SLUG - Stochastically Lighting Up Galaxies I: Methods and Validating Tests

TL;DR

SLUG is a new Monte Carlo-based code that models stochastic effects in stellar populations, enabling detailed analysis of low-mass galaxies and star clusters with validated accuracy and diverse outputs.

Contribution

The paper introduces SLUG, a novel tool that accurately simulates stochastic stellar populations, including clustering, evolution, and disruption effects, with validated results and public availability.

Findings

SLUG accurately reproduces starburst99 results in well-sampled regimes.

SLUG effectively models stochastic effects in low-mass stellar populations.

Outputs include detailed catalogs and photometric properties of simulated galaxies.

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 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 treats stochastic sampling properly 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 i) catalogs of star clusters and their properties, such as their stellar initial mass distributions and their photometric properties in a variety of filters, ii) two dimensional histograms of color-magnitude diagrams of every star in the simulation, iii) and 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 the SLUG's capabilities by presenting outputs in the stochastic regime. SLUG is publicly distributed through the website http://sites.google.com/site/runslug/.