Regulation of star formation:
from local to cosmic scales

Galaxies are complex objects: there physical nature is set by a range of processes acting on a wide range of spatial and temporal scales (see figure below). Nevertheless, the galaxy population shows regularity and scaling laws exist at most cosmic times. On of these scaling laws, the star-forming main sequence, is a rather tight relation between the mass and the star-formation rate (SFR) of star-forming galaxies (the most massive galaxies are quiescent and do not form stars, see here to learn more about quenching). A key implication of this tight main sequence relation is that star-forming galaxies sustain their SFRs for extended periods in quasi-steady state of gas inflow, gas outflow and gas consumption, rather than short-lived periods of merger-induced starburst peaks. The decline of the normalization of the main sequence with cosmic time can naturally be explained by the decline of gas accretion rate onto the galaxies.

Beside this coherency in the galaxy population, we still lack an understanding of the scatter of the star-forming main sequence, which encodes how star formation is regulated. In order to shed new light onto this, we have pioneered a new approach quantify fluctuations of the SFR on different timescales (power spectrum density). Different physical processes act on different timescales, allowing us to constrain from the power spectrum density the importance of different processing in driving star formation. We believe that this approach is particularly useful to learn about star-formation regulation in high-redshift galaxies, where spatially resolved information is not available. Our goal is therefore to temporally resolve galaxies!

Why is there a star-forming main sequence?

Using state-of-the-art cosmological zoom-in simulations, we find in Tacchella et al. (2016a) that the mechanisms of gas compaction, depletion and replenishment confine the star-forming galaxies to the narrow star-forming main sequence. The star-forming galaxies oscillate about the main sequence ridge. The propagation upwards is due to gas compaction, triggered, e.g., by mergers, counter-rotating streams, and/or violent disk instabilities. The downturn at the upper envelope is due to central gas depletion by peak star formation and outflows while inflow from the shrunken gas disk is suppressed. An upturn at the lower envelope can occur once the extended disk has been replenished by fresh gas and a new compaction can be triggered, namely as long as the replenishment time is shorter than the depletion time. Together with our companion paper, Tacchella et al. (2016b), we show that the spatial distribution of star formation and the build-up of bulges are linked to these galactic-scale oscillations, where bulges preferential form at the upper envelope of the star-forming main sequence (see morphology). Full quenching occurs in massive halos and/or at low redshifts, where the replenishment time is long compared to the depletion time (see here for more about quenching).

How to quantify the variability of star formation?

As described in Caplar & Tacchella (2019), we put forward the idea - inspired from AGN light curve modeling - to quantify fluctuation in the SFR of galaxies with the power spectrum density (PSD). We show that we can quantify the variability of star formation by measuring the scatter of a suite of SFRs probing different timescales for an ensemble of galaxies. Based on this technique, we find that star-formation histories of MW-like galaxies lose “memory” of their previous activity (i.e. decorrelate) on a time-scale of ~200 Myr. We are currently working on refining this methodology of measuring the PSD for galaxies.

How do physical processes shape the star-formation variability?

In Tacchella, Forbes & Caplar (2020) study the imprint of the life-cycle of molecular clouds and gas accretion of galaxies on the variability of star formation. We show that in the general case the PSD of the SFR has three breaks, corresponding to the correlation time of the inflow rate, the equilibrium timescale of the gas reservoir of the galaxy, and the average lifetime of individual molecular clouds. We explore the applicability of our method for understanding the star-formation process on cloud-scale from galaxy-integrated measurements (measurements such as JWST will deliver for galaxies at z>4).

How does the star-formation variability look in different galaxy formation models?

In Iyer, Tacchella, et al. (2020), we compiled the star-formation histories (SFHs) of galaxies at z=0 from an extensive set of simulations (Illustris, IllustrisTNG, Mufasa, Simba, Eagle, FIRE-2, g14, and Marvel/Justice League, Santa Cruz SAM, UniverseMachine) and quantified their variability on different timescales using the PSD formalism (see Caplar & Tacchella 2019). We show that the PSD framework is a very interesting parameter space to study the SFHs of galaxies since model predictions vary widely.