2024/12/11 by Matteo Zennaro, Zennaro, Matteo, Giovanni Aricò +15 · 3 citations
Engineering · Physics and Astronomy · #Astronomy and Astrophysical Research #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Particle Accelerators and Free-Electron Lasers
paper · pdf · doi:10.48550/arxiv.2412.08623
openalex publication_date 2024/12/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Galaxy clustering and galaxy-galaxy lensing are two of the main observational probes in Stage-IV large-scale structure surveys. Unfortunately, the complicated relationship between galaxies and matter limits the exploitation of this data. Galaxy bias models -- such as the hybrid Lagrangian bias expansion -- allow describing galaxy clustering down to scales as small as k = 0.7h/Mpc. However, the galaxy-matter cross-power spectra are already affected by baryons on these scales, directly impacting the modelling of galaxy-galaxy lensing. We propose to extend models of the galaxy-matter cross-power spectrum P\rm gm(k) (currently only accounting for dark matter) by including a baryonic correction inferred from the matter component (S\rm mm(k)), so that P\rm gm, full physics (k) = √S\rm mm P\rm gm, gravity only. We use the FLAMINGO simulations to measure the effect of baryons on the galaxy-matter cross-power spectrum and to assess the performance of our model. We perform a Bayesian analysis of synthetic data, implementing a model based on BACCO's hybrid Lagrangian bias expansion (for the nonlinear galaxy bias) and Baryon Correction Model. Ignoring baryons in the galaxy-matter cross-power spectrum leads to a biased inference of the galaxy bias, while ignoring baryons in both the galaxy-matter and matter-matter power spectra leads to a biased inference of both the galaxy bias and cosmological parameters. In contrast, our method is 1% accurate compared to all physics variations in FLAMINGO and on all scales described by hybrid perturbative models (k < 0.7h/Mpc). Moreover, our model leads to inferred bias and cosmological parameters compatible within 1σ with their reference values. We anticipate that our method will be a promising candidate for analysing forthcoming Stage-IV survey data.