Astronomy and Astrophysics
Aims: We introduce a method for uncertainty-aware blob detection developed in the context of stellar population modelling of integrated-light spectra of stellar systems.
Methods: We developed a theory and computational tools for an uncertainty-aware version of the classic Laplacian-of-Gaussians method for blob detection, which we call ULoG. This identifies significant blobs considering a variety of scales. As a prerequisite to apply ULoG to stellar population modelling, we introduced a method for efficient computation of uncertainties for spectral modelling. This method is based on the truncated Singular Value Decomposition and Markov chain Monte Carlo sampling (SVD-MCMC).
Results: We applied the methods to data of the star cluster M 54. We show that the SVD-MCMC inferences match those from standard MCMC, but they are a factor 5-10 faster to compute. We apply ULoG to the inferred M 54 age/metallicity distributions, identifying between two or three significant, distinct populations amongst its stars.
We are a large, diverse, and very active research group aiming to provide a comprehensive picture for the formation of galaxies in the Universe. Rooted in detailed stellar population analysis, we are constantly exploring and developing new tools and ideas to understand how galaxies came to be what we now observe.