arXiv subjects

Constantin Payerne

Publications and source records attributed to Constantin Payerne.

12 recordsLinked to original sources

arXiv2026

Assessing the large-scale angular clustering of UNIONS Lyman Break Galaxies via cross-correlations

Lyman-break galaxies (LBGs), selected via the strong spectral break blueward of the Lyman limit, are powerful tracers of large-scale structure at redshifts $z>2$. In this work, we assess the feasibility of using LBGs selected from the Ultraviolet Near Infrared Optical Northern Survey (UNIONS) multi-band photometric catalog as cosmological probes of the high-redshift Universe using two-point statistics. We demonstrate that spatially varying imaging systematics, driven by variations in PSF depth, seeing across the UNIONS footprint, limit robust measurements of the LBG auto-angular power spectrum on large scales, even after correcting the LBG field with linear or non-linear mitigation techniques. This study shows that clustering analyses of faint galaxy samples close to survey depth are challenging. We therefore turn to cross-correlation measurements with external tracers, in particular the \textit{Planck} CMB lensing convergence and quasars from DESI DR1 and \textit{Quaia}, which are less sensitive to the angular imaging systematics. Using both data and mock catalogues, we demonstrate that the LBG--CMB lensing cross-power spectrum can be measured more robustly than the auto-spectrum, with an amplitude consistent with theoretical predictions. Residual systematics primarily manifest as excess variance at large angular scales, without introducing a significant bias in the recovered signal. Taken together, these results establish UNIONS-selected LBGs as reliable tracers for cross-correlation cosmology at $z\sim 2.5$, and highlight cross-correlation techniques as a powerful and robust avenue for extracting cosmological information from photometric high-redshift galaxy samples in the presence of complex imaging systematics.

astro-ph.CO↗

arXiv2026

Constraining Galaxy Cluster Triaxiality via Weak Lensing -- I. Preparation for the Rubin Data Beyond Leading Order

The 3D mass distributions of galaxy clusters are generally triaxial, a geometry that is difficult to constrain from projected observations. In this work, we measure the projected halo shapes of clusters from their weak lensing signatures using the triaxiality functionality in the Cluster Lensing Mass Modeling software, a tool developed by the Dark Energy Science Collaboration to analyze data from NSF-DOE Rubin Observatory's Legacy Survey of Space and Time (LSST). We measure ensemble halo ellipticity on the plane of the sky via axis-aligned stacking and multipole expansion of the weak lensing data. We study a precursor dataset -- the redMaPPer cluster catalog, the metacalibration shape catalog, and the Directional Neighborhood Fitting photometric redshift catalog from the Dark Energy Survey Year 3 public data release. We select clusters that have a high centering probability (>90%) of the identified central galaxy, and use the satellite galaxy distribution to determine the major-axis orientation for stacking. We extend the analysis to the second order of ellipticity in the monopole and quadrupole measurement. The projected ellipticity of the cluster sample is found to be $0.310^{+0.017}_{-0.016}$ (axis ratio $0.527^{+0.018}_{-0.019}$). The projected cluster ellipticity shows no statistically significant dependence on mass and redshift. We further verify the accuracy of the cluster shape measurement using mock catalogs. This analysis is applicable to datasets from upcoming wide-area cosmic surveys such as LSST, Euclid, and the Roman Space Telescope, where larger sample sizes will lead to tighter constraints on the cluster ellipticities.

astro-ph.CO↗

arXiv2026

Simulation-based cosmological inference from optically-selected galaxy clusters with $\texttt{Capish}$

Galaxy clusters are powerful probes of the growth of cosmic structure through measurements of their abundance as a function of mass and redshift. Extracting precise cosmological constraints from cluster surveys is challenging, as we must contend the complex relationship between richness and the underlying halo mass, selection function biases, super-sample covariance, and correlated measurement noise between mass proxies. As upcoming photometric surveys are expected to detect tens to hundreds of thousands of galaxy clusters, controlling these systematics becomes essential. In this paper, we present a forward-modelling approach using Simulation-Based Inference (SBI), which provides a natural framework for jointly modelling cluster abundance and lensing mass observables while capturing systematic uncertainties at higher fidelity than analytic likelihood methods - which rely on simplifying assumptions such as fixed covariances and Gaussianity - without requiring an explicit likelihood formulation. We introduce $\texttt{Capish}$, a Python code for generating forward-modelled galaxy cluster catalogues using halo mass functions and incorporating observational effects. We perform SBI using neural density estimation with normalizing flows, trained on abundance and mean lensing mass measurements in observed redshift-richness bins. Our forward model accounts for realistic noise, redshift uncertainties, selection functions, and correlated scatter between lensing mass and observed richness. We find good agreement with likelihood-based analyses, with broader SBI posteriors reflecting the increased realism of the forward model. We also test $\texttt{Capish}$ on cluster catalogues built from a large cosmological simulation, finding a good fit to cosmological parameters.

