Gamma-ray Blazar Classification using Machine Learning with Advanced Weight Initialization and Self-Supervised Learning Techniques
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866914629218729984 |
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| author | Bhatta, Gopal Gharat, Sarvesh Borthakur, Abhimanyu Kumar, Aman |
| author_facet | Bhatta, Gopal Gharat, Sarvesh Borthakur, Abhimanyu Kumar, Aman |
| contents | Machine learning has emerged as a powerful tool in the field of gamma-ray astrophysics. The algorithms can distinguish between different source types, such as blazars and pulsars, and help uncover new insights into the high-energy universe. The Large Area Telescope (LAT) on-board the Fermi Gamma-ray telescope has significantly advanced our understanding of the Universe. The instrument has detected a large number of gamma-ray emitting sources, among which a significant number of objects have been identified as active galactic nuclei (AGN). The sample is primarily composed of blazars; however, more than one-third of these sources are either of an unknown class or lack a definite association with a low-energy counterpart. In this work, we employ multiple machine learning algorithms to classify the sources based on their other physical properties. In particular, we utilized smart initialisation techniques and self-supervised learning for classifying blazars into BL Lacertae objects (BL Lac) and flat spectrum radio quasars (FSRQ). The core advantage of the algorithm is its simplicity, usage of minimum number of features and easy deployment due to lesser number of parameters without compromising on the performance. The model predicts that out of the 1115 sources of uncertain type in the 4FGL-DR3 catalog, 820 can be classified as BL Lacs, and 295 can be classified as FSRQs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_06095 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Gamma-ray Blazar Classification using Machine Learning with Advanced Weight Initialization and Self-Supervised Learning Techniques Bhatta, Gopal Gharat, Sarvesh Borthakur, Abhimanyu Kumar, Aman High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics Machine learning has emerged as a powerful tool in the field of gamma-ray astrophysics. The algorithms can distinguish between different source types, such as blazars and pulsars, and help uncover new insights into the high-energy universe. The Large Area Telescope (LAT) on-board the Fermi Gamma-ray telescope has significantly advanced our understanding of the Universe. The instrument has detected a large number of gamma-ray emitting sources, among which a significant number of objects have been identified as active galactic nuclei (AGN). The sample is primarily composed of blazars; however, more than one-third of these sources are either of an unknown class or lack a definite association with a low-energy counterpart. In this work, we employ multiple machine learning algorithms to classify the sources based on their other physical properties. In particular, we utilized smart initialisation techniques and self-supervised learning for classifying blazars into BL Lacertae objects (BL Lac) and flat spectrum radio quasars (FSRQ). The core advantage of the algorithm is its simplicity, usage of minimum number of features and easy deployment due to lesser number of parameters without compromising on the performance. The model predicts that out of the 1115 sources of uncertain type in the 4FGL-DR3 catalog, 820 can be classified as BL Lacs, and 295 can be classified as FSRQs. |
| title | Gamma-ray Blazar Classification using Machine Learning with Advanced Weight Initialization and Self-Supervised Learning Techniques |
| topic | High Energy Astrophysical Phenomena Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2310.06095 |