Spectral energy distribution fitting with markov chain Monte Carlo: Methodology and application to z = 3.1 Lyα-emitting galaxies

Viviana Acquaviva, Eric Gawiser, Lucia Guaita

Research output: Contribution to journalArticle

54 Citations (Scopus)

Abstract

We present GalMC, a Markov Chain Monte Carlo (MCMC) algorithm designed to fit the spectral energy distributions (SEDs) of galaxies to infer physical properties such as age, stellar mass, dust reddening, metallicity, redshift, and star formation rate. We describe the features of the code and the extensive tests conducted to ensure that our procedure leads to unbiased parameter estimation and accurate evaluation of uncertainties. We compare its performance to grid-based algorithms, showing that the efficiency in CPU time is 100 times better for MCMC for a three-dimensional parameter space and increasing with the number of dimensions. We use GalMC to fit the stacked SEDs of two samples of Lyman alpha emitters (LAEs) at redshift z = 3.1. Our fit reveals that the typical LAE detected in the IRAC 3.6 μm band has age = 0.67 [0.37-1.81]Gyr and stellar mass = 3.2 [2.5-4.2] × 109 M·, while the typical LAE not detected at 3.6 μm has age = 0.06 [0.01-0.2]Gyr and stellar mass = 2 [1.1-3.4] × 108 M·. The SEDs of both stacks are consistent with the absence of dust. The data do not significantly prefer exponential with respect to constant star formation history. The stellar populations of these two samples are consistent with the previous study by Lai et al., with some differences due to the improved modeling of the stellar populations. A constraint on the metallicity of z = 3.1 LAEs from broadband photometry, requiring Z < Z· at 95% confidence, is found here for the first time.

Original languageEnglish (US)
Article number47
JournalAstrophysical Journal
Volume737
Issue number2
DOIs
StatePublished - Aug 20 2011

Fingerprint

Markov chains
Markov chain
spectral energy distribution
emitters
stellar mass
methodology
galaxies
dust
metallicity
energy
star formation rate
physical property
photometry
star formation
confidence
physical properties
grids
histories
history
broadband

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

Keywords

  • galaxies: fundamental parameters
  • galaxies: statistics
  • methods: statistical

Cite this

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abstract = "We present GalMC, a Markov Chain Monte Carlo (MCMC) algorithm designed to fit the spectral energy distributions (SEDs) of galaxies to infer physical properties such as age, stellar mass, dust reddening, metallicity, redshift, and star formation rate. We describe the features of the code and the extensive tests conducted to ensure that our procedure leads to unbiased parameter estimation and accurate evaluation of uncertainties. We compare its performance to grid-based algorithms, showing that the efficiency in CPU time is 100 times better for MCMC for a three-dimensional parameter space and increasing with the number of dimensions. We use GalMC to fit the stacked SEDs of two samples of Lyman alpha emitters (LAEs) at redshift z = 3.1. Our fit reveals that the typical LAE detected in the IRAC 3.6 μm band has age = 0.67 [0.37-1.81]Gyr and stellar mass = 3.2 [2.5-4.2] × 109 M·, while the typical LAE not detected at 3.6 μm has age = 0.06 [0.01-0.2]Gyr and stellar mass = 2 [1.1-3.4] × 108 M·. The SEDs of both stacks are consistent with the absence of dust. The data do not significantly prefer exponential with respect to constant star formation history. The stellar populations of these two samples are consistent with the previous study by Lai et al., with some differences due to the improved modeling of the stellar populations. A constraint on the metallicity of z = 3.1 LAEs from broadband photometry, requiring Z < Z· at 95{\%} confidence, is found here for the first time.",
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Spectral energy distribution fitting with markov chain Monte Carlo : Methodology and application to z = 3.1 Lyα-emitting galaxies. / Acquaviva, Viviana; Gawiser, Eric; Guaita, Lucia.

In: Astrophysical Journal, Vol. 737, No. 2, 47, 20.08.2011.

Research output: Contribution to journalArticle

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T1 - Spectral energy distribution fitting with markov chain Monte Carlo

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AU - Acquaviva, Viviana

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AU - Guaita, Lucia

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