astro-ph.CO↗

arXiv2025

Forecasting local Primordial Non-Gaussianities from UNIONS Lyman-Break Galaxies and Planck CMB lensing

Local Primordial non-Gaussianities (PNGs), characterized by $f_{\rm NL}^{\rm loc}$, provide a powerful window into the physics of inflation. Cross-correlating high-redshift tracer samples with the CMB lensing potential offers a particularly robust probe of PNGs, mitigating imaging systematics that typically affect large-scale measurements from tracer auto-spectra. In this context, UNIONS enables the selection of $u$-dropout high-redshift Lyman-Break Galaxies (LBGs). We perform a MCMC-based forecast to estimate the uncertainties on $f_{\rm NL}^{\rm loc}$ and on a galaxy bias parameter, which captures our uncertainty in the tracer bias. From the angular cross-power spectrum between LBGs and Planck CMB lensing, we forecast $\sigma(f_{\rm NL}^{\rm loc})=34$ for an idealized photometric sample of $r<24.3$ LBGs selected with a Random Forest classification algorithm from UNIONS-like $ugriz$ imaging, with a resulting surface density of $1,100$ deg$^{-2}$. This precision can be improved to $\sigma(f_{\rm NL}^{\rm loc})=20$ after spectroscopic follow-up with DESI, during its next phase starting in 2029, DESI-II. We test a more realistic $u$-dropout LBG selection using early UNIONS data, which yields a denser sample of $r<24.2$ objects at $1,400$ deg$^{-2}$. From this sample, covering a larger footprint and expected to have a higher large-scale galaxy bias, we forecast $\sigma(f_{\rm NL}^{\rm loc})=20$, with similar precision achievable after DESI spectroscopic follow-up. In addition, we perform preliminary validation of the redshift distribution using the clustering-redshift method with DESI DR1 data, confirming the calibration from deep, small-area photometric fields. However, accounting for uncertainties in the clustering-redshift distribution significantly degrades the $f_{\rm NL}^{\rm loc}$ constraining power.

astro-ph.CO↗

arXiv2025

Quantifying the Impact of LSST $u$-band Survey Strategy on Photometric Redshift Estimation and the Detection of Lyman-break Galaxies

The Vera C. Rubin Observatory will conduct the Legacy Survey of Space and Time (LSST), promising to discover billions of galaxies out to redshift 7, using six photometric bands ($ugrizy$) spanning the near-ultraviolet to the near-infrared. The exact number of and quality of information about these galaxies will depend on survey depth in these six bands, which in turn depends on the LSST survey strategy: i.e., how often and how long to expose in each band. $u$-band depth is especially important for photometric redshift (photo-$z$) estimation and for detection of high-redshift Lyman-break galaxies (LBGs). In this paper we use a simulated galaxy catalog and an analytic model for the LBG population to study how recent updates and proposed changes to Rubin's $u$-band throughput and LSST survey strategy impact photo-$z$ accuracy and LBG detection. We find that proposed variations in $u$-band strategy have a small impact on photo-$z$ accuracy for $z < 1.5$ galaxies, but the outlier fraction, scatter, and bias for higher redshift galaxies varies by up to 50%, depending on the survey strategy considered. The number of $u$-band dropout LBGs at $z \sim 3$ is also highly sensitive to the $u$-band depth, varying by up to 500%, while the number of $griz$-band dropouts is only modestly affected. Under the new $u$-band strategy recommended by the Rubin Survey Cadence Optimization Committee, we predict $u$-band dropout number densities of $110$ deg$^{-2}$ (3200 deg$^{-2}$) in year 1 (10) of LSST. We discuss the implications of these results for LSST cosmology.

