., Degaudenzi H., Douspis M., Dubath F., Duncan C.A.J., Dupac X., Dusini S., Farrens S., Ferriol S., Frailis M., Franceschi E., Fumana M., Garilli B., Gillard W., Gillis B., Giocoli C., Grazian A., Grupp F., Haugan S.V.H., Holmes W., Hormuth F., Hudelot P., Jahnke K., Kermiche S., Kiessling A., Kilbinger M., Kitching T., Kohley R., Kummel M., Kunz M., Kurki-Suonio H., Ligori S., Lilje P.B., Lloro I., Maiorano E., Mansutti O., Marggraf O., Markovic K., Marulli F., Massey R., Maurogordato S., Melchior M., Meneghetti M., Meylan G., Moresco M., Morin B., Moscardini L., Munari E., Nakajima R., Niemi S.M., Padilla C., Paltani S., Pasian F., Pedersen K., Pettorino V., Pires S., Poncet M., Popa L., Pozzetti L., Raison F., Rebolo R., Rhodes J., Roncarelli M., Rossetti E., Saglia R., Schneider P., Secroun A., Seidel G., Sirignano C., Sirri G., Stanco L., Starck J.-L., Tallada-Crespi P., Taylor A.N., Tereno I., Toledo-Moreo R., Torradeflot F., Valentijn E.A., Valenziano L., Wang Y., Welikala N., Weller J., Zamorani G., Zoubian J., Baldi M., Bardelli S., Camera S., Farinelli R., Medinaceli E., Mei S., Polenta G., Romelli E., Tenti M., Vassallo T., Zacchei A., Zucca E., Baccigalupi C., Balaguera-Antolinez A., Biviano A., Borgani S., Bozzo E., Burigana C., Cappi A., Carvalho C.S., Casas S., Castignani G., Colodro-Conde C., Coupon J., De La Torre S., Fabricius M., Farina M., Ferreira P.G., Flose-Reimberg P., Fotopoulou S., Galeotta S., Ganga K., Garcia-Bellido J., Gaztanaga E., Gozaliasl G., Hook I.M., Joachimi B., Kansal V., Kashlinsky A., Keihanen E., Kirkpatrick C.C., Lindholm V., Mainetti G., Maino D., Maoli R., Martinelli M., Martinet N., McCracken H.J., Metcalf R.B., Morgante G., Morisset N., Nightingale J., Nucita A., Patrizii L., Potter D., Renzi A., Riccio G., Sanchez A.G., Sapone D., Schirmer M., Schultheis M., Scottez V., Sefusatti E., Teyssier R., Tutusaus I., Valiviita J., Viel M., Whittaker L., Knapen J.H., Astronomy, Bretonniere, H, Huertas-Company, M, Boucaud, A, Lanusse, F, Jullo, E, Merlin, E, Tuccillo, D, Castellano, M, Brinchmann, J, Conselice, Cj, Dole, H, Cabanac, R, Courtois, Hm, Castander, Fj, Duc, Pa, Fosalba, P, Guinet, D, Kruk, S, Kuchner, U, Serrano, S, Soubrie, E, Tramacere, A, Wang, L, Amara, A, Auricchio, N, Bender, R, Bodendorf, C, Bonino, D, Branchini, E, Brau-Nogue, S, Brescia, M, Capobianco, V, Carbone, C, Carretero, J, Cavuoti, S, Cimatti, A, Cledassou, R, Congedo, G, Conversi, L, Copin, Y, Corcione, L, Costille, A, Cropper, M, Da Silva, A, Degaudenzi, H, Douspis, M, Dubath, F, Duncan, Caj, Dupac, X, Dusini, S, Farrens, S, Ferriol, S, Frailis, M, Franceschi, E, Fumana, M, Garilli, B, Gillard, W, Gillis, B, Giocoli, C, Grazian, A, Grupp, F, Haugan, Svh, Holmes, W, Hormuth, F, Hudelot, P, Jahnke, K, Kermiche, S, Kiessling, A, Kilbinger, M, Kitching, T, Kohley, R, Kummel, M, Kunz, M, Kurki-Suonio, H, Ligori, S, Lilje, Pb, Lloro, I, Maiorano, E, Mansutti, O, Marggraf, O, Markovic, K, Marulli, F, Massey, R, Maurogordato, S, Melchior, M, Meneghetti, M, Meylan, G, Moresco, M, Morin, B, Moscardini, L, Munari, E, Nakajima, R, Niemi, Sm, Padilla, C, Paltani, S, Pasian, F, Pedersen, K, Pettorino, V, Pires, S, Poncet, M, Popa, L, Pozzetti, L, Raison, F, Rebolo, R, Rhodes, J, Roncarelli, M, Rossetti, E, Saglia, R, Schneider, P, Secroun, A, Seidel, G, Sirignano, C, Sirri, G, Stanco, L, Starck, Jl, Tallada-Crespi, P, Taylor, An, Tereno, I, Toledo-Moreo, R, Torradeflot, F, Valentijn, Ea, Valenziano, L, Wang, Y, Welikala, N, Weller, J, Zamorani, G, Zoubian, J, Baldi, M, Bardelli, S, Camera, S, Farinelli, R, Medinaceli, E, Mei, S, Polenta, G, Romelli, E, Tenti, M, Vassallo, T, Zacchei, A, Zucca, E, Baccigalupi, C, Balaguera-Antolinez, A, Biviano, A, Borgani, S, Bozzo, E, Burigana, C, Cappi, A, Carvalho, C, Casas, S, Castignani, G, Colodro-Conde, C, Coupon, J, de la Torre, S, Fabricius, M, Farina, M, Ferreira, Pg, Flose-Reimberg, P, Fotopoulou, S, Galeotta, S, Ganga, K, Garcia-Bellido, J, Gaztanaga, E, Gozaliasl, G, Hook, Im, Joachimi, B, Kansal, V, Kashlinsky, A, Keihanen, E, Kirkpatrick, Cc, Lindholm, V, Mainetti, G, Maino, D, Maoli, R, Martinelli, M, Martinet, N, Mccracken, Hj, Metcalf, Rb, Morgante, G, Morisset, N, Nightingale, J, Nucita, A, Patrizii, L, Potter, D, Renzi, A, Riccio, G, Sanchez, Ag, Sapone, D, Schirmer, M, Schultheis, M, Scottez, V, Sefusatti, E, Teyssier, R, Tutusaus, I, Valiviita, J, Viel, M, Whittaker, L, Knapen, Jh, Department of Physics, Research Program in Systems Oncology, and Helsinki Institute of Physics

