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SWEET-Cat (Stars With ExoplanETs Catalogue) was originally introduced in 2013, and since then, the number of confirmed exoplanets has increased significantly. A crucial step for a comprehensive understanding of these new worlds is the precise and homogeneous characterization of their host stars. We used a large number of high-resolution spectra to continue the addition of new stellar parameters for planet-host stars in SWEET-Cat following the new detection of exoplanets listed both at the Extrasolar Planets Encyclopedia and at the NASA exoplanet archive. We obtained high-resolution spectra for a significant number of these planet-host stars, either observed by our team or collected through public archives. For FGK stars, the spectroscopic stellar parameters were derived for the spectra following the same homogeneous process using ARES+MOOG as for the previous SWEET-Cat releases. The stellar properties are combined with the planet properties to study possible correlations that could shed more light into the star-planet connection studies. We increase the number of stars with homogeneous parameters by 232 ($\sim$ 25\% - from 959 to 1191). We then focus on the exoplanets with both mass and radius determined to review the mass-radius relation where we find consistent results with the ones previously reported in the literature. For the massive planets we also revisit the radius anomaly where we confirm a metallicity correlation for the radius anomaly already hinted in previous results., Comment: 8 pages, 5 figures, Accepted for A&A

Software services are increasingly migrating to the cloud, requiring trust in actors with direct access to the hardware, software and data comprising the service. A distributed datastore storing critical data sits at the core of many services; a prime example being etcd in Kubernetes. Trusted execution environments can secure this data from cloud providers during execution, but it is complex to build trustworthy data storage systems using such mechanisms. We present the design and evaluation of the Ledger-backed Secure Key-Value datastore (LSKV), a distributed datastore that provides an etcd-like API but can use trusted execution mechanisms to keep cloud providers outside the trust boundary. LSKV provides a path to transition traditional systems towards confidential execution, provides competitive performance compared to etcd, and helps clients to gain trust in intermediary services. LSKV forms a foundational core, lowering the barriers to building more trustworthy systems.

Application tail latency is a key metric for many services, with high latencies being linked directly to loss of revenue. Modern deeply-nested micro-service architectures exacerbate tail latencies, increasing the likelihood of users experiencing them. In this work, we show how CPU overcommitment by OS threads leads to high tail latencies when applications are under heavy load. CPU overcommitment can arise from two operational factors: incorrectly determining the number of CPUs available when under a CPU quota, and the ignorance of neighbour applications and their CPU usage. We discuss different languages' solutions to obtaining the CPUs available, evaluating the impact, and discuss opportunities for a more unified language-independent interface to obtain the number of CPUs available. We then evaluate the impact of neighbour usage on tail latency and introduce a new neighbour-aware threadpool, the friendlypool, that dynamically avoids overcommitment. In our evaluation, the friendlypool reduces maximum worker latency by up to $6.7\times$ at the cost of decreasing throughput by up to $1.4\times$.

The rapid adoption of large language models (LLMs) has led to significant advances in natural language processing and text generation. However, the energy consumed through LLM model inference remains a major challenge for sustainable AI deployment. To address this problem, we model the workload-dependent energy consumption and runtime of LLM inference tasks on heterogeneous GPU-CPU systems. By conducting an extensive characterization study of several state-of-the-art LLMs and analyzing their energy and runtime behavior across different magnitudes of input prompts and output text, we develop accurate (R^2>0.96) energy and runtime models for each LLM. We employ these models to explore an offline, energy-optimal LLM workload scheduling framework. Through a case study, we demonstrate the advantages of energy and accuracy aware scheduling compared to existing best practices., Comment: 7 pages, appearing at HotCarbon 2024

