Spectral Synthesis with Empirical Priors
Laerte Sodré Junior · Redalyc (Universidad Autónoma del Estado de México) · 2017
"We have been developing a Bayesian parameter estimator whic h is very competitive compared with other machine learning methods, as evidenced by several experime nts performed by our group (e.g., on photometric redshifts and galaxy spectral synthesis). Our approach rel ies on a training set, i.e., a (empirical, theoretical or mixed) data set with known parameters, and outputs the pro bability distribution function of a certain parameter, as well as other statistical summaries of this di stribution, for all galaxies in the survey. We propose to build a large training set using theoretical libraries an d use them to derive galaxy parameters from S-PLUS, J-PLUS and J-PAS observations."