Uncertainty-aware blob detection with an application to integrated-light stellar population recoveries

¹ Faculty of Mathematics, University of Vienna, Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria
² Department of Astrophysics, University of Vienna, Türkenschanzstraße 17, 1180 Vienna, Austria
e-mail: prashin.jethwa@univie.ac.at
³ Max-Planck Institut für Astronomie, Königstuhl 17, 69117 Heidelberg, Germany
⁴ Instituto de Astrofísica de Canarias, C/ Vía Láctea s/n, 38205 La Laguna, Spain
⁵ Facultad de Ingeniería y Arquitectura, Universidad Central de Chile, Av. Francisco de Aguirre 0405, La Serena, Coquimbo, Chile
⁶ Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
⁷ Johann Radon Institute for Computational and Applied Mathematics (RICAM), Altenbergerstraße 69, 4040 Linz, Austria
⁸ Christian Doppler Laboratory for Mathematical Modeling and Simulation of Next Generations of Ultrasound Devices (MaMSi), Oskar-Morgenstern-Platz 1, 1090 Vienna, Austria

Received: 11 August 2022
Accepted: 9 March 2023

Abstract

Context. Blob detection is a common problem in astronomy. One example is in stellar population modelling, where the distribution of stellar ages and metallicities in a galaxy is inferred from observations. In this context, blobs may correspond to stars born in situ versus those accreted from satellites, and the task of blob detection is to disentangle these components. A difficulty arises when the distributions come with significant uncertainties, as is the case for stellar population recoveries inferred from modelling spectra of unresolved stellar systems. There is currently no satisfactory method for blob detection with uncertainties.

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.