RADYNVERSION: Learning to invert a solar flare atmosphere with invertible neural networks

Osborne, C. M.J., Armstrong, J. A. and Fletcher, L. (2019) RADYNVERSION: Learning to invert a solar flare atmosphere with invertible neural networks. Astrophysical Journal, 873, 128. (doi: 10.3847/1538-4357/ab07b4)

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During a solar flare, it is believed that reconnection takes place in the corona followed by fast energy transport to the chromosphere. The resulting intense heating strongly disturbs the chromospheric structure and induces complex radiation hydrodynamic effects. Interpreting the physics of the flaring solar atmosphere is one of the most challenging tasks in solar physics. Here we present a novel deep-learning approach, an invertible neural network, to understanding the chromospheric physics of a flaring solar atmosphere via the inversion of observed solar line profiles in Hα and Ca ii λ8542. Our network is trained using flare simulations from the 1D radiation hydrodynamic code RADYN as the expected atmosphere and line profile. This model is then applied to single pixels from an observation of an M1.1 solar flare taken with the Swedish 1 m Solar Telescope/CRisp Imaging SpectroPolarimeter instrument just after the flare onset. The inverted atmospheres obtained from observations provide physical information on the electron number density, temperature, and bulk velocity flow of the plasma throughout the solar atmosphere ranging from 0 to 10 Mm in height. The density and temperature profiles appear consistent with the expected atmospheric response, and the bulk plasma velocity provides the gradients needed to produce the broad spectral lines while also predicting the expected chromospheric evaporation from flare heating. We conclude that we have taught our novel algorithm the physics of a solar flare according to RADYN and that this can be confidently used for the analysis of flare data taken in these two wavelengths. This algorithm can also be adapted for a menagerie of inverse problems providing extremely fast (~10 μs) inversion samples.

Item Type:Articles
Additional Information:C.M.J.O. acknowledges support from the UK’s Science and Technology Facilities Council (STFC) doctoral training grant ST/R504750/1. J.A.A. acknowledges a data-intensive science studentship with the STFC “ScotDIST” center for doctoral training supported by grant ST/R504750/1. L.F. acknowledges support from STFC grant ST/P000533/1. The authors are grateful to M. Carlsson and the F-CHROMA collaboration for the production and availability of the grid of RADYN simulations. The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 606862 (F-CHROMA) and from the Research Council of Norway through the Programme for Supercomputing.
Glasgow Author(s) Enlighten ID:Armstrong, Mr John and Fletcher, Professor Lyndsay and Osborne, Mr Christopher
Authors: Osborne, C. M.J., Armstrong, J. A., and Fletcher, L.
College/School:College of Science and Engineering > School of Physics and Astronomy
Journal Name:Astrophysical Journal
Publisher:American Astronomical Society
ISSN (Online):1538-4357
Copyright Holders:Copyright © 2019 The American Astronomical Society
First Published:First published in Astrophysical Journal 873:128
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher
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