Return to [Log-]Normalcy: Rethinking Quenching, The Star Formation Main Sequence, and Perhaps Much More

TL;DR

This paper introduces a simple lognormal star formation history model that can replicate key galaxy evolution observations, challenging traditional grow-and-quench paradigms and emphasizing the importance of exploring alternative models.

Contribution

The study demonstrates that a physics-free, lognormal SFH model can reproduce multiple galaxy evolution data sets, offering a new perspective on galaxy quenching and evolution.

Findings

Model reproduces stellar mass functions up to z=8

Captures the slope of the star formation main sequence up to z=6

Explains the correlation between formation timescale and sSFR

Abstract

Knowledge of galaxy evolution rests on cross-sectional observations of different objects at different times. Understanding of galaxy evolution rests on longitudinal interpretations of how these data relate to individual objects moving through time. The connection between the two is often assumed to be clear, but we use a simple "physics-free" model to show that it is not, and that exploring its nuances can yield new insights. Comprising nothing more than $2094$ loosely constrained lognormal star formation histories (SFHs), the model faithfully reproduces the following data it was not designed to match: stellar mass functions at $z\leq8$; the slope of the star formation rate/stellar mass relation (the SF "Main Sequence") at $z\leq6$; the mean ${\rm sSFR}(\equiv{\rm SFR}/M_*)$ of low-mass galaxies at $z\leq7$; "fast-" and "slow-track" quenching; downsizing; and a correlation between formation timescale and ${\rm sSFR}(M_*; t)$ similar to results from simulations that provides a natural connection to bulge growth. We take these findings---which suggest that quenching is the natural downturn of all SFHs affecting galaxies at rates/times correlated with their densities---to mean that: (1) models in which galaxies are diversified on Hubble timescales by something like initial conditions rival the dominant grow-and-quench framework as good descriptions of the data; or (2) absent spatial information, many metrics of galaxy evolution are too undiscriminating---if not inherently misleading---to confirm a unique explanation. We outline future tests of our model but stress that, even if ultimately incorrect, it illustrates how exploring different paradigms can aid learning and, we hope, more detailed modeling efforts.