This work focuses on the analysis of the mean longitudinal magnetic field as a stellar activity tracer in the context of small exoplanet detection and characterisation in radial-velocity (RV) surveys. We use SDO/HMI filtergrams to derive Sun-as-a-star magnetic field measurements, and show that the mean longitudinal magnetic field is an excellent rotational period detector and a useful tracer of the solar magnetic cycle. To put these results into context, we compare the mean longitudinal magnetic field to three common activity proxies derived from HARPS-N Sun-as-a-star data: the full-width at half-maximum, the bisector span and the S-index. The mean longitudinal magnetic field does not correlate with the RVs and therefore cannot be used as a one-to-one proxy. However, with high cadence and a long baseline, the mean longitudinal magnetic field outperforms all other considered proxies as a solar rotational period detector, and can be used to inform our understanding of the physical processes happening on the surface of the Sun. We also test the mean longitudinal magnetic field as a "stellar proxy" on a reduced solar dataset to simulate stellar-like observational sampling. With a Gaussian Process regression analysis, we confirm that the solar mean longitudinal magnetic field is the most effective of the considered indicators, and is the most efficient rotational period indicator over different levels of stellar activity. This work highlights the need for polarimetric time series observations of stars., Comment: 23 pages, 27 figures

As global fertilizer application rates increase, high-quality datasets are paramount for comprehensive analyses to support informed decision-making and policy formulation in crucial areas such as food security or climate change. This study aims to fill existing data gaps by employing two machine learning models, eXtreme Gradient Boosting and HistGradientBoosting algorithms to produce precise country-level predictions of nitrogen ($N$), phosphorus pentoxide ($P_2O_5$), and potassium oxide ($K_2O$) application rates. Subsequently, we created a comprehensive dataset of 5-arcmin resolution maps depicting the application rates of each fertilizer for 13 major crop groups from 1961 to 2019. The predictions were validated by both comparing with existing databases and by assessing the drivers of fertilizer application rates using the model's SHapley Additive exPlanations. This extensive dataset is poised to be a valuable resource for assessing fertilization trends, identifying the socioeconomic, agricultural, and environmental drivers of fertilizer application rates, and serving as an input for various applications, including environmental modeling, causal analysis, fertilizer price predictions, and forecasting., Comment: 47 pages, 7 figures, 8 tables

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

Planets orbiting binary systems are relatively unexplored compared to those around single stars. Detections of circumbinary planets and planetary systems offer a first detailed view into our understanding of circumbinary planet formation and dynamical evolution. The BEBOP (Binaries Escorted by Orbiting Planets) radial velocity survey plays a special role in this adventure as it focuses on eclipsing single-lined binaries with an FGK dwarf primary and M dwarf secondary allowing for the highest-radial velocity precision using the HARPS and SOPHIE spectrographs. We obtained 4512 high-resolution spectra for the 179 targets in the BEBOP survey which we used to derive the stellar atmospheric parameters using both equivalent widths and spectral synthesis. We furthermore derive stellar masses, radii, and ages for all targets. With this work, we present the first homogeneous catalogue of precise stellar parameters for these eclipsing single-lined binaries., Comment: accepted for publication in MNRAS

We report on the discovery of Gliese 12 b, the nearest transiting temperate, Earth-sized planet found to date. Gliese 12 is a bright ($V=12.6$ mag, $K=7.8$ mag) metal-poor M4V star only $12.162\pm0.005$ pc away from the Solar System with one of the lowest stellar activity levels known for an M-dwarf. A planet candidate was detected by TESS based on only 3 transits in sectors 42, 43, and 57, with an ambiguity in the orbital period due to observational gaps. We performed follow-up transit observations with CHEOPS and ground-based photometry with MINERVA-Australis, SPECULOOS, and Purple Mountain Observatory, as well as further TESS observations in sector 70. We statistically validate Gliese 12 b as a planet with an orbital period of $12.76144\pm0.00006$ days and a radius of $1.0\pm{0.1}$ R$_\oplus$, resulting in an equilibrium temperature of $\sim$315K. Gliese 12 b has excellent future prospects for precise mass measurement, which may inform how planetary internal structure is affected by the stellar compositional environment. Gliese 12 b also represents one of the best targets to study whether Earth-like planets orbiting cool stars can retain their atmospheres, a crucial step to advance our understanding of habitability on Earth and across the Galaxy., Comment: 19 pages, 7 figures, Accepted for publication in MNRAS, Authors Shishir Dholakia and Larissa Palethorpe contributed equally

