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Proxy-based Prediction of Solar Extreme Ultraviolet Emission Using Deep Learning

Pineci, Anthony and Sadowski, Peter and Gaidos, Eric and Sun, Xudong (2021) Proxy-based Prediction of Solar Extreme Ultraviolet Emission Using Deep Learning. Astrophysical Journal Letters, 910 (2). Art. No. L25. ISSN 2041-8213. doi:10.3847/2041-8213/abee89. https://resolver.caltech.edu/CaltechAUTHORS:20210318-153535430

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Abstract

High-energy radiation from the Sun governs the behavior of Earth's upper atmosphere and such radiation from any planet-hosting star can drive the long-term evolution of a planetary atmosphere. However, much of this radiation is unobservable because of absorption by Earth's atmosphere and the interstellar medium. This motivates the identification of a proxy that can be readily observed from the ground. Here, we evaluate absorption in the near-infrared 1083 nm triplet line of neutral orthohelium as a proxy for extreme ultraviolet (EUV) emission in the 30.4 nm line of He ii and 17.1 nm line of Fe ix from the Sun. We apply deep learning to model the nonlinear relationships, training and validating the model on historical, contemporaneous images of the solar disk acquired in the triplet He i line by the ground-based SOLIS observatory and in the EUV by the NASA Solar Dynamics Observatory. The model is a fully convolutional neural network that incorporates spatial information and accounts for the projection of the spherical Sun to 2d images. Using normalized target values, results indicate a median pixelwise relative error of 20% and a mean disk-integrated flux error of 7% on a held-out test set. Qualitatively, the model learns the complex spatial correlations between He i absorption and EUV emission has a predictive ability superior to that of a pixel-by-pixel model; it can also distinguish active regions from high-absorption filaments that do not result in EUV emission.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3847/2041-8213/abee89DOIArticle
https://arxiv.org/abs/2103.08887arXivDiscussion Paper
ORCID:
AuthorORCID
Pineci, Anthony0000-0002-6053-4974
Sadowski, Peter0000-0002-7354-5461
Gaidos, Eric0000-0002-5258-6846
Sun, Xudong0000-0003-4043-616X
Additional Information:© 2021. The American Astronomical Society. Received 2020 December 10; revised 2021 March 11; accepted 2021 March 15; published 2021 April 6. Tom Schad offered valuable comments on a draft of this manuscript. A.P. was supported in part by Samuel P. and Frances Krown through the Caltech Summer Undergraduate Research Fellowship program. This material is based upon work supported by the National Science Foundation under grant No. 2008344. Advanced computing resources from the University of Hawai'i Information Technology Services Cyberinfrastructure are gratefully acknowledged. E.G. acknowledges support as a long-term visitor in the Center for Space and Habitability at the University of Bern.
Funders:
Funding AgencyGrant Number
Caltech Summer Undergraduate Research Fellowship (SURF)UNSPECIFIED
NSF2008344
University of BernUNSPECIFIED
Subject Keywords:Convolutional neural networks; Solar extreme ultraviolet emission; Neural networks; Ground-based astronomy
Issue or Number:2
Classification Code:Unified Astronomy Thesaurus concepts: Convolutional neural networks (1938); Solar extreme ultraviolet emission (1493); Neural networks (1933); Ground-based astronomy (686)
DOI:10.3847/2041-8213/abee89
Record Number:CaltechAUTHORS:20210318-153535430
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210318-153535430
Official Citation:Anthony Pineci et al 2021 ApJL 910 L25
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108494
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:19 Mar 2021 00:01
Last Modified:21 Apr 2021 21:15

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