2025/06/04 by Pavel Kůs, Diana López Nacir, Kůs, Pavel +3
Physics and Astronomy · #Cosmology and Gravitation Theories #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #Dark Matter and Cosmic Phenomena #FOS: Physical sciences #High Energy Astrophysical Phenomena (astro-ph.HE) #Pulsars and Gravitational Waves Research
paper · pdf · doi:10.48550/arxiv.2506.04100
openalex publication_date 2025/06/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Ultra-light dark matter (ULDM) is a compelling candidate for cosmological dark matter. If ULDM interacts with ordinary matter, it can induce measurable, characteristic signals in pulsar-timing data because it causes the orbits of pulsars in binary systems to osculate. In this work, we investigate the potential of machine learning (ML) techniques to detect such ULDM signals. To this end, we construct three types of neural networks: an autoencoder, a binary classifier, and a multiclass classifier. We apply these methods to four theoretically well-motivated ULDM models: a linearly coupled scalar field, a quadratically coupled scalar field, a vector field and a tensor field. We show that the sensitivity achieved using ML methods is comparable to that of a semi-analytical Bayesian approach, which to date has only been applied to the linear scalar case. The ML approach is readily applicable to all four ULDM models and, in the case of the multiclass classifier, can distinguish between them. Our results, derived from simulated data, lay the foundation for future applications to real pulsar-timing observations.