Uncertainty-Aware Blob Detection with an Application to Integrated-Light Stellar Population Recoveries
- Author(s)
- Fabian Parzer, Prashin Jethwa, Alina Boecker, Mayte Alfaro-Cuello, Otmar Scherzer, Glenn van de Ven
- 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.
- Organisation(s)
- Department of Mathematics, Department of Astrophysics
- External organisation(s)
- Max-Planck-Institut für Astronomie, Institute of Astrophysics of the Canary Islands, Universidad Central de Chile, Space Telescope Science Institute, Johann Radon Institute for Computational and Applied Mathematics (RICAM)
- Journal
- Astronomy & Astrophysics
- Volume
- 674
- No. of pages
- 18
- ISSN
- 0004-6361
- DOI
- https://doi.org/10.1051/0004-6361/202244739
- Publication date
- 08-2022
- Peer reviewed
- Yes
- Austrian Fields of Science 2012
- 103003 Astronomy, 103004 Astrophysics
- Keywords
- ASJC Scopus subject areas
- Astronomy and Astrophysics, Space and Planetary Science
- Portal url
- https://ucrisportal.univie.ac.at/en/publications/9e3b9cc5-d58d-49f5-9ca5-e0ceafc1e398