GalSBI: Forward Modelling Galaxy Clustering and Population

Abstract

Forward modelling is a powerful approach for analyzing large-scale structure surveys. For this purpose, we extend the GalSBI framework to jointly model the galaxy population and clustering using an efficient subhalo abundance matching scheme based on optimal transport. We use simulation-based inference to constrain the model parameters by comparing UFig image simulations with DES Y3 imaging data. As a validation, we find that galaxy photometry and morphology agree well with multi-band imaging data of different depths, namely DES and HSC deep fields. Galaxy clustering for simulation and data is also in good agreement when comparing the angular power spectrum for different magnitude and color cuts. We further compare simulated redshift distributions against high-precision photometric redshifts in HSC deep field imaging of the COSMOS field. We find the redshift distributions across magnitude cuts to be similar to previous work, however with more realistic uncertainty modelling due to the addition of clustering contribution to sample variance. The agreement of the mean redshifts with data is very good, between $0.2\sigma$ and $1.6\sigma$ for different magnitude cuts, with sample variance being the dominant uncertainty contributor in bright samples ($<24$ mag) and subdominant compared to galaxy population model uncertainty in fainter samples. As a byproduct we measure the galaxy luminosity function and galaxy-halo connection, which are broadly consistent with existing literature. The updated GalSBI code and galaxy population model are publicly available. They enable accurate forward-modelled image simulations with realistic clustering, which can be used to model the effect of sample variance, source clustering, redshift distributions, and blending in large-scale-structure surveys. This makes GalSBI a powerful tool for the analysis of current and next-generation cosmological galaxy surveys.