paper

Stochastic modeling of star-formation histories I: the scatter of the star-forming main sequence

arXiv:1901.07556 · doi:10.1093/mnras/stz1449

Abstract

We present a framework for modelling the star-formation histories of galaxies as a stochastic process. We define this stochastic process through a power spectrum density with a functional form of a broken power-law. Star-formation histories are correlated on short timescales, the strength of this correlation described by a power-law slope, , and they decorrelate to resemble white noise over a timescale that is proportional to the timescale of the break in the power spectrum density, . We use this framework to explore the properties of the stochastic process that, we assume, gives rise to the log-normal scatter about the relationship between star-formation rate and stellar mass, the so-called galaxy star-forming main sequence. Specifically, we show how the measurements of the normalisation and width () of the main sequence, measured in several passbands that probe different timescales, give a constraint on the parameters of the underlying power spectrum density. We first derive these results analytically for a simplified case where we model observations by averaging over the recent star-formation history. We then run numerical simulations to find results for more realistic observational cases. As a proof of concept, we use observational estimates of the main sequence scatter at and measured in H, UV+IR and the u-band, and show that combination of these point to Myr, when assuming . This implies that star-formation histories of galaxies lose "memory" of their previous activity on a timescale of Myr, highlighting the importance of baryonic effects that act over the dynamical timescales of galaxies.

arXiv comment24 pages, 15 figures, submitted to MNRAS, comments welcome

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