Development of Bayesian methods for the characterization of galaxy star formation histories through stellar population synthesis models
Edoardo Rossi

TL;DR
This paper develops Bayesian methods to characterize galaxy star formation histories by analyzing spectral features and photometry, enabling improved constraints on SFH duration and extension from galaxy spectra.
Contribution
It introduces a novel Bayesian framework and spectral indices to quantify galaxy SFH duration, advancing beyond mean stellar age estimates.
Findings
Deltaage_n,min effectively distinguishes extended SFH from negligible duration.
Spectral features' dependence on SFH duration is consistent across a wide age range.
Uncertainty in SFH duration retrieval is reduced with higher SNR, but plateaus beyond SNR=100.
Abstract
This project establishes parameters to characterize galaxy Star Formation History (SFH) beyond mean stellar age. We use ages at which fixed star fractions form to characterize SFH duration. We define Deltaage_n = (age_10 - age_90)/age_50 for SFH extension, where age_10, age_90, age_50 correspond to ages when 10%, 90%, and 50% of stars formed. Probability distributions for observed galaxies use robust Bayesian statistics comparing observed and model spectral features. We create composite stellar population (CSP) libraries using SEDlibrary software, implementing varied SFH, metallicity, and dust properties. We compare spectra using ten features: five spectral indices (D4000n, [Hdelta+Hgamma], Hbeta, [Mg_2Fe], [MgFe]') and five SDSS ugriz photometric fluxes. First, we focus on limiting Deltaage_n distinguishing extended from negligible SFH duration, Deltaage_n,min (time resolution). Using…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Scientific Research and Discoveries
