arXiv subjects

Aurélien Verdier

Publications and source records attributed to Aurélien Verdier.

4 recordsLinked to original sources

arXiv2026

4MOST Cosmology Redshift Survey (CRS): Clustering Properties of CRS Bright Galaxy and Luminous Red Galaxy Target Catalogues

The 4-metre Multi-Object Spectroscopic Telescope Cosmology Redshift Survey (4MOST CRS) will obtain 5.4 million spectroscopic redshifts over ~5700 deg^2 to map large-scale structure and enable measurements of baryon acoustic oscillations, growth rates via redshift-space distortions, and cross-correlations with weak-lensing surveys. We validate the target selections, photometry, masking, systematics, and redshift distributions of the bright galaxy (BG) and luminous red galaxy (LRG) target catalogues selected from the Dark Energy Spectroscopic Instrument (DESI) Legacy Survey DR10.1 (LS) imaging. We measure the angular two-point correlation function, $w(θ)$, test masking strategies, and recover redshift distributions via cross-correlation with DESI DR1 spectroscopy. For BGs, we adopt LS MASKBITS that veto bright stars and extended sources; for LRGs, we pair these with unblurred coadds of the WISE imaging (unWISE) W1 artefact masks. These choices suppress small-scale excess power without imprinting large-scale modes. A Limber-scaling test across BG $r$-band magnitude slices shows that, after applying the scaling, the $w(θ)$ curves collapse to a near-common power law, demonstrating photometric uniformity with depth and consistency between the North and South Galactic Caps. Cross-correlations with DESI spectroscopy recover the expected N(z), albeit with high shot noise at the brightest magnitudes. For LRGs, angular clustering in photo-$z$ slices ($0.4\le z<1.0$) is consistent between the Dark Energy Camera Legacy Survey (DECaLS) and Dark Energy Survey (DES) footprints and is well described by an approximate power law once photo-$z$ smearing is included; halo-occupation fits are consistent with recent LRG studies. Together, these tests indicate that the masks and target selections yield reliable clustering statistics, supporting precision large-scale structure analyses with 4MOST CRS.

astro-ph.CO↗

arXiv2025

The changing transit shape of TOI-3884 b

TOI-3884 b is a sub-Saturn transiting a fully convective M-dwarf. Observations indicate that the transit shape is chromatic and asymmetric as a result of persistent starspot crossings. This, along with the lack of photometric variability of the host star, indicates that the rotational axis of the star is tilted along our line of sight and the planet-occulted starspot is located close to the stellar pole. We acquired photometric transits over a period of three years with the Swiss 1.2-meter Euler telescope to track changes in the starspot configuration and detect any signs of decay or growth. The shape of the transit changes over time, and so far no two observations match perfectly. We conclude that the observed variability is likely not caused by changes in the temperature and size of the spot, but due to a slight (5.64 $\pm$ 0.64$^{\circ}$) misalignment between the spot center and the stellar pole, i.e., a small spin-spot angle ($Θ$). In addition, we were able to obtain precise measurements of the sky-projected spin-orbit angle ($λ$) of 37.3 $\pm$ 1.5\degree, and the true spin-orbit angle ($ψ$) of 54.3 $\pm$ 1.4\degree. The precise alignment measurements along with future atmospheric characterisation with the James Webb Space Telescope will be vital for understanding the formation and evolution of close-in, massive planets around fully convective stars.

astro-ph.EP↗

arXiv2024

Galaxy Spectra Networks (GaSNet). III. Generative pre-trained network for spectrum reconstruction, redshift estimate and anomaly detection

Classification of spectra (1) and anomaly detection (2) are fundamental steps to guarantee the highest accuracy in redshift measurements (3) in modern all-sky spectroscopic surveys. We introduce a new Galaxy Spectra Neural Network (GaSNet-III) model that takes advantage of generative neural networks to perform these three tasks at once with very high efficiency. We use two different generative networks, an autoencoder-like network and U-Net, to reconstruct the rest-frame spectrum (after redshifting). The autoencoder-like network operates similarly to the classical PCA, learning templates (eigenspectra) from the training set and returning modeling parameters. The U-Net, in contrast, functions as an end-to-end model and shows an advantage in noise reduction. By reconstructing spectra, we can achieve classification, redshift estimation, and anomaly detection in the same framework. Each rest-frame reconstructed spectrum is extended to the UV and a small part of the infrared (covering the blueshift of stars). Owing to the high computational efficiency of deep learning, we scan the chi-squared value for the entire type and redshift space and find the best-fitting point. Our results show that generative networks can achieve accuracy comparable to the classical PCA methods in spectral modeling with higher efficiency, especially achieving an average of $>98\%$ classification across all classes ($>99.9\%$ for star), and $>99\%$ (stars), $>98\%$ (galaxies) and $>93\%$ (quasars) redshift accuracy under cosmology research requirements. By comparing different peaks of chi-squared curves, we define the ``robustness'' in the scanned space, offering a method to identify potential ``anomalous'' spectra. Our approach provides an accurate and high-efficiency spectrum modeling tool for handling the vast data volumes from future spectroscopic sky surveys.

astro-ph.GA↗

arXiv2021

Galaxy cluster cores as seen with VLT/MUSE: new strong-lensing analyses of RX J2129.4+0009, MS 0451.6-0305 & MACSJ2129.4-0741

We present strong-lensing analyses of three galaxy clusters, RXJ2129.4+0009 (z=0.235), MS0451.6-0305 (z=0.55), and MACSJ2129.4-0741 (z=0.589), using the powerful combination of Hubble Space Telescope (HST) multi-band observations, and Multi-Unit Spectroscopic Explorer (MUSE) spectroscopy. In RXJ2129, we newly spectroscopically confirm 15 cluster members. Our resulting mass model uses 8 multiple image systems as we include a galaxy-galaxy lensing system North-East of the cluster, and is composed of 71 halos including one dark matter cluster-scale halo and 2 galaxy-scale halos optimized individually. For MS0451, we report the spectroscopic identification of 2 new systems of multiple images in the Northern region, and 112 cluster members. Our mass model uses 16 multiple image systems, and 146 halos, including 2 large-scale halos, and 7 galaxy-scale halos independently optimized. For MACSJ2129, we report the spectroscopic identification of one new multiple image system at z=4.41, and newly measure spectroscopic redshifts for 4 cluster members. Our mass model uses 14 multiple image systems, and is composed of 151 halos, including 2 large-scale halos and 4 galaxy-scale halos independently optimized. Our best models have rms of 0.29'', 0.6'', 0.74'' in the image plane for RXJ2129, MS0451, and MACSJ2129 respectively. This analysis presents a detailed comparison with the existing literature showing excellent agreements, and discuss specific studies of lensed galaxies, e.g. a group of submilimeter galaxies at z=2.9 in MS0451, and a bright z=2.1472 red singly imaged galaxy in MACSJ2129.

astro-ph.GA↗