Subjects
INFORMATION, structure [Galaxies], FOS: Physical sciences, Techniques: image processing, Morphology (biology), observation [Cosmology], Astrophysics, Astrophysics::Cosmology and Extragalactic Astrophysics, Surveys, Cosmology: observation, 01 natural sciences, Settore FIS/05 - Astronomia e Astrofisica, DEPENDENCE, Galaxies: structure, galaxies, evolution, 0103 physical sciences, image processing [Techniques], structure, Survey, 010303 astronomy & astrophysics, Astrophysics::Galaxy Astrophysics, COSMOS, Physics, 010308 nuclear & particles physics, galaxie, Cosmology: observations, [PHYS.ASTR.GA] Physics [physics]/Astrophysics [astro-ph]/Galactic Astrophysics [astro-ph.GA], Galaxies: evolution, Física, Astronomy and Astrophysics, technique, 115 Astronomy, Space science, evolution [Galaxies], Astrophysics - Astrophysics of Galaxies, techniques, image processing, surveys, cosmology, observations, Galaxy, [PHYS.ASTR.GA]Physics [physics]/Astrophysics [astro-ph]/Galactic Astrophysics [astro-ph.GA], Space and Planetary Science, Astrophysics of Galaxies (astro-ph.GA), SIMULATION, Generative grammar
Abstract

Artículo escrito por un elevado número de autores, sólo se referencian el que aparece en primer lugar, los autores pertenecientes a la UAM y el nombre del grupo de colaboración, si lo hubiere., We present a machine learning framework to simulate realistic galaxies for the Euclid Survey, producing more complex and realistic galaxies than the analytical simulations currently used in Euclid. The proposed method combines a control on galaxy shape parameters offered by analytic models with realistic surface brightness distributions learned from real Hubble Space Telescope observations by deep generative models. We simulate a galaxy field of 0.4 deg2 as it will be seen by the Euclid visible imager VIS, and we show that galaxy structural parameters are recovered to an accuracy similar to that for pure analytic Sérsic profiles. Based on these simulations, we estimate that the Euclid Wide Survey (EWS) will be able to resolve the internal morphological structure of galaxies down to a surface brightness of 22.5 mag arcsec-2, and the Euclid Deep Survey (EDS) down to 24.9 mag arcsec-2. This corresponds to approximately 250 million galaxies at the end of the mission and a 50% complete sample for stellar masses above 1010.6 M (resp. 109.6 M) at a redshift z ∼ 0.5 for the EWS (resp. EDS). The approach presented in this work can contribute to improving the preparation of future high-precision cosmological imaging surveys by allowing simulations to incorporate more realistic galaxies

Published
2021

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