Astronomy and Astrophysics – Astronomy
Scientific paper
Jan 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011aas...21740704a&link_type=abstract
American Astronomical Society, AAS Meeting #217, #407.04; Bulletin of the American Astronomical Society, Vol. 43, 2011
Astronomy and Astrophysics
Astronomy
Scientific paper
The analysis of Spectral Energy Distributions (SEDs) of faraway galaxies provides us with valuable information on how the structures in the Universe evolved into what we see today. This requires a correct interpretation of data which are constantly improving in volume and precision, which can only be done by developing adequately sophisticated instruments of statistical analysis. We present our Markov Chain Monte Carlo (MCMC) algorithm, which is able to sample large parameter spaces and complicated star formation histories efficiently and can handle multiple stellar populations. This instrument is key for obtaining reliable estimates of SED parameters (e.g. age, stellar mass, dust content) and their uncertainties. It also reveals degeneracies between parameters and illustrates which physical quantities are best suited to describe certain samples of galaxies. We apply this method to the sample of 250 z = 2.1 Lyman Alpha Emitters (LAEs) from Guaita et al (2010a). High-redshift LAEs are of great interest because they probe the faint end of the galaxy luminosity function, where the bulk of galaxies reside, and have been shown to be building blocks of Milky-Way type galaxies today. This analysis complements the ones presented for z=3.1 LAEs in Gawiser et al (2007) and for a number of subsamples of the same z=2.1 LAE sample in Guaita et al (2010b), which were carried out using a grid-based maximum likelihood method. Our results confirm and strengthen the findings that LAEs at z = 2.1 have similar stellar masses to, but are dustier than, z=3.1 LAEs; typical values are respectively M* 5*108 MSun and Av 0.9. The current data don't allow us to discriminate among different star formation histories. We gratefully acknowledge support from NSF, DOE and NASA.
Acquaviva Viviana
Gawiser Eric
Guaita Lucia
Padilla Nelson
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