astro-ph.CO↗

arXiv2025

Weak lensing mass-richness relation of redMaPPer clusters in the LSST DESC DC2 simulations

Cluster scaling relations are key ingredients in cluster abundance-based cosmological studies. In optical cluster cosmology, where clusters are detected through their richness, cluster-weak gravitational lensing has proven to be a powerful tool to constrain the cluster mass-richness relation. This work is conducted as part of the Dark Energy Science Collaboration (DESC), which aims to analyze the Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory, starting in 2026. Weak lensing-inferred cluster properties, such as mass, suffer from several sources of bias. We constrain the mass-richness relation of 3,600 clusters detected by the redMaPPer algorithm in the cosmoDC2 extra-galactic mock catalog of the LSST DESC DC2 simulation, covering 440 square degrees, using number count measurements and either stacked weak lensing profiles or mean cluster masses in intervals of richness ($20 < \lambda < 200$) and redshift ($0.2 < z < 1$). We find that, for an LSST-like source galaxy density, our constraints are robust to changes in the concentration-mass relation and dark matter density profile modeling choices, when source redshifts and shapes are perfectly known. We find that photometric redshift uncertainties can introduce bias at the 1$\sigma$ level, which can be mitigated by an overall correction factor, fitted jointly with the scaling parameters. We find that including positive shear-richness covariance in the fit shifts the results by up to 0.5$\sigma$. Our constraints also compare fairly to a fiducial mass-richness relation, obtained from matching cosmoDC2 halo masses to redMaPPer-detected cluster richnesses.

astro-ph.CO↗

arXiv2024

High redshift LBGs from deep broadband imaging for future spectroscopic surveys

Lyman break galaxies (LBGs) are promising probes for clustering measurements at high redshift, $z>2$, a region only covered so far by Lyman-$α$ forest measurements. In this paper, we investigate the feasibility of selecting LBGs by exploiting the existence of a strong deficit of flux shortward of the Lyman limit, due to various absorption processes along the line of sight. The target selection relies on deep imaging data from the HSC and CLAUDS surveys in the $g,r,z$ and $u$ bands, respectively, with median depths reaching 27 AB in all bands. The selections were validated by several dedicated spectroscopic observation campaigns with DESI. Visual inspection of spectra has enabled us to develop an automated spectroscopic typing and redshift estimation algorithm specific to LBGs. Based on these data and tools, we assess the efficiency and purity of target selections optimised for different purposes. Selections providing a wide redshift coverage retain $57\%$ of the observed targets after spectroscopic confirmation with DESI, and provide an efficiency for LBGs of $83\pm3\%$, for a purity of the selected LBG sample of $90\pm2\%$. This would deliver a confirmed LBG density of $\sim 620$ deg$^{-2}$ in the range $2.3<z<3.5$ for a $r$-band limiting magnitude $r<24.2$. Selections optimised for high redshift efficiency retain $73\%$ of the observed targets after spectroscopic confirmation, with $89\pm4\%$ efficiency for $97\pm2\%$ purity. This would provide a confirmed LBG density of $\sim 470$ deg$^{-2}$ in the range $2.8<z<3.5$ for a $r$-band limiting magnitude $r<24.5$. A preliminary study of the LBG sample 3d-clustering properties is also presented and used to estimate the LBG linear bias. A value of $b_{LBG} = 3.3 \pm 0.2 (stat.)$ is obtained for a mean redshift of 2.9 and a limiting magnitude in $r$ of 24.2, in agreement with results reported in the literature.

astro-ph.CO↗

arXiv2024

Towards including super-sample covariance in the unbinned likelihood for cluster abundance cosmology

The measurement of the abundance of galaxy clusters in the Universe is a sensitive probe of cosmology, which depends on both the expansion history of the Universe and the growth of structure. Density fluctuations across the finite survey volume add noise to this measurement, this is often referred to as super-sample covariance (SSC). For an unbinned cluster analysis, such noise has not been included in the cluster likelihood, since the effect of SSC was small compared to the Poisson shot-noise for samples of a few hundred clusters. For upcoming large cluster surveys such as the Rubin LSST, which will deliver catalogs of tens of thousands of clusters, this effect will no longer be negligible. In this paper, we propose a new hybrid likelihood based on the Gauss-Poisson Compound model (GPC), by using infinitesimal mass bins and standard redshift bins. This likelihood has the advantages of an unbinned Poisson likelihood whilst successfully incorporating the effects of SSC. Using a simulated dark matter halo catalog, we find that the hybrid likelihood, accounting for both Poisson noise and SSC, increases the dispersion of the parameter posteriors by 20 percent when using 100,000 clusters compared to the standard unbinned likelihood, based on Poisson statistics only.

