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.