Stellar magnetic activity induces both distortions and Doppler-shifts in the absorption line profiles of Sun-like stars. Those effects produce apparent radial velocity (RV) signals which greatly hamper the search for potentially habitable, Earth-like planets. In this work, we investigate these distortions in the Sun using cross-correlation functions (CCFs), derived from intensive monitoring with the high-precision spectrograph HARPS-N. We show that the RV signal arising from line-shape variations on time-scales associated with the solar rotation and activity cycle can be robustly extracted from the data, reducing the RV dispersion by half. Once these have been corrected, activity-induced Doppler-shifts remain, that are modulated at the solar rotation period, and that are most effectively modelled in the time domain, using Gaussian Processes (GPs). Planet signatures are still best retrieved with multi-dimensonal GPs, when activity is jointly modelled from the raw RVs and indicators of the line width or of the Ca II H and K emission. After GP modelling, the residual RVs exhibit a dispersion of 0.6-0.8 m/s, likely to be dominated by signals induced by super-granulation. Finally, we find that the statistical properties of the RVs evolve significantly over time, and that this evolution is primarily driven by sunspots, which control the smoothness of the signal. Such evolution, which reduces the sensitivity to long-period planet signatures, is no longer seen in the activity-induced Doppler-shifts, which is promising for long term RV monitoring surveys such as the Terra Hunting Experiment or the PLATO follow-up campaign., Comment: Accepted by MNRAS on the 2024 May 20. 17 pages, 14 figures (plus 8 pages of Appendix, 9 Appendix figures)

We develop a Hamiltonian formalism for simulating interacting chiral fermions on the lattice while preserving unitarity and locality and without breaking the chiral symmetry. The fermion doubling problem is circumvented by constructing a Fock space endowed with a semi-definite norm. When projecting our theory on the the single-particle sector, we recover the framework of Stacey fermions, and we demonstrate that the scaling limit of the free model recovers the chiral fermion field. Technically, we make use of a matrix product operator norm to mimick the boundary of a higher dimensional topological theory. As a proof of principle, we consider a single Weyl fermion on a periodic ring with Hubbard-type nearest-neighbor interactions and construct a variational generalized DMRG code to demonstrate that the ground state for large system sizes can be determined efficiently. As our tensor network approach does not exhibit any sign problem, we can add a chemical potential and study real-time evolution.

In this article, we develop a new class of multivariate distributions adapted for count data, called Tree P{\'o}lya Splitting. This class results from the combination of a univariate distribution and singular multivariate distributions along a fixed partition tree. As we will demonstrate, these distributions are flexible, allowing for the modeling of complex dependencies (positive, negative, or null) at the observation level. Specifically, we present the theoretical properties of Tree P{\'o}lya Splitting distributions by focusing primarily on marginal distributions, factorial moments, and dependency structures (covariance and correlations). The abundance of 17 species of Trichoptera recorded at 49 sites is used, on one hand, to illustrate the theoretical properties developed in this article on a concrete case, and on the other hand, to demonstrate the interest of this type of models, notably by comparing them to classical approaches in ecology or microbiome.

Both the training and use of Large Language Models (LLMs) require large amounts of energy. Their increasing popularity, therefore, raises critical concerns regarding the energy efficiency and sustainability of data centers that host them. This paper addresses the challenge of reducing energy consumption in data centers running LLMs. We propose a hybrid data center model that uses a cost-based scheduling framework to dynamically allocate LLM tasks across hardware accelerators that differ in their energy efficiencies and computational capabilities. Specifically, our workload-aware strategy determines whether tasks are processed on energy-efficient processors or high-performance GPUs based on the number of input and output tokens in a query. Our analysis of a representative LLM dataset, finds that this hybrid strategy can reduce CPU+GPU energy consumption by 7.5% compared to a workload-unaware baseline.

