Year: 2012
Type: article
Abstract: The effects of stochasticity on the luminosities of stellar populations are an often neglected but crucial element for understanding populations in the low-mass or the low star formation rate regime. To address this issue, we present SLUG, a new code to "Stochastically Light Up Galaxies." SLUG synthesizes stellar populations using a Monte Carlo technique that properly treats stochastic sampling including the effects of clustering, the stellar initial mass function, star formation history, stellar evolution, and cluster disruption. This code produces many useful outputs, including (1) catalogs of star clusters and their properties such as their stellar initial mass distributions and their photometric properties in a variety of filters, (2) two dimensional histograms of color–magnitude diagrams of every star in the simulation, and (3) the photometric properties of field stars and the integrated photometry of the entire simulated galaxy. After presenting the SLUG algorithm in detail, we validate the code through comparisons with STARBURST99 in the well-sampled regime, and with observed photometry of Milky Way clusters. Finally, we demonstrate SLUG's capabilities by presenting outputs in the stochastic regime. SLUG is publicly distributed through the Web site http://sites.google.com/site/runslug/.
Source: The Astrophysical Journal
Authors: Robert L. da Silva, Michele Fumagalli, Mark Krumholz
Institution: University of California, Santa Cruz
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Awards: THE FORMATION OF HIGH-MASS STARS AND THEIR FEEDBACK EFFECTS THE FORMATION OF MASSIVE STARS REMAINS ONE OF THE MOST SIGNIFICANT UNSOLVED PROBLEMS IN, CAREER: Enriching the Universe -- Investigating Nucleosynthesis and Galaxy Formation in the First Few Billion Years, CAREER: Toward a Predictive Theory of Star Formation, G6671297155, The Hidden Lives of Massive Protostars: Fragmentation, Disks, and Feedback
OpenAlex ID: W2053936073