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


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Date

2024-05

Publication Type

Journal Article

ETH Bibliography

yes

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Data

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 1σ 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.

Publication status

published

Editor

Book title

Volume

2024 (5)

Pages / Article No.

49

Publisher

IOP Publishing

Event

Edition / version

Methods

Software

Geographic location

Date collected

Date created

Subject

galaxy surveys; galaxy evolution; Bayesian reasoning; cosmological simulations

Organisational unit

03928 - Refregier, Alexandre / Refregier, Alexandre check_circle

Notes

Funding

192243 - Cosmological Weak Lensing and Neutral Hydrogen (SNF)

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