On Finding Black Holes in Photometric Microlensing Surveys

• DOI: 10.3847/1538-4357/adb1d7
• Publication Date: 2025-03-07T17:30:21Z
• Authors: Zofia Kaczmarek; Peter McGill; Scott E. Perkins; William A. Dawson; Macy Huston; Ming-Feng Ho; Natasha S. Abrams; Jessica R. Lu
• Keywords: Gravitational microlensing

Abstract There are expected to be millions of isolated black holes in the galaxy resulting from deaths massive stars. Measuring abundance and properties this remnant population would shed light on end stages stellar evolution paths hole systems. Detecting is currently only possible via gravitational microlensing, which has so far yielded one definitive detection. The difficulty finding microlensing lies having choose a small subset events, based characteristics their curves, allocate expensive scarce follow-up resources confirm identity lens. Current methods either rely simple cuts parameter space without using full distribution information or effective subsets events. In paper, we present new lens classification method. classifier takes posterior constraints light-curve parameters combines them with Galactic simulation estimate class probability. This method flexible can used any set parameters, making it applicable large samples We make framework available popclass Python package. apply ∼10,000 events Optical Gravitational Lensing Experiment survey find 23 high-probability candidates. Our also suggests that known an observational outlier, according current models, allocation astrometric event was high-risk strategy.