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Deep Learning Techniques for Inverting Observations from the EUV Snapshot Imaging Spectrograph

Presentation #124.12 in the session High-Energy Solar Investigations Through Next-Generation Remote Sensing: Spectroscopy, Imaging, and Beyond — Poster Session.

Published onOct 20, 2022
Deep Learning Techniques for Inverting Observations from the EUV Snapshot Imaging Spectrograph

Imaging spectroscopy of the solar atmosphere with high spatial, spectral, and temporal resolution over a wide field of view is a longstanding goal of heliophysics because it allows for the measurement of important plasma parameters such as velocity and density with high fidelity. The EUV Snapshot Imaging Spectrograph (ESIS) is a sounding rocket instrument designed to capture EUV spectral line profiles over a large, 2D field of view with much higher temporal resolution than current rastering slit spectrographs. ESIS achieves this using a computed tomography imaging spectrograph design, with four channels. Each channel is an independent slitless spectrograph, illuminated by a common primary mirror, but oriented with a unique dispersion direction. Each ESIS exposure, comprising of four channels, can be inverted to recover spectral line profiles for every point in the field of view using limited-angle computed tomography techniques. In this work we present progress on the development of a deep learning algorithm that learns to solve the ESIS limited-angle tomography problem using data from the Interface Region Imaging Spectrograph as a training dataset. We will apply this algorithm to observations from the 2019 ESIS flight and compare the results to those obtained using previous methods.

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