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Microlensing signatures of extended dark objects using machine learning

Crispim Romao, Miguel; Croon, Djuna

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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.


Crispim Romao, M., & Croon, D. (2024). Microlensing signatures of extended dark objects using machine learning. Physical Review D, 109(12), Article 123004.

Journal Article Type Article
Acceptance Date Apr 26, 2024
Online Publication Date Jun 3, 2024
Publication Date Jun 3, 2024
Deposit Date Jul 8, 2024
Publicly Available Date Jul 8, 2024
Journal Physical Review D
Print ISSN 2470-0010
Electronic ISSN 2470-0029
Publisher American Physical Society
Peer Reviewed Peer Reviewed
Volume 109
Issue 12
Article Number 123004
Public URL


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