Machine Learning Approach for Estimating Magnetic Field Strength in Galaxy Clusters from Synchrotron Emission
Magnetic fields play a crucial role in various astrophysical processes within the intracluster medium, including heat conduction, cosmic-ray acceleration, and the generation of synchrotron radiation. However, measuring magnetic field strength is typically challenging due to the limited availability...
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IOP Publishing
2025-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/adeb7a |
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| author | Jiyao Zhang Yue Hu Alex Lazarian |
| author_facet | Jiyao Zhang Yue Hu Alex Lazarian |
| author_sort | Jiyao Zhang |
| collection | DOAJ |
| description | Magnetic fields play a crucial role in various astrophysical processes within the intracluster medium, including heat conduction, cosmic-ray acceleration, and the generation of synchrotron radiation. However, measuring magnetic field strength is typically challenging due to the limited availability of Faraday rotation measure sources. To address the challenge, we propose a novel method that employs Convolutional Neural Networks (CNNs) alongside synchrotron emission observations to estimate magnetic field strengths in galaxy clusters. Our CNN model is trained on either magnetohydrodynamic (MHD) turbulence simulations or MHD galaxy cluster simulations, which incorporate complex dynamics such as cluster mergers and sloshing motions. The results demonstrate that CNNs can effectively estimate magnetic field strengths with mean-squared error of approximately 0.135 µ G ^2 , 0.044 µ G ^2 , and 0.02 µ G ^2 for β = 100, 200, and 500 conditions, respectively. Additionally, we have confirmed that our CNN model remains robust against noise and variations in viewing angles with sufficient training, ensuring reliable performance under a wide range of observational conditions. We compare the CNN approach with the traditional magnetic field strength estimate method that assumes equipartition between cosmic-ray electron energy and magnetic field energy. In contrast to the equipartition method, this CNN approach relies on the morphological feature of synchrotron images, offering a new perspective for complementing traditional estimates and enhancing our understanding of cosmic-ray acceleration mechanisms. |
| format | Article |
| id | doaj-art-d13d178609ac4d65a5b6135c01db1fc7 |
| institution | Kabale University |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
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| series | The Astrophysical Journal |
| spelling | doaj-art-d13d178609ac4d65a5b6135c01db1fc72025-08-20T03:47:07ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-01989221710.3847/1538-4357/adeb7aMachine Learning Approach for Estimating Magnetic Field Strength in Galaxy Clusters from Synchrotron EmissionJiyao Zhang0Yue Hu1https://orcid.org/0000-0002-8455-0805Alex Lazarian2https://orcid.org/0000-0002-7336-6674Department of Mathematics, University of Pennsylvania , Philadelphia, PA 19104, USA ; jiyaoz@sas.upenn.eduInstitute for Advanced Study , 1 Einstein Drive, Princeton, NJ 08540, USA ; yuehu@ias.eduDepartment of Astronomy, University of Wisconsin-Madison , Madison, WI 53706, USA ; alazarian@facstaff.wisc.eduMagnetic fields play a crucial role in various astrophysical processes within the intracluster medium, including heat conduction, cosmic-ray acceleration, and the generation of synchrotron radiation. However, measuring magnetic field strength is typically challenging due to the limited availability of Faraday rotation measure sources. To address the challenge, we propose a novel method that employs Convolutional Neural Networks (CNNs) alongside synchrotron emission observations to estimate magnetic field strengths in galaxy clusters. Our CNN model is trained on either magnetohydrodynamic (MHD) turbulence simulations or MHD galaxy cluster simulations, which incorporate complex dynamics such as cluster mergers and sloshing motions. The results demonstrate that CNNs can effectively estimate magnetic field strengths with mean-squared error of approximately 0.135 µ G ^2 , 0.044 µ G ^2 , and 0.02 µ G ^2 for β = 100, 200, and 500 conditions, respectively. Additionally, we have confirmed that our CNN model remains robust against noise and variations in viewing angles with sufficient training, ensuring reliable performance under a wide range of observational conditions. We compare the CNN approach with the traditional magnetic field strength estimate method that assumes equipartition between cosmic-ray electron energy and magnetic field energy. In contrast to the equipartition method, this CNN approach relies on the morphological feature of synchrotron images, offering a new perspective for complementing traditional estimates and enhancing our understanding of cosmic-ray acceleration mechanisms.https://doi.org/10.3847/1538-4357/adeb7aGalaxy clustersIntracluster mediumMagnetic fieldsConvolutional neural networksRadio astronomy |
| spellingShingle | Jiyao Zhang Yue Hu Alex Lazarian Machine Learning Approach for Estimating Magnetic Field Strength in Galaxy Clusters from Synchrotron Emission The Astrophysical Journal Galaxy clusters Intracluster medium Magnetic fields Convolutional neural networks Radio astronomy |
| title | Machine Learning Approach for Estimating Magnetic Field Strength in Galaxy Clusters from Synchrotron Emission |
| title_full | Machine Learning Approach for Estimating Magnetic Field Strength in Galaxy Clusters from Synchrotron Emission |
| title_fullStr | Machine Learning Approach for Estimating Magnetic Field Strength in Galaxy Clusters from Synchrotron Emission |
| title_full_unstemmed | Machine Learning Approach for Estimating Magnetic Field Strength in Galaxy Clusters from Synchrotron Emission |
| title_short | Machine Learning Approach for Estimating Magnetic Field Strength in Galaxy Clusters from Synchrotron Emission |
| title_sort | machine learning approach for estimating magnetic field strength in galaxy clusters from synchrotron emission |
| topic | Galaxy clusters Intracluster medium Magnetic fields Convolutional neural networks Radio astronomy |
| url | https://doi.org/10.3847/1538-4357/adeb7a |
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