City University of New York
Understanding the physical properties of galaxies and how they change through cosmic time allows us to learn about the cosmic expansion, gravity, and the physical mechanisms that regulate the growth of structures. My work focuses on developing and using better tools to extract information about galaxy properties, such as stellar mass, star formation history, dust attenuation, chemical enrichment history, and redshift, using data from large multi-wavelength galaxy surveys. Several years ago, I created GalMC, the first publicly available Markov Chain Monte Carlo algorithm for spectral energy distribution (SED) fitting; since then, the astronomy community has made immense progress in modeling and fitting the stellar populations of galaxies. I will summarize some of the lessons we have learned in these years, and describe what in my opinion are the most significant lingering sources of systematic uncertainties. I will give an overview of our ongoing effort at extracting information from galaxy SEDs, in particular star formation histories and metallicity, using machine learning algorithms coupled with SED fitting and Bayesian model selection.