We show how fermionic statistics can be naturally incorporated in tensor networks on arbitrary graphs through the use of graded Hilbert spaces. This formalism allows to use tensor network methods for fermionic lattice systems in a local way, avoiding the need of a Jordan-Wigner transformation or the explicit tracking of leg crossings by swap gates in 2D tensor networks. The graded Hilbert spaces can be readily integrated with other internal and lattice symmetries in tensor networks, and only require minor extensions to an existing tensor network software package. We review and benchmark the fermionic versions of common algorithms for matrix product states and projected entangled-pair states., Comment: 78 pages, 6 figures

We present an exhaustive photometric and spectroscopic analysis of TOI-837, a F9/G0 35 Myr young star, hosting a transiting exoplanet, TOI-837b, with an orbital period of 8.32d. Utilising data from TESS and ground-based observations, we determine a planetary radius of 0.82 R_J for TOI-837b. Through detailed HARPS spectroscopic time series analysis, we derive a Doppler semi-amplitude of 35 m/s, corresponding to a planetary mass of 0.39 M_J. The derived planetary properties suggest a substantial core of approximately 70 M_E, constituting about 60% of the planet's total mass. This finding poses a significant challenge to existing theoretical models of core formation. We propose that future atmospheric observations with JWST could provide insights into resolving ambiguities of TOI-837b, offering new perspectives on its composition, formation, and evolution., Comment: MNRAS published version, MNRAS, 531, 4275

We report on the detailed characterization of the HD 77946 planetary system. HD 77946 is an F5 ($M_*$ = 1.17 M$_{\odot}$, $R_*$ = 1.31 R$_{\odot}$) star, which hosts a transiting planet recently discovered by NASA's Transiting Exoplanet Survey Satellite (TESS), classified as TOI-1778 b. Using TESS photometry, high-resolution spectroscopic data from HARPS-N, and photometry from CHEOPS, we measure the radius and mass from the transit and RV observations, and find that the planet, HD 77946 b, orbits with period $P_{\rm b}$ = $6.527282_{-0.000020}^{+0.000015}$ d, has a mass of $M_{\rm b} = 8.38\pm{1.32}$M$_\oplus$, and a radius of $R_{\rm b} = 2.705_{-0.081}^{+0.086}$R$_\oplus$. From the combination of mass and radius measurements, and the stellar chemical composition, the planet properties suggest that HD 77946 b is a sub-Neptune with a $\sim$1\% H/He atmosphere. However, a degeneracy still exists between water-world and silicate/iron-hydrogen models, and even though interior structure modelling of this planet favours a sub-Neptune with a H/He layer that makes up a significant fraction of its radius, a water-world composition cannot be ruled out, as with $T_{\rm eq} = 1248^{+40}_{-38}~$K, water may be in a supercritical state. The characterisation of HD 77946 b, adding to the small sample of well-characterised sub-Neptunes, is an important step forwards on our journey to understanding planetary formation and evolution pathways. Furthermore, HD 77946 b has one of the highest transmission spectroscopic metrics for small planets orbiting hot stars, thus transmission spectroscopy of this key planet could prove vital for constraining the compositional confusion that currently surrounds small exoplanets., Comment: 19 pages, 13 figures, 5 tables. Published in MNRAS

The extreme environments of ultra-short-period planets (USPs) make excellent laboratories to study how exoplanets obtain, lose, retain, and/or regain gaseous atmospheres. We present the confirmation and characterization of the USP TOI-1347 b, a $1.8 \pm 0.1$ R$_\oplus$ planet on a 0.85 day orbit that was detected with photometry from the TESS mission. We measured radial velocities of the TOI-1347 system using Keck/HIRES and HARPS-N and found the USP to be unusually massive at $11.1 \pm 1.2$ M$_\oplus$. The measured mass and radius of TOI-1347 b imply an Earth-like bulk composition. A thin H/He envelope (>0.01% by mass) can be ruled out at high confidence. The system is between 1 and 1.8 Gyr old; therefore, intensive photoevaporation should have concluded. We detected a tentative phase curve variation (3$\sigma$) and a secondary eclipse (2$\sigma$) in TESS photometry, which if confirmed could indicate the presence of a high-mean-molecular-weight atmosphere. We recommend additional optical and infrared observations to confirm the presence of an atmosphere and investigate its composition., Comment: 22 pages, 9 figures, accepted for publication in The Astronomical Journal

