Towards Uncovering Dark Matter Effects on Neutron Star Properties: A Machine Learning Approach
Over the last few years, researchers have become increasingly interested in understanding how dark matter affects neutron stars, helping them to better understand complex astrophysical phenomena. In this paper, we delve deeper into this problem by using advanced machine learning techniques to find p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Online Access: | https://www.mdpi.com/2571-712X/7/1/5 |
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| author | Prashant Thakur Tuhin Malik Tarun Kumar Jha |
| author_facet | Prashant Thakur Tuhin Malik Tarun Kumar Jha |
| author_sort | Prashant Thakur |
| collection | DOAJ |
| description | Over the last few years, researchers have become increasingly interested in understanding how dark matter affects neutron stars, helping them to better understand complex astrophysical phenomena. In this paper, we delve deeper into this problem by using advanced machine learning techniques to find potential connections between dark matter and various neutron star characteristics. We employ Random Forest classifiers to analyze neutron star (NS) properties and investigate whether these stars exhibit characteristics indicative of dark matter admixture. Our dataset includes 32,000 sequences of simulated NS properties, each described by mass, radius, and tidal deformability, inferred using recent observations and theoretical models. We explore a two-fluid model for the NS, incorporating separate equations of state for nucleonic and dark matter, with the latter considering a fermionic dark matter scenario. Our classifiers are trained and validated in a variety of feature sets, including the tidal deformability for various masses. The performance of these classifiers is rigorously assessed using confusion matrices, which reveal that NS with admixed dark matter can be identified with approximately 17% probability of misclassification as nuclear matter NS. In particular, we find that additional tidal deformability data do not significantly improve the precision of our predictions. This article also delves into the potential of specific NS properties as indicators of the presence of dark matter. Radius measurements, especially at extreme mass values, emerge as particularly promising features. The insights gained from our study are pivotal for guiding future observational strategies and enhancing the detection capabilities of dark matter in NS. This study is the first to show that the radii of neutron stars at 1.4 and 2.07 solar masses, measured using NICER data from pulsars PSR J0030+0451 and PSR J0740+6620, strongly suggest that the presence of dark matter in a neutron star is more likely than only hadronic composition. |
| first_indexed | 2024-04-24T17:56:25Z |
| format | Article |
| id | doaj.art-bbf77ad43ed34a2aa9fc858f967a681c |
| institution | Directory Open Access Journal |
| issn | 2571-712X |
| language | English |
| last_indexed | 2024-04-24T17:56:25Z |
| publishDate | 2024-01-01 |
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| record_format | Article |
| series | Particles |
| spelling | doaj.art-bbf77ad43ed34a2aa9fc858f967a681c2024-03-27T13:58:44ZengMDPI AGParticles2571-712X2024-01-0171809510.3390/particles7010005Towards Uncovering Dark Matter Effects on Neutron Star Properties: A Machine Learning ApproachPrashant Thakur0Tuhin Malik1Tarun Kumar Jha2Department of Physics, BITS-Pilani, K. K. Birla Goa Campus, Sancoale 403726, Goa, IndiaCFisUC, Department of Physics, University of Coimbra, P-3004-516 Coimbra, PortugalDepartment of Physics, BITS-Pilani, K. K. Birla Goa Campus, Sancoale 403726, Goa, IndiaOver the last few years, researchers have become increasingly interested in understanding how dark matter affects neutron stars, helping them to better understand complex astrophysical phenomena. In this paper, we delve deeper into this problem by using advanced machine learning techniques to find potential connections between dark matter and various neutron star characteristics. We employ Random Forest classifiers to analyze neutron star (NS) properties and investigate whether these stars exhibit characteristics indicative of dark matter admixture. Our dataset includes 32,000 sequences of simulated NS properties, each described by mass, radius, and tidal deformability, inferred using recent observations and theoretical models. We explore a two-fluid model for the NS, incorporating separate equations of state for nucleonic and dark matter, with the latter considering a fermionic dark matter scenario. Our classifiers are trained and validated in a variety of feature sets, including the tidal deformability for various masses. The performance of these classifiers is rigorously assessed using confusion matrices, which reveal that NS with admixed dark matter can be identified with approximately 17% probability of misclassification as nuclear matter NS. In particular, we find that additional tidal deformability data do not significantly improve the precision of our predictions. This article also delves into the potential of specific NS properties as indicators of the presence of dark matter. Radius measurements, especially at extreme mass values, emerge as particularly promising features. The insights gained from our study are pivotal for guiding future observational strategies and enhancing the detection capabilities of dark matter in NS. This study is the first to show that the radii of neutron stars at 1.4 and 2.07 solar masses, measured using NICER data from pulsars PSR J0030+0451 and PSR J0740+6620, strongly suggest that the presence of dark matter in a neutron star is more likely than only hadronic composition.https://www.mdpi.com/2571-712X/7/1/5dark matter in NSmachine learning |
| spellingShingle | Prashant Thakur Tuhin Malik Tarun Kumar Jha Towards Uncovering Dark Matter Effects on Neutron Star Properties: A Machine Learning Approach Particles dark matter in NS machine learning |
| title | Towards Uncovering Dark Matter Effects on Neutron Star Properties: A Machine Learning Approach |
| title_full | Towards Uncovering Dark Matter Effects on Neutron Star Properties: A Machine Learning Approach |
| title_fullStr | Towards Uncovering Dark Matter Effects on Neutron Star Properties: A Machine Learning Approach |
| title_full_unstemmed | Towards Uncovering Dark Matter Effects on Neutron Star Properties: A Machine Learning Approach |
| title_short | Towards Uncovering Dark Matter Effects on Neutron Star Properties: A Machine Learning Approach |
| title_sort | towards uncovering dark matter effects on neutron star properties a machine learning approach |
| topic | dark matter in NS machine learning |
| url | https://www.mdpi.com/2571-712X/7/1/5 |
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