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We show that supersonic MHD turbulence yields a star formation rate (SFR) as low as observed in molecular clouds (MCs), for characteristic values of the free-fall time divided by the dynamical time, $t_{rm ff}/t_{rm dyn}$, the alfv{e}nic Mach number, ${cal M}_{rm a}$, and the sonic Mach number, ${cal M}_{rm s}$. Using a very large set of deep adaptive-mesh-refinement simulations, we quantify the dependence of the SFR per free-fall time, $epsilon_{rm ff}$, on the above parameters. Our main results are: i) $epsilon_{rm ff}$ decreases exponentially with increasing $t_{rm ff}/t_{rm dyn}$, but is insensitive to changes in ${cal M}_{rm s}$, for constant values of $t_{rm ff}/t_{rm dyn}$ and ${cal M}_{rm a}$. ii) Decreasing values of ${cal M}_{rm a}$ (stronger magnetic fields) reduce $epsilon_{rm ff}$, but only to a point, beyond which $epsilon_{rm ff}$ increases with a further decrease of ${cal M}_{rm a}$. iii) For values of ${cal M}_{rm a}$ characteristic of star-forming regions, $epsilon_{rm ff}$ varies with ${cal M}_{rm a}$ by less than a factor of two. We propose a simple star-formation law, based on the empirical fit to the minimum $epsilon_{rm ff}$, and depending only on $t_{rm ff}/t_{rm dyn}$: $epsilon_{rm ff} approx epsilon_{rm wind} exp(-1.6 ,t_{rm ff}/t_{rm dyn})$. Because it only depends on the mean gas density and rms velocity, this law is straightforward to implement in simulations and analytical models of galaxy formation and evolution.
We study the star formation (SF) law in 12 Galactic molecular clouds with ongoing high-mass star formation (HMSF) activity, as traced by the presence of a bright IRAS source and other HMSF tracers. We define the molecular cloud (MC) associated to eac
Recent observations have revealed massive galactic molecular outflows that may have physical conditions (high gas densities) required to form stars. Indeed, several recent models predict that such massive galactic outflows may ignite star formation w
In this paper, I review to what extent we can understand the photometric properties of star clusters, and of low-mass, unresolved galaxies, in terms of population synthesis models designed to describe `simple stellar populations (SSPs), i.e., groups
Deriving a simple, analytic galaxy star formation history (SFH) using observational data is a complex task without the proper tool to hand. We therefore present SNITCH, an open source code written in Python, developed to quickly (~2 minutes) infer th
We address a simple model where the Kennicutt-Schmidt (KS) relation between the macroscopic densities of star-formation rate (SFR, $rho_{rm sfr}$) and gas ($n$) in galactic discs emerges from self-regulation of the SFR via supernova feedback. It aris