Title: Fitting Analysis using Differential Evolution Optimization (FADO): Spectral population synthesis through genetic optimization under self-consistency boundary conditions,
Abstract: The goal of population spectral synthesis (PSS) is to decipher from the
spectrum of a galaxy the mass, age and metallicity of its constituent stellar
populations. This technique has been established as a 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. 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. 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 other unique elements in its
mathematical concept (e.g., optimization of the spectral library using
artificial intelligence, convergence test, quasi-parallelization) results in
key improvements with respect to computational efficiency and uniqueness of the
best-fitting SFHs.