Date: 18.03.2025

Zofia Kaczmarek (Heidelberg,GER)

Optimising microlensing black hole yields

Abstract:
Although millions of isolated black holes are expected to reside in the Galaxy, only one detection has been confirmed so far. Studying a population of these objects would provide extremely valuable input for various fields, including stellar evolution, supernova physics or gravitational wave studies. The only known channel through which they can be detected is gravitational microlensing. Currently, only very small subsets of microlensing events can be chosen to allocate expensive and scarce follow-up resources that can confirm the identity of the lens. We have developed a new classifier that combines posterior constraints on microlensing parameters and a Galactic simulation to estimate the lens class probability. This method is fast, flexible, and relies only on information easily obtained from the lightcurve. It is also particularly efficient at differentiating black holes from other lens classes. Therefore, it is well-suited to be applied as a filter for large databases or real-time data streams, picking out the optimal black hole candidates for allocating additional resources. I will present our classification framework, the open-source software (`popclass`) that makes it easily accessible to the community, the results of its application to OGLE events, and plans for its extension.