Simulation-based inference of deep fields: galaxy population model and redshift distributions

Abstract

Accurate redshift calibration is required to obtain unbiased cosmological information from large-scale galaxy surveys. In a forward modelling approach, the redshift distribution n(z) of a galaxy sample is measured using a parametric galaxy population model constrained by observations. We use a model that captures the redshift evolution of the galaxy luminosity functions, colours, and morphology, for red and blue samples. We constrain this model via simulation-based inference, using factorized Approximate Bayesian Computation (ABC) at the image level. We apply this framework to HSC deep field images, complemented with photometric redshifts from COSMOS2020. The simulated telescope images include realistic observational and instrumental effects. By applying the same processing and selection to real data and simulations, we obtain a sample of n(z) distributions from the ABC posterior. The photometric properties of the simulated galaxies are in good agreement with those from the real data, including magnitude, colour and redshift joint distributions. We compare the posterior n(z) from our simulations to the COSMOS2020 redshift distributions obtained via template fitting photometric data spanning the wavelength range from UV to IR. We mitigate sample variance in COSMOS by applying a reweighting technique. We thus obtain a good agreement between the simulated and observed redshift distributions, with a difference in the mean at the 1$\sigma$ level up to a magnitude of 24 in the i band. We discuss how our forward model can be applied to current and future surveys and be further extended. The ABC posterior and further material will be made publicly available at https://cosmology.ethz.ch/research/software-lab/ufig.html.

Accepted for publication in JCAP. 35 pages, 9 figures

References in corpus (10)

• Array Programming with NumPy
• The Gaia mission
• The Cosmic Evolution Survey (COSMOS) -- Overview
• EAZY: A Fast, Public Photometric Redshift Code
• K-corrections and filter transformations in the ultraviolet, optical, and near infrared
• The Evolution of Galaxy Structure over Cosmic Time
• The Zurich Extragalactic Bayesian Redshift Analyzer (ZEBRA) and its first application: COSMOS
• Estimating the Redshift Distribution of Faint Galaxy Samples
• Photometric Redshifts for Next-Generation Surveys
• Fast Forward Modelling of Galaxy Spatial and Statistical Distributions

Cited by in corpus (12)

• KiDS-SBI: Simulation-based inference analysis of KiDS-1000 cosmic shear
• pop-cosmos: A comprehensive picture of the galaxy population from COSMOS data
• Deriving the star formation histories of galaxies from spectra with simulation-based inference
• Impact of stellar population synthesis choices on forward modelling-based redshift distribution estimates
• pop-cosmos: Scaleable inference of galaxy properties and redshifts with a data-driven population model
• pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population
• Data-Space Validation of High-Dimensional Models by Comparing Sample Quantiles
• UFig v1: The ultra-fast image generator
• galsbi: A Python package for the GalSBI galaxy population model
• Flexible Simulation Based Inference for Galaxy Photometric Fitting with Synthesizer
• GalSBI: Phenomenological galaxy population model for cosmology using simulation-based inference
• GalSBI-SPS: a stellar population synthesis-based galaxy population model for cosmology and galaxy evolution applications