DiffstarPop: A generative physical model of galaxy star formation history

TL;DR

DiffstarPop is a differentiable model linking galaxy star formation histories to dark matter halo assembly, accurately reproducing simulation distributions and enabling fast generation of synthetic galaxy catalogs.

Contribution

The paper introduces DiffstarPop, a minimal-flexibility, differentiable model that connects galaxy SFH parameters with halo assembly histories, matching diverse simulation outputs.

Findings

Accurately reproduces distributions from IllustrisTNG, Galacticus, and UniverseMachine.

Generates SFHs for 1 million galaxies in just 1.1 CPU-seconds.

Provides a publicly available JAX code with Monte Carlo generators.

Abstract

We present DiffstarPop, a differentiable forward model of cosmological populations of galaxy star formation histories (SFH). In the model, individual galaxy SFH is parametrized by Diffstar, which has parameters $\theta_{\rm SFH}$ that have a direct interpretation in terms of galaxy formation physics, such as star formation efficiency and quenching. DiffstarPop is a model for the statistical connection between $\theta_{\rm SFH}$ and the mass assembly history (MAH) of dark matter halos. We have formulated DiffstarPop to have the minimal flexibility needed to accurately reproduce the statistical distributions of galaxy SFH predicted by a diverse range of simulations, including the IllustrisTNG hydrodynamical simulation, the Galacticus semi-analytic model, and the UniverseMachine semi-empirical model. Our publicly available code written in JAX includes Monte Carlo generators that supply statistical samples of galaxy assembly histories that mimic the populations seen in each simulation, and can generate SFHs for $10^6$ galaxies in 1.1 CPU-seconds, or 0.03 GPU-seconds. We conclude the paper with a discussion of applications of DiffstarPop, which we are using to generate catalogs of synthetic galaxies populating the merger trees in cosmological N-body simulations.