Dark Classification Matters: Searching for Primordial Black Holes with LSST

TL;DR

This paper forecasts how LSST microlensing data can constrain primordial black holes as dark matter, emphasizing the importance of classification accuracy and proposing effective algorithms for identifying true signals.

Contribution

It introduces a methodology for constraining primordial black holes using LSST microlensing data, highlighting the role of classification algorithms and false positive rate minimization.

Findings

Bayesian information criterion and Boosted Decision Tree outperform standard $$-test in classification.

Effective discrimination reduces false positives, improving PBH abundance constraints.

Simulated LSST data demonstrates the potential for competitive dark matter constraints.

Abstract

We present projected constraints on the abundance of primordial black holes (PBHs) as a constituent of dark matter, based on microlensing observations from the upcoming Legacy Survey of Space and Time (LSST) at the Vera C. Rubin Observatory. We use a catalogue of microlensing light curves simulated with Rubin Observatory's OpSims to demonstrate that competitive constraints crucially rely on minimising the false positive rate (FPR) of the classification algorithm. We propose the Bayesian information criterion and a Boosted Decision Tree as effective discriminators and compare their derived efficiency and FPR to a more standard $\chi^2$-test.