astro-ph.CO↗

arXiv2023

Multipole analysis of cluster weak lensing shear in The Three Hundred project

Weak gravitational lensing is an important tool to estimate the masses of galaxy clusters, as it allows us to directly access their projected surface mass density along the line-of-sight (LOS) in a manner largely independent of their dynamical state. Moreover, we can extract information on the projected shape of the cluster mass distribution. In this work, we generate mock catalogs of lensed background galaxies to measure the individual lensing properties of galaxy clusters from the simulation project The Three Hundred. By repeating the analysis for different projections of the same cluster, we find that the use of shear multipoles provides constraints on the ellipticity of the cluster projected mass density but does not have a significant impact on the cluster mass reconstruction compared to the standard approach.

astro-ph.CO↗

arXiv2023

Testing the accuracy of likelihoods for cluster abundance cosmology

The abundance of galaxy clusters is a sensitive probe to the amplitude of matter density fluctuations, the total amount of matter in the Universe as well as its expansion history. Inferring correct values and accurate uncertainties of cosmological parameters requires accurate knowledge of cluster abundance statistics, encoded in the likelihood function. In this paper, we test the accuracy of cluster abundance likelihoods used in the literature, namely the Poisson and Gaussian likelihoods as well as the more complete description of the Gauss-Poisson Compound likelihood. This is repeated for a variety of binning choices and analysis setups. In order to evaluate the accuracy of a given likelihood, this work compares individual posterior covariances to the covariance of estimators over the 1000 simulated dark matter halo catalogs obtained from PINOCCHIO algorithm. We find that for Rubin/LSST or Euclid-like surveys the Gaussian likelihood gives robust constraints over a large range of binning choices. The Poisson likelihood, that does not account for sample covariance, always underestimates the errors on the parameters, even when the sample volume is reduced or only high-mass clusters are considered. We find no benefit in using the more complex Gauss-Poisson Compound likelihood as it gives essentially the same results as the Gaussian likelihood, but at a greater computational cost. Finally, in this ideal setup, we note only a small gain on the parameter error bars when using a large number of bins in the mass-redshift plane.

astro-ph.CO↗

arXiv2022

Statistical properties of the gravitational force through ordering statistics

We investigate the statistical distribution of Newtonian gravitational forces acting on a test particle embedded in an infinite, homogeneous, and uncorrelated random gas of point masses. Using order statistics, we derive the probability density functions of distances to the $n$-th nearest neighbors in arbitrary spatial dimensions and analyze their contributions to the total gravitational force. We show that, in three dimensions, the formally divergent variance of the Holtsmark probability density function arises entirely from the first nearest neighbor, while contributions from more distant neighbors remain finite. Our results provide a clear decomposition of local versus distant contributions to the gravitational forces exerted on a test particle, and explore the dominant role of nearest neighbors in the divergence of the Holtsmark distribution's variance.

cond-mat.stat-mech↗

arXiv2022

Validating Synthetic Galaxy Catalogs for Dark Energy Science in the LSST Era

Large simulation efforts are required to provide synthetic galaxy catalogs for ongoing and upcoming cosmology surveys. These extragalactic catalogs are being used for many diverse purposes covering a wide range of scientific topics. In order to be useful, they must offer realistically complex information about the galaxies they contain. Hence, it is critical to implement a rigorous validation procedure that ensures that the simulated galaxy properties faithfully capture observations and delivers an assessment of the level of realism attained by the catalog. We present here a suite of validation tests that have been developed by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). We discuss how the inclusion of each test is driven by the scientific targets for static ground-based dark energy science and by the availability of suitable validation data. The validation criteria that are used to assess the performance of a catalog are flexible and depend on the science goals. We illustrate the utility of this suite by showing examples for the validation of cosmoDC2, the extragalactic catalog recently released for the LSST DESC second Data Challenge.

astro-ph.CO↗