arXiv subjects

J. Choppin de Janvry

Publications and source records attributed to J. Choppin de Janvry.

3 recordsLinked to original sources

arXiv2026

Cosmic Shear constraints from HSC Year 3 with clustering calibration of the tomographic redshift distributions from DESI

We reanalyze cosmological constraints from Hyper Suprime-Cam (HSC) Y3 shear-shear correlation function using new calibration of the tomographic redshift distribution via the clustering redshifts method with DESI spectroscopy presented in Choppin de Janvry et al. (2025a). We present both importance sampling of the original MCMC chains by HSC, applying the weights of our newly calibrated $Δz$ priors, as well as full MCMC analysis with new photometric redshift distributions, finding consistent results between the two. We obtain the growth of structure parameter $S_8\equivσ_8\sqrt{Ω_m/0.3}=0.804^{0.019}_{-0.018}$, compared to previous HSC Y3 result of $S_8=0.769^{+0.031}_{-0.034}$, which is a 1.8 reduction of error due to the improved clustering redshift calibrations, with the central value shifting considerably higher towards Planck cosmology. With the new photometric redshift calibration, HSC Y3 has comparable constraining power to the recent KIDS Legacy and DES Y6 results.

astro-ph.CO↗

arXiv2026

Full calibration of the tomographic redshift distribution from the HSC PDR3 Shape Catalog with DESI

The calibration of tomographic redshift distributions is essential for cosmological analysis of weak lensing data. In this work, we calibrate all four tomographic bins of the Hyper Suprime Camera (HSC) weak lensing catalog with the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 and 2 using the clustering redshifts technique. We include z > 1.2 redshift sources such as emission line galaxies (ELG) and quasars (QSO) sources in our calibration, which were not available in the previous HSC calibration (Rau et al. 2022), allowing a complete calibration of all the redshift bins. We find the first tomographic bin exhibits a small shift towards low redshifts. The second bin is in good agreement with the photometric calibration, while third and fourth bin exhibit a shift towards higher redshifts. However, these shifts are considerably smaller than the shifts obtained in the HSC Year 3 cosmic shear analyses. We evaluate the impact of galaxy bias and magnification effects from all the samples on the measurements, finding them to be small, and we propose corrections to reduce them further. Specifically, we relax the assumption of linear bias and only assume no redshift evolution of the cross-correlation coefficient, allowing us to leverage smaller clustering scales. We model the redshift distributions with splines and compare our results to previous analyses as well as to other parameterizations found in literature. For the two high-redshift tomographic bins, we find the shifts to higher redshifts with respect to the measurements performed in Rau+2022 to be $Δz_3=-0.043^{+0.022}_{-0.020}$ and $Δz_4=-0.050^{+0.012}_{-0.012}$.

astro-ph.CO↗

arXiv2026

Lyman Break Galaxy selection and redshift measurement with supervised contrastive learning

Some of the next steps for high-precision cosmology lie within the high-redshift, high-density universe. Spectroscopic survey experiments such as the Dark Energy Spectroscopic Instrument (DESI)'s second phase DESI Run 2 will shift towards probing Lyman Break Galaxy (LBG) populations from z$\sim$2 to z$\sim$4.5. For this faint sample, spectroscopic redshift measurement and sample decontamination remains a challenge, even after target selection. We propose an approach based on supervised weighted contrastive learning, in order to both learn a redshift representation for spectra and decontaminate the sample from quasars and low redshift emission line galaxies. This strategy generalizes the contrastive learning loss approach with continuous relationship weights, such that the network simultaneously learns redshift and classification tasks. The model shows stronger outlier classification and comparable redshift identification performances when compared to the previous network used for DESI (a modified version of QuasarNET) on the same dataset. In particular, contrastive learning is well suited to the small, visually-inspected sample used for training and testing, especially given the multi-task nature of this work.

astro-ph.CO↗