A probabilistic formulation for Empirical Population Synthesis: Sampling methods and tests

Details A probabilistic formulation for Empirical Population Synthesis: Sampling methods and tests A probabilistic formulation for Empirical Population Synthesis: Sampling methods and tests

2001-02-04

arxiv.org/abs/astro-ph/0102061v1

Mon.Not.Roy.Astron.Soc. 325 (2001) 60

Astronomy and Astrophysics

Astrophysics

22 pages, 12 figs. MNRAS accepted

Scientific paper

10.1046/j.1365-8711.2001.04366.x

We present a probabilistic formulation of the classical problem of synthesizing spectral properties of a galaxy using a base of star clusters. The problem consists of estimating the population vector x, composed by the contributions of n_star base elements to the integrated spectrum of a galaxy, and the extinction A_V, given a set of absorption line equivalent widths and continuum colors. The formalism is applied to the n_star = 12 base defined by Schmidt etal and subsequently used in several studies. The 13-D parameter space is explored with a Markov chain Monte Carlo sampling scheme based on the Metropolis algorithm, which produces a smooth and efficient mapping of the P(x,A_V) probability distribution. This version of Empirical Population Synthesis is used to investigate the ability to recover the detailed history of star-formation and chemical evolution using this spectral base. This is studied as a function of (1) the magnitude of the measurement errors and (2) the set of observables used in the synthesis. Only for extremely high S/N all 12 base proportions can be accurately recovered, though the observables are very precisely reproduced for any S/N. Furthermore, the individual mean x components are biased in the sense that components which carry a large fraction of the light tend to share their contribution preferably among components of same age. This compensation effect is linked to noise-induced linear dependences in the base, which very effectively redistribute the likelihood in x-space. The age distribution, however, can be satisfactorily recovered for realistic data quality. (abridged)

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