Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds
We adopt the deep learning method casi-3d (convolutional approach to structure identification-3D) to infer the orientation of magnetic fields in sub-/trans-Alfvénic turbulent clouds from molecular line emission. We carry out magnetohydrodynamic simulations with different magnetic field strengths and...
Ausführliche Beschreibung
Autor*in: |
Duo Xu [verfasserIn] Chi-Yan Law [verfasserIn] Jonathan C. Tan [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2023 |
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Schlagwörter: |
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Übergeordnetes Werk: |
In: The Astrophysical Journal - IOP Publishing, 2022, 942(2023), 2, p 95 |
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Übergeordnetes Werk: |
volume:942 ; year:2023 ; number:2, p 95 |
Links: |
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DOI / URN: |
10.3847/1538-4357/aca66c |
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Katalog-ID: |
DOAJ089168275 |
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520 | |a We adopt the deep learning method casi-3d (convolutional approach to structure identification-3D) to infer the orientation of magnetic fields in sub-/trans-Alfvénic turbulent clouds from molecular line emission. We carry out magnetohydrodynamic simulations with different magnetic field strengths and use these to generate synthetic observations. We apply the 3D radiation transfer code radmc-3d to model ^12 CO and ^13 CO (J = 1−0) line emission from the simulated clouds and then train a casi-3d model on these line emission data cubes to predict magnetic field morphology at the pixel level. The trained casi-3d model is able to infer magnetic field directions with a low error (≲10° for sub-Alfvénic samples and ≲30° for trans-Alfvénic samples). We further test the performance of casi-3d on a real sub-/trans- Alfvénic region in Taurus. The casi-3d prediction is consistent with the magnetic field direction inferred from Planck dust polarization measurements. We use our developed methods to produce a new magnetic field map of Taurus that has a three times higher angular resolution than the Planck map. | ||
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10.3847/1538-4357/aca66c doi (DE-627)DOAJ089168275 (DE-599)DOAJea7eea4c0335486b83d7ba4a24e11a36 DE-627 ger DE-627 rakwb eng QB460-466 Duo Xu verfasserin aut Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We adopt the deep learning method casi-3d (convolutional approach to structure identification-3D) to infer the orientation of magnetic fields in sub-/trans-Alfvénic turbulent clouds from molecular line emission. We carry out magnetohydrodynamic simulations with different magnetic field strengths and use these to generate synthetic observations. We apply the 3D radiation transfer code radmc-3d to model ^12 CO and ^13 CO (J = 1−0) line emission from the simulated clouds and then train a casi-3d model on these line emission data cubes to predict magnetic field morphology at the pixel level. The trained casi-3d model is able to infer magnetic field directions with a low error (≲10° for sub-Alfvénic samples and ≲30° for trans-Alfvénic samples). We further test the performance of casi-3d on a real sub-/trans- Alfvénic region in Taurus. The casi-3d prediction is consistent with the magnetic field direction inferred from Planck dust polarization measurements. We use our developed methods to produce a new magnetic field map of Taurus that has a three times higher angular resolution than the Planck map. Interstellar medium Interstellar magnetic fields Convolutional neural networks Molecular clouds Magnetohydrodynamics Astrophysics Chi-Yan Law verfasserin aut Jonathan C. Tan verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 942(2023), 2, p 95 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:942 year:2023 number:2, p 95 https://doi.org/10.3847/1538-4357/aca66c kostenfrei https://doaj.org/article/ea7eea4c0335486b83d7ba4a24e11a36 kostenfrei https://doi.org/10.3847/1538-4357/aca66c kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 942 2023 2, p 95 |
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10.3847/1538-4357/aca66c doi (DE-627)DOAJ089168275 (DE-599)DOAJea7eea4c0335486b83d7ba4a24e11a36 DE-627 ger DE-627 rakwb eng QB460-466 Duo Xu verfasserin aut Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We adopt the deep learning method casi-3d (convolutional approach to structure identification-3D) to infer the orientation of magnetic fields in sub-/trans-Alfvénic turbulent clouds from molecular line emission. We carry out magnetohydrodynamic simulations with different magnetic field strengths and use these to generate synthetic observations. We apply the 3D radiation transfer code radmc-3d to model ^12 CO and ^13 CO (J = 1−0) line emission from the simulated clouds and then train a casi-3d model on these line emission data cubes to predict magnetic field morphology at the pixel level. The trained casi-3d model is able to infer magnetic field directions with a low error (≲10° for sub-Alfvénic samples and ≲30° for trans-Alfvénic samples). We further test the performance of