Disentangling the Black Hole Mass Spectrum with Photometric Microlensing Surveys

TL;DR

This paper introduces a Bayesian method to classify and analyze dark compact objects, especially primordial black holes, via photometric microlensing surveys, improving population inference despite observational degeneracies.

Contribution

It presents a probabilistic framework that jointly models lens type and population characteristics, outperforming existing methods in identifying black hole subpopulations and constraining dark matter contributions.

Findings

Outperforms current black hole identification pipelines.

Jointly constrains primordial black hole contribution to dark matter (~25%).

Highlights difficulty in inferring the lower mass cutoff for stellar black holes.

Abstract

From the formation mechanisms of stars and compact objects to nuclear physics, modern astronomy frequently leverages surveys to understand populations of objects to answer fundamental questions. The population of dark and isolated compact objects in the Galaxy contains critical information related to many of these topics, but is only practically accessible via gravitational microlensing. However, photometric microlensing observables are degenerate for different types of lenses, and one can seldom classify an event as involving either a compact object or stellar lens on its own. To address this difficulty, we apply a Bayesian framework that treats lens type probabilistically and jointly with a lens population model. This method allows lens population characteristics to be inferred despite intrinsic uncertainty in the lens-class of any single event. We investigate this method's effectiveness on a simulated ground-based photometric survey in the context of characterizing a hypothetical population of primordial black holes (PBHs) with an average mass of $30 M_{\odot}$. On simulated data, our method outperforms current black hole (BH) lens identification pipelines and characterizes different subpopulations of lenses while jointly constraining the PBH contribution to dark matter to ${\approx}25$\%. Key to robust inference, our method can marginalize over population model uncertainty. We find the lower mass cutoff for stellar origin BHs, a key observable in understanding the BH mass gap, particularly difficult to infer in our simulations. This work lays the foundation for cutting-edge PBH abundance constraints to be extracted from current photometric microlensing surveys.