Reconstructing galaxy star formation histories from COSMOS2020 photometry using simulation-based inference
G. Aufort, C. Laigle, H.J. McCracken, D. Le Borgne, R. Arango-Toro, L. Ciesla, O. Ilbert, L. Tresse, Y. Dubois

TL;DR
This paper introduces a new simulation-based inference method using neural networks to reconstruct galaxy star formation histories from broad-band photometry, providing accurate, unbiased posterior estimates and insights into galaxy evolution.
Contribution
The study presents a novel neural network-based SBI approach to infer galaxy SFHs from photometry, validated with simulations and applied to COSMOS2020 data, enabling large-scale SFH analysis.
Findings
Accurate reconstruction of SFHs with well-calibrated credible intervals.
Galaxy SFHs show mass-dependent trends, with low-mass galaxies more actively forming stars.
Passive galaxies' formation redshifts are mostly around z~3, regardless of mass.
Abstract
We propose a novel method to reconstruct the full posterior distribution of the star formation histories (SFHs) of galaxies from broad-band photometry. Our method combines simulation-based inference (SBI) using a neural network trained with SFHs and photometry from the {\sc Horizon-AGN} hydrodynamical cosmological simulation. We apply it to reconstruct SFHs using COSMOS2020 photometry at redshift . We are able to accurately estimate the SFH and quantify the uncertainty on simulated data, with an unbiased posterior mean and well calibrated credible intervals. Our SFHs are in broad agreement with independent literature measurements The SFHs of galaxies as a function of location in the versus diagram are in agreement with expectations. We extract summary statistics to quantify the shape of the SFH, number of peaks, and formation redshift. The slopes of the…
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