Diffstar: A Fully Parametric Physical Model for Galaxy Assembly History

Abstract

We present Diffstar, a smooth parametric model for the in-situ star formation history (SFH) of galaxies. Diffstar is distinct from conventional SFH models that are used to interpret the spectral energy distribution (SED) of an observed galaxy, because our model is parametrized directly in terms of basic features of galaxy formation physics. The Diffstar model assumes that star formation is fueled by the accretion of gas into the dark matter halo of the galaxy, and at the foundation of Diffstar is a parametric model for halo mass assembly, Diffmah. We include parametrized ingredients for the fraction of accreted gas that is eventually transformed into stars, ms , and for the timescale over which this transformation occurs, τ cons ; some galaxies in Diffstar experience a quenching event at time t q , and may subsequently experience rejuvenated star formation. We fit the SFHs of galaxies predicted by the IllustrisTNG (TNG) and UniverseMachine (UM) simulations with the Diffstar parameterization, and show that our model is sufficiently flexible to describe the average stellar mass histories of galaxies in both simulations with an accuracy of ∼ 0.1 dex across most of cosmic time. We use Diffstar to compare TNG to UM in common physical terms, finding that: (i) star formation in UM is less efficient and burstier relative to TNG; (ii) galaxies in UM have longer gas consumption timescales, τ cons , relative to TNG; (iii) rejuvenated star formation is ubiquitous in UM, whereas quenched TNG galaxies rarely experience sustained rejuvenation; and (iv) in both simulations, the distributions of ms , τ cons , and t q share a common characteristic dependence upon halo mass, and present significant correlations with halo assembly history. We conclude the paper with a discussion of how Diffstar can be used in future applications to fit the SEDs of individual observed galaxies, as well as in forward-modeling applications that populate cosmological simulations with synthetic galaxies.

Molecular Gas Reservoirs in Massive Quiescent Galaxies at z ∼ 0.7 Linked to Late-time Star Formation

“…This may be in agreement with the findings of Carnall et al (2018) that up to 60% of quiescent galaxies may have experienced low-level rejuvenation or a merger since z < 1.5 (see also Mancini et al 2019;Carleton et al 2020). Some evidence from theory and cosmological simulations implies that a similar fraction of massive galaxies undergo weak rejuvenations (10%-40% of massive halos with > M log 13 halo ), supporting the idea that weak rejuvenations are relatively common (Alarcon et al 2022). While the rejuvenations we measured are relatively small, they nonetheless contribute to the surprisingly large number of detectable reservoirs of molecular gas.…”

Recovering the Star Formation Histories of Recently Quenched Galaxies: The Impact of Model and Prior Choices

“…Additional flexibility in the shapes of galaxy SFHs has recently been made possible through advances in inference techniques allowing higher dimensional models: these nonparametric SFHs do not assume a specific analytic form for the SFH but instead allow for arbitrary SFRs in adjacent time bins (e.g., Conroy 2013;Iyer & Gawiser 2017;Iyer et al 2019;Leja et al 2019aLeja et al , 2019b; see also Alarcon et al 2022 for a flexible physically motivated parametric model).…”

Hierarchical Bayesian inference of photometric redshifts with stellar population synthesis models

“…Challenges that need to be overcome at this stage involve the computational needs of SPS calls, and the fact that they are not differentiable analytically (although differentiable SPS models are now being developed, Alarcon et al 2022;Hearin et al 2021). We address these issues with neural emulators of the model predictions, which are both ∼ 10 4 faster than native SPS calls and differentiable (Alsing et al 2020).…”