A data-driven Bayesian approach to finding young stellar populations in early-type galaxies from their UV-optical spectra

Efficient predictive models and data analysis techniques for the analysis of photometric and spectroscopic observations of galaxies are not only desirable, but also required, in view of the overwhelming quantities of data becoming available. We present the results of a novel application of Bayesian latent variable modelling techniques, where we have formulated a data-driven algorithm that allows one to explore the stellar populations of a large sample of galaxies from their spectra, without the application of detailed physical models. Our only assumption is that the galaxy spectrum can be expressed as a linear superposition of a small number of independent factors, each a spectrum of a stellar subpopulation that cannot be individually observed. A probabilistic latent variable architecture that explicitly encodes this assumption is then formulated, and a rigorous Bayesian methodology is employed for solving the inverse modelling problem from the available data. A powerful aspect of this method is that it formulates a density model of the spectra, based on which we can handle observational errors. Further, we can recover missing data both from the original set of spectra which might have incomplete spectral coverage of each galaxy, or from previously unseen spectra of th