casi-3d on a real sub-/trans- Alfvénic region in Taurus. The casi-3d prediction is consistent with the magnetic field direction inferred from Planck dust polarization measurements. We use our developed methods to produce a new magnetic field map of Taurus that has a three times higher angular resolution than the Planck map. Interstellar medium Interstellar magnetic fields Convolutional neural networks Molecular clouds Magnetohydrodynamics Astrophysics Chi-Yan Law verfasserin aut Jonathan C. Tan verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 942(2023), 2, p 95 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:942 year:2023 number:2, p 95 https://doi.org/10.3847/1538-4357/aca66c kostenfrei https://doaj.org/article/ea7eea4c0335486b83d7ba4a24e11a36 kostenfrei https://doi.org/10.3847/1538-4357/aca66c kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 942 2023 2, p 95 |
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10.3847/1538-4357/aca66c doi (DE-627)DOAJ089168275 (DE-599)DOAJea7eea4c0335486b83d7ba4a24e11a36 DE-627 ger DE-627 rakwb eng QB460-466 Duo Xu verfasserin aut Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We adopt the deep learning method casi-3d (convolutional approach to structure identification-3D) to infer the orientation of magnetic fields in sub-/trans-Alfvénic turbulent clouds from molecular line emission. We carry out magnetohydrodynamic simulations with different magnetic field strengths and use these to generate synthetic observations. We apply the 3D radiation transfer code radmc-3d to model ^12 CO and ^13 CO (J = 1−0) line emission from the simulated clouds and then train a casi-3d model on these line emission data cubes to predict magnetic field morphology at the pixel level. The trained casi-3d model is able to infer magnetic field directions with a low error (≲10° for sub-Alfvénic samples and ≲30° for trans-Alfvénic samples). We further test the performance of casi-3d on a real sub-/trans- Alfvénic region in Taurus. The casi-3d prediction is consistent with the magnetic field direction inferred from Planck dust polarization measurements. We use our developed methods to produce a new magnetic field map of Taurus that has a three times higher angular resolution than the Planck map. Interstellar medium Interstellar magnetic fields Convolutional neural networks Molecular clouds Magnetohydrodynamics Astrophysics Chi-Yan Law verfasserin aut Jonathan C. Tan verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 942(2023), 2, p 95 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:942 year:2023 number:2, p 95 https://doi.org/10.3847/1538-4357/aca66c kostenfrei https://doaj.org/article/ea7eea4c0335486b83d7ba4a24e11a36 kostenfrei https://doi.org/10.3847/1538-4357/aca66c kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 942 2023 2, p 95 |
allfieldsGer |
10.3847/1538-4357/aca66c doi (DE-627)DOAJ089168275 (DE-599)DOAJea7eea4c0335486b83d7ba4a24e11a36 DE-627 ger DE-627 rakwb eng QB460-466 Duo Xu verfasserin aut Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We adopt the deep learning method casi-3d (convolutional approach to structure identification-3D) to infer the orientation of magnetic fields in sub-/trans-Alfvénic turbulent clouds from molecular line emission. We carry out magnetohydrodynamic simulations with different magnetic field strengths and use these to generate synthetic observations. We apply the 3D radiation transfer code radmc-3d to model ^12 CO and ^13 CO (J = 1−0) line emission from the simulated clouds and then train a casi-3d model on these line emission data cubes to predict magnetic field morphology at the pixel level. The trained casi-3d model is able to infer magnetic field directions with a low error (≲10° for sub-Alfvénic samples and ≲30° for trans-Alfvénic samples). We further test the performance of casi-3d on a real sub-/trans- Alfvénic region in Taurus. The casi-3d prediction is consistent with the magnetic field direction inferred from Planck dust polarization measurements. We use our developed methods to produce a new magnetic field map of Taurus that has a three times higher angular resolution than the Planck map. Interstellar medium Interstellar magnetic fields Convolutional neural networks Molecular clouds Magnetohydrodynamics Astrophysics Chi-Yan Law verfasserin aut Jonathan C. Tan verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 942(2023), 2, p 95 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:942 year:2023 number:2, p 95 https://doi.org/10.3847/1538-4357/aca66c kostenfrei https://doaj.org/article/ea7eea4c0335486b83d7ba4a24e11a36 kostenfrei https://doi.org/10.3847/1538-4357/aca66c kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 942 2023 2, p 95 |
allfieldsSound |
