arXiv subjects

G. Leroy

Publications and source records attributed to G. Leroy.

5 recordsLinked to original sources

arXiv2026

COSMOS-Web: From early star-formation enhancement to late suppression in galaxy groups

Galaxy groups trace dense environments where interactions, gas removal, and reduced accretion may drive quenching. Common diagnostics trace star formation over short timescales ($\lesssim 100$ Myr), so time-resolved star formation histories (SFHs) are needed to separate brief changes from longer-term evolution at fixed mass and redshift. Using COSMOS-Web data, we test how group environment correlates with star formation, how this evolves with cosmic time and group-centric distance, and how high-richness group galaxies differ from field galaxies. We combine COSMOS2025/COSMOS-Web stellar masses and non-parametric SFHs with AMICO group detections and probabilistic memberships. Using stacked SFHs and evolution diagnostics, we compare group and matched field galaxies as a function of normalized group-centric distance ($R_{\rm norm}$), using the richest groups as reference. The clearest suppression appears at $z<1.5$ and low-to-intermediate mass ($8.1<\log(M_\star/M_\odot)<10.5$), reaching a group-field SFH deficit up to 0.8 dex. At $z>1.5$, SFHs show weak suppression or occasional enhancement, a more heterogeneous contrast despite possible systematics. The radial signal also evolves: low-redshift profiles are broadly quenching-oriented across radius, while a clear inner-outer contrast emerges at $z\gtrsim 1$, though ordering at $z\gtrsim 2$ remains tentative given growing uncertainty in AMICO centroids. These results suggest an evolving picture: at early epochs groups are more mixed, with both suppressed and elevated SFHs; from $z\lesssim 1.5$, suppression dominates, most clearly for low-to-intermediate-mass galaxies. This fits inner-region galaxies spending more time within the group potential, undergoing more passages through dense intra-group regions, and receiving less pristine cold gas, making quenching progressively clearer with cosmic time.

astro-ph.GA↗

arXiv2026

Multi-scale weak lensing detection of galaxy clusters with source redshift tomography

Recently, a number of methods have emerged to detect galaxy clusters solely through their weak lensing signal. Using the recently-introduced wavelet multi-scale detection method, we focus here on the potential for the use of tomographic information of the source galaxies to increase the number of weak lensing detections. We apply the $z_{s,\mathrm{min}}$-cut technique, consisting of the combination of weak lensing peak detections emerging from lensing maps obtained using different source redshift bins, to mock data sets of progressively increasing sophistication. The source redshift distribution is chosen to be $Euclid$-like, with a maximum depth of $z_{s,\mathrm{max}}=3$, and overlapping tomographic redshift bins are constructed by progressively increasing the minimum source redshift $z_{s,\mathrm{min}}$. Considering all possible detection combinations from one to four tomographic bins, we find that a single source redshift bin, with $z_{s,\mathrm{min}}=0.4$, performs as well as the combination of multiple redshift bins. By running detections on synthetic clusters of varying complexity -- from isolated Navarro Frenk White haloes to haloes embedded in and formed within N-body cosmological simulations, and considering both true and photometric source redshifts -- we show that while large-scale structure contamination and photometric redshift errors reduce the potential gains of the tomographic approach, the dominant limitation is the accumulation of spurious detections across redshift bins, leading to decreased purity at a fixed detection threshold.

astro-ph.CO↗

arXiv2025

Euclid Quick Data Release (Q1): VIS processing and data products

This paper describes the VIS Processing Function (VIS PF) of the Euclid ground segment pipeline, which processes and calibrates raw data from the VIS camera. We present the algorithms used in each processing element, along with a description of the on-orbit performance of VIS PF, based on Performance Verification (PV) and Q1 data. We demonstrate that the principal performance metrics (image quality, astrometric accuracy, photometric calibration) are within pre-launch specifications. The image-to-image photometric scatter is less than $0.8\%$, and absolute astrometric accuracy compared to Gaia is $5$ mas Image quality is stable over all Q1 images with a full width at half maximum (FWHM) of $0.\!^{\prime\prime}16$. The stacked images (combining four nominal and two short exposures) reach $I_\mathrm{E} = 25.6$ ($10\sigma$, measured as the variance of $1.\!^{\prime\prime}3$ diameter apertures). We also describe quality control metrics provided with each image, and an appendix provides a detailed description of the provided data products. The excellent quality of these images demonstrates the immense potential of Euclid VIS data for weak lensing. VIS data, covering most of the extragalactic sky, will provide a lasting high-resolution atlas of the Universe.

astro-ph.IM↗

arXiv2024

Crowdsourcing with Enhanced Data Quality Assurance: An Efficient Approach to Mitigate Resource Scarcity Challenges in Training Large Language Models for Healthcare

Large Language Models (LLMs) have demonstrated immense potential in artificial intelligence across various domains, including healthcare. However, their efficacy is hindered by the need for high-quality labeled data, which is often expensive and time-consuming to create, particularly in low-resource domains like healthcare. To address these challenges, we propose a crowdsourcing (CS) framework enriched with quality control measures at the pre-, real-time-, and post-data gathering stages. Our study evaluated the effectiveness of enhancing data quality through its impact on LLMs (Bio-BERT) for predicting autism-related symptoms. The results show that real-time quality control improves data quality by 19 percent compared to pre-quality control. Fine-tuning Bio-BERT using crowdsourced data generally increased recall compared to the Bio-BERT baseline but lowered precision. Our findings highlighted the potential of crowdsourcing and quality control in resource-constrained environments and offered insights into optimizing healthcare LLMs for informed decision-making and improved patient care.

cs.CL↗

arXiv2023

Fast multiscale galaxy cluster detection with weak lensing: towards a mass-selected sample

The sensitivity and wide area reached by ongoing and future wide-field optical surveys allows for the detection of an increasing number of galaxy clusters uniquely through their weak lensing (WL) signal. This motivates the development of new methods to analyse the unprecedented volume of data faster and more efficiently. Here we introduce a new multi-scale WL detection method based on application of wavelet filters to the convergence maps. We compare our results to those obtained from four commonly-used single scale approaches based on the application of aperture mass filters to the shear in real and Fourier space. The method is validated on Euclid-like mocks from the DUSTGRAIN-pathfinder simulations. We introduce a new matching procedure that takes into account the theoretical signal-to-noise of detection by WL and the filter size. We perform a complete analysis of the filters, and a comparison of the purity and the completeness of the resulting detected catalogues. We show that equivalent results are obtained when the detection is undertaken in real and Fourier space, and when the algorithms are applied to the shear and the convergence. We show that the multiscale method applied to the convergence is faster and more efficient at detecting clusters than single scale methods applied to the shear. We obtained an increase of 25% in the number of detections while maintaining the same purity compared to the most up-to-date aperture mass filter. We analyse the detected catalogues and quantify the efficiency of the matching procedure, showing in particular that less than 5% of the detections from the multiscale method can be ascribed to line-of-sight alignments. The method is well-adapted to the more sensitive, wider-area, optical surveys that will be available in the near future, and paves the way to cluster samples that are as near as possible to being selected by total matter content.

astro-ph.CO↗