Speaker
Description
Next-generation cosmological surveys — LSST, Euclid, Roman — will be limited not by statistical power but by systematic uncertainties rooted in our understanding of galaxy populations. Photometric redshift calibration, intrinsic alignments, and baryonic effects on the matter power spectrum are all manifestations of the same underlying problem: cosmological inference requires an accurate model of the galaxy population and its evolution. I will present pop-cosmos, a simulation-based inference framework that learns the joint distribution of galaxy properties — redshift, stellar mass, star formation history, dust, metallicity, and black hole activity — from deep multiwavelength photometric data. The resulting generative model simultaneously delivers calibrated redshift distributions for weak lensing tomography, enables physically-motivated sample selection that mitigates intrinsic alignment contamination and unlocks multi-tracer clustering analyses, and provides improved Type Ia supernova standardisation through host galaxy property inference. It also reveals the astrophysical processes — including black-hole-driven feedback and the cessation of star formation — that shape the very populations we use as cosmological tracers. I will show applications spanning the Kilo-Degree Survey, DESI, the Zwicky Transient Facility, and COSMOS-Web, illustrating how a unified model of galaxy evolution connects to the next generation of cosmological constraints.