SuNeRF: 3D Reconstruction of the Solar EUV Corona Using Neural Radiance Fields
To understand its evolution and the effects of its eruptive events, the Sun is permanently monitored by multiple satellite missions. The optically thin emission of the solar plasma and the limited number of viewpoints make it challenging to reconstruct the geometry and structure of the solar atmosph...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | The Astrophysical Journal Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.3847/2041-8213/ad12d2 |
| _version_ | 1797347718973095936 |
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| author | Robert Jarolim Benoit Tremblay Andrés Muñoz-Jaramillo Kyriaki-Margarita Bintsi Anna Jungbluth Miraflor Santos Angelos Vourlidas James P. Mason Sairam Sundaresan Cooper Downs Ronald M. Caplan |
| author_facet | Robert Jarolim Benoit Tremblay Andrés Muñoz-Jaramillo Kyriaki-Margarita Bintsi Anna Jungbluth Miraflor Santos Angelos Vourlidas James P. Mason Sairam Sundaresan Cooper Downs Ronald M. Caplan |
| author_sort | Robert Jarolim |
| collection | DOAJ |
| description | To understand its evolution and the effects of its eruptive events, the Sun is permanently monitored by multiple satellite missions. The optically thin emission of the solar plasma and the limited number of viewpoints make it challenging to reconstruct the geometry and structure of the solar atmosphere; however, this information is the missing link to understand the Sun as it is: a 3D evolving star. We present a method that enables a complete 3D representation of the uppermost solar layer (corona) observed in extreme ultraviolet (EUV) light. We use a deep-learning approach for 3D scene representation that accounts for radiative transfer to map the entire solar atmosphere from three simultaneous observations. We demonstrate that our approach provides unprecedented reconstructions of the solar poles and directly enables height estimates of coronal structures, solar filaments, coronal hole profiles, and coronal mass ejections. We validate the approach using model-generated synthetic EUV images, finding that our method accurately captures the 3D geometry of the Sun even from a limited number of 32 ecliptic viewpoints (∣latitude∣ ≤ 7°). We quantify the uncertainties of our model using an ensemble approach that allows us to estimate the model performance in the absence of a ground truth. Our method enables a novel view of our closest star and is a breakthrough technology for the efficient use of multi-instrument data sets, which paves the way for future cluster missions. |
| first_indexed | 2024-03-08T11:51:52Z |
| format | Article |
| id | doaj.art-e7f6847784534d7b9e393e2ddc58fbd3 |
| institution | Directory Open Access Journal |
| issn | 2041-8205 |
| language | English |
| last_indexed | 2024-03-08T11:51:52Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal Letters |
| spelling | doaj.art-e7f6847784534d7b9e393e2ddc58fbd32024-01-24T09:14:31ZengIOP PublishingThe Astrophysical Journal Letters2041-82052024-01-019612L3110.3847/2041-8213/ad12d2SuNeRF: 3D Reconstruction of the Solar EUV Corona Using Neural Radiance FieldsRobert Jarolim0https://orcid.org/0000-0002-9309-2981Benoit Tremblay1https://orcid.org/0000-0002-5181-7913Andrés Muñoz-Jaramillo2https://orcid.org/0000-0002-4716-0840Kyriaki-Margarita Bintsi3https://orcid.org/0000-0003-3875-7757Anna Jungbluth4https://orcid.org/0000-0002-9888-6262Miraflor Santos5Angelos Vourlidas6https://orcid.org/0000-0002-8164-5948James P. Mason7https://orcid.org/0000-0002-3783-5509Sairam Sundaresan8https://orcid.org/0000-0002-6648-0591Cooper Downs9https://orcid.org/0000-0003-1759-4354Ronald M. Caplan10https://orcid.org/0000-0002-2633-4290University of Graz , Universitätsplatz 5, A-8010 Graz, AustriaHigh Altitude Observatory , 3080 Center Green Dr., Boulder, CO 80301, USASouthwest Research Institute , 1050 Walnut St., Suite 300, Boulder, CO 80302, USAImperial College London , London SW7 2AZ, UKEuropean Space Agency (ESA)—ECSAT , Fermi Avenue, Harwell, UKMassachusetts Institute of Technology , 77 Massachusetts Ave., Cambridge, MA 02139, USAJohns Hopkins University Applied Physics Laboratory , 11100 Johns Hopkins Rd., Laurel, MD 20723, USAJohns Hopkins University Applied Physics Laboratory , 11100 Johns Hopkins Rd., Laurel, MD 20723, USAIntel Labs, 2200 Mission College Blvd. , Santa Clara, CA 95054, USAPredictive Science Inc. , 9990 Mesa Rim Rd., Suite 170, San Diego, CA 92121, USAPredictive Science Inc. , 9990 Mesa Rim Rd., Suite 170, San Diego, CA 92121, USATo understand its evolution and the effects of its eruptive events, the Sun is permanently monitored by multiple satellite missions. The optically thin emission of the solar plasma and the limited number of viewpoints make it challenging to reconstruct the geometry and structure of the solar atmosphere; however, this information is the missing link to understand the Sun as it is: a 3D evolving star. We present a method that enables a complete 3D representation of the uppermost solar layer (corona) observed in extreme ultraviolet (EUV) light. We use a deep-learning approach for 3D scene representation that accounts for radiative transfer to map the entire solar atmosphere from three simultaneous observations. We demonstrate that our approach provides unprecedented reconstructions of the solar poles and directly enables height estimates of coronal structures, solar filaments, coronal hole profiles, and coronal mass ejections. We validate the approach using model-generated synthetic EUV images, finding that our method accurately captures the 3D geometry of the Sun even from a limited number of 32 ecliptic viewpoints (∣latitude∣ ≤ 7°). We quantify the uncertainties of our model using an ensemble approach that allows us to estimate the model performance in the absence of a ground truth. Our method enables a novel view of our closest star and is a breakthrough technology for the efficient use of multi-instrument data sets, which paves the way for future cluster missions.https://doi.org/10.3847/2041-8213/ad12d2Solar coronaActive solar coronaNeural networksSolar filament eruptionsSolar coronal holes |
| spellingShingle | Robert Jarolim Benoit Tremblay Andrés Muñoz-Jaramillo Kyriaki-Margarita Bintsi Anna Jungbluth Miraflor Santos Angelos Vourlidas James P. Mason Sairam Sundaresan Cooper Downs Ronald M. Caplan SuNeRF: 3D Reconstruction of the Solar EUV Corona Using Neural Radiance Fields The Astrophysical Journal Letters Solar corona Active solar corona Neural networks Solar filament eruptions Solar coronal holes |
| title | SuNeRF: 3D Reconstruction of the Solar EUV Corona Using Neural Radiance Fields |
| title_full | SuNeRF: 3D Reconstruction of the Solar EUV Corona Using Neural Radiance Fields |
| title_fullStr | SuNeRF: 3D Reconstruction of the Solar EUV Corona Using Neural Radiance Fields |
| title_full_unstemmed | SuNeRF: 3D Reconstruction of the Solar EUV Corona Using Neural Radiance Fields |
| title_short | SuNeRF: 3D Reconstruction of the Solar EUV Corona Using Neural Radiance Fields |
| title_sort | sunerf 3d reconstruction of the solar euv corona using neural radiance fields |
| topic | Solar corona Active solar corona Neural networks Solar filament eruptions Solar coronal holes |
| url | https://doi.org/10.3847/2041-8213/ad12d2 |
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