3D Solar Coronal Loop Reconstructions with Machine Learning
| Autor: | Iulia Chifu, R. Gafeira |
|---|---|
| Přispěvatelé: | Foundation for Science and Technology, German Research Foundation, European Commission, Ministerio de Economía y Competitividad (España) |
| Rok vydání: | 2021 |
| Předmět: |
Solar phenomena
010504 meteorology & atmospheric sciences Field (physics) Extrapolation FOS: Physical sciences Machine learning computer.software_genre 01 natural sciences 0103 physical sciences Astrophysics::Solar and Stellar Astrophysics 010303 astronomy & astrophysics Solar and Stellar Astrophysics (astro-ph.SR) 0105 earth and related environmental sciences Physics Photosphere business.industry 3D reconstruction Astronomy and Astrophysics Coronal loop Magnetic field Astrophysics - Solar and Stellar Astrophysics Space and Planetary Science Coronal plane Physics::Space Physics Artificial intelligence business computer |
| Zdroj: | Digital.CSIC. Repositorio Institucional del CSIC instname Digital.CSIC: Repositorio Institucional del CSIC Consejo Superior de Investigaciones Científicas (CSIC) |
| Popis: | This is an open access article, original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. The magnetic field plays an essential role in the initiation and evolution of different solar phenomena in the corona. The structure and evolution of the 3D coronal magnetic field are still not very well known. A way to ascertain the 3D structure of the coronal magnetic field is by performing magnetic field extrapolations from the photosphere to the corona. In previous work, it was shown that by prescribing the 3D-reconstructed loops' geometry, the magnetic field extrapolation produces a solution with a better agreement between the modeled field and the reconstructed loops. This also improves the quality of the field extrapolation. Stereoscopy, which uses at least two view directions, is the traditional method for performing 3D coronal loop reconstruction. When only one vantage point of the coronal loops is available, other 3D reconstruction methods must be applied. Within this work, we present a method for the 3D loop reconstruction based on machine learning. Our purpose for developing this method is to use as many observed coronal loops in space and time for the modeling of the coronal magnetic field. Our results show that we can build machine-learning models that can retrieve 3D loops based only on their projection information. Ultimately, the neural network model will be able to use only 2D information of the coronal loops, identified, traced, and extracted from the extreme-ultraviolet images, for the calculation of their 3D geometry. © 2021. The Author(s). Published by the American Astronomical Society. Data are courtesy of NASA/SDO and the HMI science teams. I.C. acknowledges DFG-grant WI 3211/5-1. The HMI data are provided courtesy of NASA/SDO and the HMI science team. R.G. acknowledges financial support by the Portuguese Government through the Foundation for Science and Technology-FCT FEDER-European Regional Development Fund through COMPETE 2020-Operational Programme Competitiveness and Internationalization. With funding from the Spanish government through the Severo Ochoa Centre of Excellence accreditation SEV-2017-0709. |
| Databáze: | OpenAIRE |
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