GalSBI: Phenomenological galaxy population model for cosmology using simulation-based inference

TL;DR

GalSBI is a new simulation-based inference model for galaxy populations that accurately reproduces observed data and can be used for cosmological analyses, leveraging an emulator for efficient inference.

Contribution

It introduces a phenomenological galaxy population model using simulation-based inference with an emulator to accelerate analysis, and demonstrates its effectiveness with real observational data.

Findings

Excellent agreement with observed galaxy properties

Redshift distribution matches high-precision photometric redshifts within 1.5σ

Open-source Python package for generating realistic galaxy catalogs

Abstract

We present GalSBI, a phenomenological model of the galaxy population for cosmological applications using simulation-based inference. The model is based on analytical parametrizations of galaxy luminosity functions, morphologies and spectral energy distributions. Model constraints are derived through iterative Approximate Bayesian Computation, by comparing Hyper Suprime-Cam deep field images with simulations which include a forward model of instrumental, observational and source extraction effects. We developed an emulator trained on image simulations using a normalizing flow. We use it to accelerate the inference by predicting detection probabilities, including blending effects and photometric properties of each object, while accounting for background and PSF variations. This enables robustness tests for all elements of the forward model and the inference. The model demonstrates excellent performance when comparing photometric properties from simulations with observed imaging data for key parameters such as magnitudes, colors and sizes. The redshift distribution of simulated galaxies agrees well with high-precision photometric redshifts in the COSMOS field within $1.5\sigma$ for all magnitude cuts. Additionally, we demonstrate how GalSBI's redshifts can be utilized for splitting galaxy catalogs into tomographic bins, highlighting its potential for current and upcoming surveys. GalSBI is fully open-source, with the accompanying Python package, $\texttt{galsbi}$, offering an easy interface to quickly generate realistic, survey-independent galaxy catalogs.