Two major limitations, often neglected, affect this type of analysis: the adoption of oversimplified models to describe the wide variety of observed galaxy SEDs and the presence of ‘systematic’ model uncertainties. This second limitation has been addressed in several studies already (e.g. Charlot, Worthey & Bressan 1996; Cerviño, Luridiana & Castander 2000; Conroy, Gunn & White 2009; Percival & Salaris 2009; Conroy & Gunn 2010; Conroy, White & Gunn 2010a). The difficulty of precisely quantifying systematic model uncertainties has led to mainly qualitative conclusions, leaving the problem unsolved. The first limitation is easier to tackle, for example, by using more physically realistic models of galaxy SEDs and combining these with advanced statistical techniques to extract physical constraints from data. This appears as the most promising route to fully exploit the information gathered by modern photometric and spectroscopic galaxy surveys. Yet, the several tools proposed so far to interpret galaxy SEDs in terms of physical parameters do not allow one to fully exploit the high quality of modern data. For example, most existing approaches rely on the adoption of a rigid physical model (e.g. analytic, two-parameter star formation histories combined with a standard dust attenuation curve and the assumption that all stars in a galaxy have the same metallicity) to describe galaxy SEDs (e.g. Bolzonella et al. 2010; Wuyts et al. 2011; Hernán-Caballero et al. 2013; Ilbert et al. 2013; Bauer et al. 2013; Muzzin et al. 2013; Lundgren et al. 2014; Kochiashvili et al. 2015; Mortlock et al. 2015; Kawinwanichakij et al. 2016). Even with the inclusion of superimposed bursts of star formation (e.g. Kauffmann et al. 2003; Gallazzi et al. 2005; Pozzetti et al. 2007; Gallazzi & Bell 2009; da Cunha et al. 2010), this does not allow a physically consistent description of the contributions by stars, gas and dust to the integrated emission from a galaxy, nor the inclusion of a potential AGN component (a notable exception is the approach of Pacifici et al. 2012, who incorporate star formation and chemical enrichment histories from numerical simulations of galaxy formation and emission from photoionized gas). Also, current spectral analysis tools are generally optimized to interpret either photometric or spectroscopic observations of galaxies, but not arbitrary combinations thereof. Finally, most existing tools suffer from additional limitations: many focus on the selection of ‘best-fitting’ parameters rather than on the uncertainties associated with these parameters (e.g. chi-square minimization techniques; Arnouts et al. 1999; Bolzonella, Miralles & Pelló 2000; Kriek et al. 2009); when this is not the case, the number of free parameters that can be explored is generally limited (e.g. with grid-based Bayesian techniques; da Cunha, Charlot & Elbaz 2008; Noll et al. 2009; Pacifici et al. 2012); and when more sophisticated (e.g. Markov Chain Monte Carlo, hereafter MCMC) techniques allow the exploration of more parameters, instrumental effects are generally not incorporated in the analysis (e.g. Acquaviva, Gawiser & Guaita 2011; Serra et al. 2011; Han & Han 2014).
Citation: Kochiashvili et al. 2015
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