Microlensing signatures of extended dark objects using machine learning

Abstract

This paper presents a machine learning-based method for the detection of the unique gravitational microlensing signatures of extended dark objects, such as boson stars, axion miniclusters and subhalos. We adapt MicroLIA, a machine learning-based package tailored to handle the challenges posed by low-cadence data in microlensing surveys. Using realistic observational time stamps, our models are trained on simulated light curves to distinguish between microlensing by pointlike and extended lenses, as well as from other object classes which give a variable magnitude. We focus on boson stars and Navarro-Frenk-White (NFW) subhalos and show that the former, which are examples of objects with a relatively flat mass distribution, can be confidently identified for 0.8≲r/rE≲3. Intriguingly, we also find that more sharply peaked structures, such as NFW subhalos, can be distinctly recognized from point lenses under regular observation cadence. Our findings significantly advance the potential of microlensing data in uncovering the elusive nature of extended dark objects. The code and dataset used are also provided.