Abstract
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.
Cited by
3 articles.
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