We report precise radial velocity (RV) observations of HD 212657 (= K2-167), a star shown by K2 to host a transiting sub-Neptune-sized planet in a 10 day orbit. Using Transiting Exoplanet Survey Satellite (TESS) photometry, we refined the planet parameters, especially the orbital period. We collected 74 precise RVs with the HARPS-N spectrograph between August 2015 and October 2016. Although this planet was first found to transit in 2015 and validated in 2018, excess RV scatter originally limited mass measurements. Here, we measure a mass by taking advantage of reductions in scatter from updates to the HARPS-N Data Reduction System (2.3.5) and our new activity mitigation method called CCF Activity Linear Model (CALM), which uses activity-induced line shape changes in the spectra without requiring timing information. Using the CALM framework, we performed a joint fit with RVs and transits using EXOFASTv2 and find $M_p = 6.3_{-1.4}^{+1.4}$ $M_{\oplus}$ and $R_p = 2.33^{+0.17}_{-0.15}$ $R_{\oplus}$, which places K2-167 b at the upper edge of the radius valley. We also find hints of a secondary companion at a $\sim$ 22 day period, but confirmation requires additional RVs. Although characterizing lower-mass planets like K2-167 b is often impeded by stellar variability, these systems especially help probe the formation physics (i.e. photoevaporation, core-powered mass loss) of the radius valley. In the future, CALM or similar techniques could be widely applied to FGK-type stars, help characterize a population of exoplanets surrounding the radius valley, and further our understanding of their formation., Comment: 20 pages, 16 figures, accepted for publication in MNRAS

Although instruments for measuring the radial velocities (RVs) of stars now routinely reach sub-meter per second accuracy, the detection of low-mass planets is still very challenging. The rotational modulation and evolution of spots and/or faculae can induce variations in the RVs at the level of a few m/s in Sun-like stars. To overcome this, a multi-dimensional Gaussian Process framework has been developed to model the stellar activity signal using spectroscopic activity indicators together with the RVs. A recently published computationally efficient implementation of this framework, S+LEAF 2, enables the rapid analysis of large samples of targets with sizeable data sets. In this work, we apply this framework to HARPS observations of 268 well-observed targets with precisely determined stellar parameters. Our long-term goal is to quantify the effectiveness of this framework to model and mitigate activity signals for stars of different spectral types and activity levels. In this first paper in the series, we initially focus on the activity indicators (S-index and Bisector Inverse Slope), and use them to a) measure rotation periods for 49 slow rotators in our sample, b) explore the impact of these results on the spin-down of middle-aged late F, G & K stars, and c) explore indirectly how the spot to facular ratio varies across our sample. Our results should provide valuable clues for planning future RV planet surveys such as the Terra Hunting Experiment or the PLATO ground-based follow-up observations program, and help fine-tune current stellar structure and evolution models., Comment: Accepted for publication in MNRAS

Global financial crime activity is driving demand for machine learning solutions in fraud prevention. However, prevention systems are commonly serviced to financial institutions in isolation, and few provisions exist for data sharing due to fears of unintentional leaks and adversarial attacks. Collaborative learning advances in finance are rare, and it is hard to find real-world insights derived from privacy-preserving data processing systems. In this paper, we present a collaborative deep learning framework for fraud prevention, designed from a privacy standpoint, and awarded at the recent PETs Prize Challenges. We leverage latent embedded representations of varied-length transaction sequences, along with local differential privacy, in order to construct a data release mechanism which can securely inform externally hosted fraud and anomaly detection models. We assess our contribution on two distributed data sets donated by large payment networks, and demonstrate robustness to popular inference-time attacks, along with utility-privacy trade-offs analogous to published work in alternative application domains.