Galaxies are an amalgamation of several components (dark matter, stars, gas, and dust), constantly interacting with one another. This interaction is imprinted on the spectral energy distribution (SED) of a given galaxy. Through a detailed study of the panchromatic SED we can shed light on the astrophysical processes that regulate galaxy evolution. However, the current SED modeling approaches come with many caveats and limitations. One of the main issues is that the star-formation histories (SFH) are usually poorly modeled, with significant systematics on parameters as the stellar mass or star-formation rate as a result. Moreover, global SED fits yield parameters that can deviate systematically from more reliable spatially resolved SED fits.
We propose a full SED fitting of a representative sample of resolved galaxies, based on a combination of panchromatic imaging and spectroscopic data. We will adopt a fully Bayesian SED modeling framework and an innovative spatial image reconstruction technique to generate maps of the most important physical galaxy parameters. We will use these maps to quantify the bias between global and local estimates, to characterize dust scaling relations on both local and global scales, to investigate dust heating mechanisms in galaxies of different types, and to analyze the SFH on local scales. We will compare our results to those obtained using other methods. Our results can serve as interesting and original benchmark for galaxy evolution models.