arXiv subjects

A. Guinot

Publications and source records attributed to A. Guinot.

5 recordsLinked to original sources

arXiv2026

UNIONS-3500 Weak Lensing: I. A Galaxy Shape Catalogue in the Northern Sky

Weak gravitational lensing has become a widely used effect to characterise the dark-matter distribution on large scales in the Universe by measuring galaxy ellipticities and their statistical correlations. We present the first weak gravitational lensing catalogue for cosmic-shear cosmology of the Ultraviolet Near Infrared Optical Northern Survey (UNIONS). We analyse approximately $3\,500$ square degrees of sky area in the Northern Hemisphere, observed in the $r$-band by MegaCam on the Canada-France Hawai'i Telescope, achieving a median seeing of 0.7 arcsec. Starting from images calibrated for astrometry and photometry, we describe the steps from image processing to catalogue creation. These steps include masking, source detection and selection, star selection, point spread function (PSF) modelling, shape measurement, and calibration. We conduct extensive validation tests, particularly to assess and mitigate the leakage of PSF ellipticity into galaxy shapes. We demonstrate the robustness of the catalogue by investigating correlations between ellipticity and other observational variables as well as structural elements, such as observer-frame image positions and proximity to bright stars. The final galaxy catalogue contains $62$ million galaxies, corresponding to an effective source density of $4.96$ arcmin$^{-2}$. The ellipticity dispersion, commonly referred to as shape noise, is $\sigma_\epsilon = 0.27$. Initiating the first major cosmological analysis by the UNIONS collaboration, this is the first in a series of five papers which cover the various aspects of a robust cosmic shear analysis. Two companion papers discuss the robustness of the catalogue, one through the level of $B$-mode contamination and another by producing and analysing dedicated image simulations for shear calibration, while the other two present cosmological results in real and harmonic space.

astro-ph.CO↗

arXiv2026

UNIONS-3500 Weak Lensing: II. B-mode validation for cosmic shear

At Stage-III sensitivities, cosmic shear $B$ modes unambiguously indicate systematic contamination and are often used to inform data selection and scale cuts for cosmological inference. We validate $B$ modes for the Ultraviolet Near-Infrared Optical Northern Survey (UNIONS)-3500 (2894 deg$^2$, $n_\mathrm{eff} \approx 5.0$ arcmin$^{-2}$) using three $E$/$B$-separable statistics: pure-mode correlation functions $\xi_\pm^{\mathrm{B}}(\theta)$, Complete Orthogonal Sets of $E$/$B$-mode Integrals (COSEBI) $B$-mode amplitudes $B_n$, and harmonic-space power spectra $C_\ell^{BB}$. For each statistic, we compute probability-to-exceed (PTE) values over a two-dimensional grid of scale-cut boundaries; our adopted cuts lie in broad stable regions of acceptable PTE. $B$-mode detections and PTE failures on initial catalog versions led us to investigate galaxy size cuts and stellar halo masking. After cuts, all three statistics pass the null test (minimum PTE $= 0.18$). Before scale cuts, we measure an oscillatory COSEBI $B$-mode pattern consistent with repeating additive shear bias, a detector-level effect seen across multiple Stage-III surveys including CFHTLenS, which used the same MegaCam camera; scale cuts that exclude the charge-coupled device (CCD) angular scale suppress it. Although these statistics probe the same two-point shear field, scale cuts in one do not map exactly onto cuts in another, because their respective filter functions weight angular scales differently. The most conservative validation therefore requires scale and sample selections that pass null tests across all frameworks simultaneously, an approach that applies directly to Stage-IV surveys where systematic errors dominate.

astro-ph.CO↗

arXiv2022

ShapePipe: A modular weak-lensing processing and analysis pipeline

We present the first public release of ShapePipe, an open-source and modular weak-lensing measurement, analysis, and validation pipeline written in Python. We describe the design of the software and justify the choices made. We provide a brief description of all the modules currently available and summarise how the pipeline has been applied to real Ultraviolet Near-Infrared Optical Northern Survey data. Finally, we mention plans for future applications and development. The code and accompanying documentation are publicly available on GitHub.

astro-ph.IM↗

arXiv2021

Deep Transfer Learning for Blended Source Identification in Galaxy Survey Data

We present BlendHunter, a proof-of-concept for a deep transfer learning based approach for the automated and robust identification of blended sources in galaxy survey data. We take the VGG-16 network with pre-trained convolutional layers and train the fully connected layers on parametric models of COSMOS images. We test the efficacy of the transfer learning by taking the weights learned on the parametric models and using them to identify blends in more realistic CFIS-like images. We compare the performance of this method to SEP (a Python implementation of SExtractor) as function of noise level and the separation between sources. We find that BlendHunter outperforms SEP by $\sim 15\%$ in terms of classification accuracy for close blends ($<10$ pixel separation between sources) regardless of the noise level used for training. Additionally, the method provides consistent results to SEP for distant blends ($\geq10$ pixel separation between sources) provided the network is trained on data with a relatively close noise standard deviation to the target images. The code and data have been made publicly available to ensure the reproducibility of the results.

astro-ph.IM↗

arXiv2020

Multi-CCD Point Spread Function Modelling

Galaxy imaging surveys observe a vast number of objects that are affected by the instrument's Point Spread Function (PSF). Weak lensing missions, in particular, aim at measuring the shape of galaxies, and PSF effects represent an important source of systematic errors which must be handled appropriately. This demands a high accuracy in the modelling as well as the estimation of the PSF at galaxy positions. Sometimes referred to as non-parametric PSF estimation, the goal of this paper is to estimate a PSF at galaxy positions, starting from a set of noisy star image observations distributed over the focal plane. To accomplish this, we need our model to first of all, precisely capture the PSF field variations over the Field of View (FoV), and then to recover the PSF at the selected positions. This paper proposes a new method, coined MCCD (Multi-CCD PSF modelling), that creates, simultaneously, a PSF field model over all of the instrument's focal plane. This allows to capture global as well as local PSF features through the use of two complementary models which enforce different spatial constraints. Most existing non-parametric models build one model per Charge Coupled Device (CCD), which can lead to difficulties in capturing global ellipticity patterns. We first test our method on a realistic simulated dataset comparing it with two state-of-the-art PSF modelling methods (PSFEx and RCA). We outperform both of them with our proposed method. Then we contrast our approach with PSFEx on real data from CFIS (Canada France Imaging Survey) that uses the CFHT (Canada-France-Hawaii Telescope). We show that our PSF model is less noisy and achieves a 22% gain on pixel Root Mean Squared Error (RMSE) with respect to PSFEx. We present, and share the code of, a new PSF modelling algorithm that models the PSF field on all the focal plane that is mature enough to handle real data.

astro-ph.IM↗