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...
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Autor*in:	Duo Xu [verfasserIn] Chi-Yan Law [verfasserIn] Jonathan C. Tan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Interstellar medium Interstellar magnetic fields Convolutional neural networks Molecular clouds Magnetohydrodynamics

Übergeordnetes Werk:	In: The Astrophysical Journal - IOP Publishing, 2022, 942(2023), 2, p 95
Übergeordnetes Werk:	volume:942 ; year:2023 ; number:2, p 95

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3847/1538-4357/aca66c

Katalog-ID:	DOAJ089168275

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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
language	English
source	In The Astrophysical Journal 942(2023), 2, p 95 volume:942 year:2023 number:2, p 95
sourceStr	In The Astrophysical Journal 942(2023), 2, p 95 volume:942 year:2023 number:2, p 95
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Interstellar medium Interstellar magnetic fields Convolutional neural networks Molecular clouds Magnetohydrodynamics Astrophysics
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container_title	The Astrophysical Journal
authorswithroles_txt_mv	Duo Xu @@aut@@ Chi-Yan Law @@aut@@ Jonathan C. Tan @@aut@@
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callnumber-first	Q - Science
author	Duo Xu
spellingShingle	Duo Xu misc QB460-466 misc Interstellar medium misc Interstellar magnetic fields misc Convolutional neural networks misc Molecular clouds misc Magnetohydrodynamics misc Astrophysics Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds
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topic_title	QB460-466 Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds Interstellar medium Interstellar magnetic fields Convolutional neural networks Molecular clouds Magnetohydrodynamics
topic	misc QB460-466 misc Interstellar medium misc Interstellar magnetic fields misc Convolutional neural networks misc Molecular clouds misc Magnetohydrodynamics misc Astrophysics
topic_unstemmed	misc QB460-466 misc Interstellar medium misc Interstellar magnetic fields misc Convolutional neural networks misc Molecular clouds misc Magnetohydrodynamics misc Astrophysics
topic_browse	misc QB460-466 misc Interstellar medium misc Interstellar magnetic fields misc Convolutional neural networks misc Molecular clouds misc Magnetohydrodynamics misc Astrophysics
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title	Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds
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title_full	Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds
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title_sort	application of convolutional neural networks to predict magnetic fields’ directions in turbulent clouds
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title_auth	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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title_short	Application of Convolutional Neural Networks to Predict Magnetic Fields’ Directions in Turbulent Clouds
url	https://doi.org/10.3847/1538-4357/aca66c https://doaj.org/article/ea7eea4c0335486b83d7ba4a24e11a36 https://doaj.org/toc/1538-4357
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up_date	2024-07-03T21:39:28.354Z
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