10.3847/1538-4357/aca66c doi (DE-627)DOAJ089168275 (DE-599)DOAJea7eea4c0335486b83d7ba4a24e11a36 DE-627 ger DE-627 rakwb eng QB460-466 Duo Xu verfasserin aut Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier We adopt the deep learning method casi-3d (convolutional approach to structure identification-3D) to infer the orientation of magnetic fields in sub-/trans-Alfvénic turbulent clouds from molecular line emission. We carry out magnetohydrodynamic simulations with different magnetic field strengths and use these to generate synthetic observations. We apply the 3D radiation transfer code radmc-3d to model ^12 CO and ^13 CO (J = 1−0) line emission from the simulated clouds and then train a casi-3d model on these line emission data cubes to predict magnetic field morphology at the pixel level. The trained casi-3d model is able to infer magnetic field directions with a low error (≲10° for sub-Alfvénic samples and ≲30° for trans-Alfvénic samples). We further test the performance of casi-3d on a real sub-/trans- Alfvénic region in Taurus. The casi-3d prediction is consistent with the magnetic field direction inferred from Planck dust polarization measurements. We use our developed methods to produce a new magnetic field map of Taurus that has a three times higher angular resolution than the Planck map. Interstellar medium Interstellar magnetic fields Convolutional neural networks Molecular clouds Magnetohydrodynamics Astrophysics Chi-Yan Law verfasserin aut Jonathan C. Tan verfasserin aut In The Astrophysical Journal IOP Publishing, 2022 942(2023), 2, p 95 (DE-627)269019219 (DE-600)1473835-1 15384357 nnns volume:942 year:2023 number:2, p 95 https://doi.org/10.3847/1538-4357/aca66c kostenfrei https://doaj.org/article/ea7eea4c0335486b83d7ba4a24e11a36 kostenfrei https://doi.org/10.3847/1538-4357/aca66c kostenfrei https://doaj.org/toc/1538-4357 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2014 GBV_ILN_2088 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 942 2023 2, p 95 |
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Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds |
abstract |
We adopt the deep learning method casi-3d (convolutional approach to structure identification-3D) to infer the orientation of magnetic fields in sub-/trans-Alfvénic turbulent clouds from molecular line emission. We carry out magnetohydrodynamic simulations with different magnetic field strengths and use these to generate synthetic observations. We apply the 3D radiation transfer code radmc-3d to model ^12 CO and ^13 CO (J = 1−0) line emission from the simulated clouds and then train a casi-3d model on these line emission data cubes to predict magnetic field morphology at the pixel level. The trained casi-3d model is able to infer magnetic field directions with a low error (≲10° for sub-Alfvénic samples and ≲30° for trans-Alfvénic samples). We further test the performance of casi-3d on a real sub-/trans- Alfvénic region in Taurus. The casi-3d prediction is consistent with the magnetic field direction inferred from Planck dust polarization measurements. We use our developed methods to produce a new magnetic field map of Taurus that has a three times higher angular resolution than the Planck map. |
abstractGer |
We adopt the deep learning method casi-3d (convolutional approach to structure identification-3D) to infer the orientation of magnetic fields in sub-/trans-Alfvénic turbulent clouds from molecular line emission. We carry out magnetohydrodynamic simulations with different magnetic field strengths and use these to generate synthetic observations. We apply the 3D radiation transfer code radmc-3d to model ^12 CO and ^13 CO (J = 1−0) line emission from the simulated clouds and then train a casi-3d model on these line emission data cubes to predict magnetic field morphology at the pixel level. The trained casi-3d model is able to infer magnetic field directions with a low error (≲10° for sub-Alfvénic samples and ≲30° for trans-Alfvénic samples). We further test the performance of casi-3d on a real sub-/trans- Alfvénic region in Taurus. The casi-3d prediction is consistent with the magnetic field direction inferred from Planck dust polarization measurements. We use our developed methods to produce a new magnetic field map of Taurus that has a three times higher angular resolution than the Planck map. |
abstract_unstemmed |
We adopt the deep learning method casi-3d (convolutional approach to structure identification-3D) to infer the orientation of magnetic fields in sub-/trans-Alfvénic turbulent clouds from molecular line emission. We carry out magnetohydrodynamic simulations with different magnetic field strengths and use these to generate synthetic observations. We apply the 3D radiation transfer code radmc-3d to model ^12 CO and ^13 CO (J = 1−0) line emission from the simulated clouds and then train a casi-3d model on these line emission data cubes to predict magnetic field morphology at the pixel level. The trained casi-3d model is able to infer magnetic field directions with a low error (≲10° for sub-Alfvénic samples and ≲30° for trans-Alfvénic samples). We further test the performance of casi-3d on a real sub-/trans- Alfvénic region in Taurus. The casi-3d prediction is consistent with the magnetic field direction inferred from Planck dust polarization measurements. We use our developed methods to produce a new magnetic field map of Taurus that has a three times higher angular resolution than the Planck map. |
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Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds |
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