Exploring Magnetic Fields in Molecular Clouds through Denoising Diffusion Probabilistic Models
Accurately measuring magnetic field strength in the interstellar medium, including giant molecular clouds, remains a significant challenge. We present a machine learning approach using denoising diffusion probabilistic models (DDPMs) to estimate magnetic field strength from synthetic observables suc...
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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/ada8a0 |
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| author | Duo Xu Jenna Karcheski Chi-Yan Law Ye Zhu Chia-Jung Hsu Jonathan C. Tan |
| author_facet | Duo Xu Jenna Karcheski Chi-Yan Law Ye Zhu Chia-Jung Hsu Jonathan C. Tan |
| author_sort | Duo Xu |
| collection | DOAJ |
| description | Accurately measuring magnetic field strength in the interstellar medium, including giant molecular clouds, remains a significant challenge. We present a machine learning approach using denoising diffusion probabilistic models (DDPMs) to estimate magnetic field strength from synthetic observables such as column density, orientation angles of the dust continuum polarization vector, and line-of-sight (LOS) nonthermal velocity dispersion. We trained three versions of the DDPM model: the 1-channel DDPM (using only column density), the 2-channel DDPM (incorporating both column density and polarization angles), and the 3-channel DDPM (which combines column density, polarization angles, and LOS nonthermal velocity dispersion). The code and trained model are available on GitHub at http://github.com/xuduo117/DDPM_Bmag . We assessed the models on both synthetic test samples and new simulation data that were outside the training set's distribution. The 3-channel DDPM consistently outperformed both the other DDPM variants and the power-law fitting approach based on column density alone, demonstrating its robustness in handling previously unseen data. Additionally, we compared the performance of the Davis–Chandrasekhar–Fermi (DCF) methods, both classical and modified, to the DDPM predictions. The classical DCF method overestimated the magnetic field strength by approximately an order of magnitude. Although the modified DCF method showed improvement over the classical version, it still fell short of the precision achieved by the 3-channel DDPM. |
| format | Article |
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| institution | Kabale University |
| issn | 1538-4357 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-4dad99ea2b434b2a895b08e1da32be4d2025-02-04T07:51:34ZengIOP PublishingThe Astrophysical Journal1538-43572025-01-0198015210.3847/1538-4357/ada8a0Exploring Magnetic Fields in Molecular Clouds through Denoising Diffusion Probabilistic ModelsDuo Xu0https://orcid.org/0000-0001-6216-8931Jenna Karcheski1Chi-Yan Law2https://orcid.org/0000-0003-1964-970XYe Zhu3https://orcid.org/0000-0003-0774-9375Chia-Jung Hsu4Jonathan C. Tan5https://orcid.org/0000-0002-3389-9142Department of Astronomy, University of Virginia , Charlottesville, VA 22904-4235, USA ; xuduo@cita.utoronto.ca; Canadian Institute for Theoretical Astrophysics, University of Toronto , 60 St. George Street, Toronto, ON M5S 3H8, CanadaUniversity of Wisconsin-Madison , Department of Astronomy, 475 N. Charter St, Madison, WI 53703, USADepartment of Space, Earth & Environment, Chalmers University of Technology , SE-412 96 Gothenburg, Sweden; European Southern Observatory , Karl-Schwarzschild-Strasse 2, D-85748 Garching, GermanyDepartment of Computer Science, Princeton University , Princeton, NJ 08544, USADepartment of Space, Earth & Environment, Chalmers University of Technology , SE-412 96 Gothenburg, SwedenDepartment of Astronomy, University of Virginia , Charlottesville, VA 22904-4235, USA ; xuduo@cita.utoronto.ca; Department of Space, Earth & Environment, Chalmers University of Technology , SE-412 96 Gothenburg, SwedenAccurately measuring magnetic field strength in the interstellar medium, including giant molecular clouds, remains a significant challenge. We present a machine learning approach using denoising diffusion probabilistic models (DDPMs) to estimate magnetic field strength from synthetic observables such as column density, orientation angles of the dust continuum polarization vector, and line-of-sight (LOS) nonthermal velocity dispersion. We trained three versions of the DDPM model: the 1-channel DDPM (using only column density), the 2-channel DDPM (incorporating both column density and polarization angles), and the 3-channel DDPM (which combines column density, polarization angles, and LOS nonthermal velocity dispersion). The code and trained model are available on GitHub at http://github.com/xuduo117/DDPM_Bmag . We assessed the models on both synthetic test samples and new simulation data that were outside the training set's distribution. The 3-channel DDPM consistently outperformed both the other DDPM variants and the power-law fitting approach based on column density alone, demonstrating its robustness in handling previously unseen data. Additionally, we compared the performance of the Davis–Chandrasekhar–Fermi (DCF) methods, both classical and modified, to the DDPM predictions. The classical DCF method overestimated the magnetic field strength by approximately an order of magnitude. Although the modified DCF method showed improvement over the classical version, it still fell short of the precision achieved by the 3-channel DDPM.https://doi.org/10.3847/1538-4357/ada8a0Interstellar mediumInterstellar magnetic fieldsAstrostatisticsAstrostatistics techniquesMolecular cloudsMagnetohydrodynamics |
| spellingShingle | Duo Xu Jenna Karcheski Chi-Yan Law Ye Zhu Chia-Jung Hsu Jonathan C. Tan Exploring Magnetic Fields in Molecular Clouds through Denoising Diffusion Probabilistic Models The Astrophysical Journal Interstellar medium Interstellar magnetic fields Astrostatistics Astrostatistics techniques Molecular clouds Magnetohydrodynamics |
| title | Exploring Magnetic Fields in Molecular Clouds through Denoising Diffusion Probabilistic Models |
| title_full | Exploring Magnetic Fields in Molecular Clouds through Denoising Diffusion Probabilistic Models |
| title_fullStr | Exploring Magnetic Fields in Molecular Clouds through Denoising Diffusion Probabilistic Models |
| title_full_unstemmed | Exploring Magnetic Fields in Molecular Clouds through Denoising Diffusion Probabilistic Models |
| title_short | Exploring Magnetic Fields in Molecular Clouds through Denoising Diffusion Probabilistic Models |
| title_sort | exploring magnetic fields in molecular clouds through denoising diffusion probabilistic models |
| topic | Interstellar medium Interstellar magnetic fields Astrostatistics Astrostatistics techniques Molecular clouds Magnetohydrodynamics |
| url | https://doi.org/10.3847/1538-4357/ada8a0 |
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