This article has an erratum: [erratum]
Volume 603, July 2017
|Number of page(s)||25|
|Published online||07 July 2017|
Fitting Analysis using Differential evolution Optimization (FADO):
Spectral population synthesis through genetic optimization under self-consistency boundary conditions⋆
Instituto de Astrofísica e Ciências do Espaço, Universidade do Porto, CAUP, Rua das Estrelas, 4150-762 Porto, Portugal
e-mail: email@example.com; firstname.lastname@example.org
Received: 24 May 2016
Accepted: 2 March 2017
The goal of population spectral synthesis (pss; also referred to as inverse, semi-empirical evolutionary- or fossil record approach) is to decipher from the spectrum of a galaxy the mass, age and metallicity of its constituent stellar populations. This technique, which is the reverse of but complementary to evolutionary synthesis, has been established as fundamental tool in extragalactic research. It has been extensively applied to large spectroscopic data sets, notably the SDSS, leading to important insights into the galaxy assembly history. However, despite significant improvements over the past decade, all current pss codes suffer from two major deficiencies that inhibit us from gaining sharp insights into the star-formation history (SFH) of galaxies and potentially introduce substantial biases in studies of their physical properties (e.g., stellar mass, mass-weighted stellar age and specific star formation rate). These are i) the neglect of nebular emission in spectral fits, consequently; ii) the lack of a mechanism that ensures consistency between the best-fitting SFH and the observed nebular emission characteristics of a star-forming (SF) galaxy (e.g., hydrogen Balmer-line luminosities and equivalent widths-EWs, shape of the continuum in the region around the Balmer and Paschen jump). In this article, we present fado (Fitting Analysis using Differential evolution Optimization) – a conceptually novel, publicly available pss tool with the distinctive capability of permitting identification of the SFH that reproduces the observed nebular characteristics of a SF galaxy. This so-far unique self-consistency concept allows us to significantly alleviate degeneracies in current spectral synthesis, thereby opening a new avenue to the exploration of the assembly history of galaxies. The innovative character of fado is further augmented by its mathematical foundation: fado is the first pss code employing genetic differential evolution optimization. This, in conjunction with various other currently unique elements in its mathematical concept and numerical realization (e.g., mid-analysis optimization of the spectral library using artificial intelligence, test for convergence through a procedure inspired by Markov chain Monte Carlo techniques, quasi-parallelization embedded within a modular architecture) results in key improvements with respect to computational efficiency and uniqueness of the best-fitting SFHs. Furthermore, fado incorporates within a single code the entire chain of pre-processing, modeling, post-processing, storage and graphical representation of the relevant output from pss, including emission-line measurements and estimates of uncertainties for all primary and secondary products from spectral synthesis (e.g., mass contributions of individual stellar populations, mass- and luminosity-weighted stellar ages and metallicities). This integrated concept greatly simplifies and accelerates a lengthy sequence of individual time-consuming steps that are generally involved in pss modeling, further enhancing the overall efficiency of the code and inviting to its automated application to large spectroscopic data sets.
Key words: galaxies: evolution / galaxies: star formation / galaxies: starburst / galaxies: stellar content / galaxies: fundamental parameters / methods: numerical
The distribution package of the FADO v.1 tool contains the binary and its auxiliary files. FADO v.1 is only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (184.108.40.206) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/603/A63
© ESO, 2017
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