POISSON CLUSTER PROCESS MODELS FOR DETECTING ULTRA-DIFFUSE GALAXIES

• 成果类型: Article
• 署名作者: Li, Dayi; Stringer, Alex; Brown, Patrick e.; Eadie, Gwendolyn m.; Abraham, Roberto g.
• 署名单位: University of Toronto; University of Waterloo; University of Toronto
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1958
• 发表日期: 2025
• 页码: 261-285
• 关键词: globular-clusters; point patterns; inference; systems

摘要

We propose a novel set of Poisson cluster process (PCP) models to detect ultra-diffuse galaxies (UDGs), a class of extremely faint, enigmatic galaxies of substantial interest in modern astrophysics. We model the unobserved UDG locations as parent points in a PCP and infer their positions based on the observed spatial point patterns of their old star cluster systems. Many UDGs have somewhere from a few to hundreds of these old star clusters, which we treat as offspring points in our models. We also present a new framework to construct a marked PCP model using the marks/characteristics of star clusters (their colors, brightnesses, etc.). The marked PCP model may enhance the detection of UDGs and offers broad applicability to problems in other disciplines. To assess the overall model performance, we design an innovative assessment tool for spatial prediction problems where only point-referenced ground truth is available, overcoming the limitation of standard ROC analyses where spatial Boolean reference maps are required. We construct a bespoke blocked Gibbs adaptive spatial birth-death-move Markov chain Monte Carlo algorithm to infer the locations of UDGs using real data from a Hubble Space Telescope imaging survey. Based on our performance assessment tool, our novel models significantly outperform existing approaches using the log-Gaussian Cox process. We also obtained preliminary evidence that the marked PCP model may improve UDG detection performance compared to the model without marks. Furthermore, we find evidence of a potential new dark galaxy that was not detected by previous methods.