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Sparse Representation of HINODE/SOT/SP Spectra Using Convolutional Neural Networks

Presentation #213.04 in the session “Solar Physics Division (SPD): Analysis Techniques and CMEs and Jets”.

Published onJun 18, 2021
Sparse Representation of HINODE/SOT/SP Spectra Using Convolutional Neural Networks

A fundamental problem in solar spectropolarimetry is relating observed spectra and their polarization to the physical parameters of the underlying atmosphere. One of the difficulties in this process is the fact that the spectra usually can be represented with a much smaller number of hyperparameters than what is suggested by the number of wavelength points used for sampling. Said differently, spectra can usually be compressed or described in a sparser basis. In this work, we use the neural networks to investigate the dimensionality of photospheric spectra, and to compare the compressed spectra with the maps of physical parameters used to generate the said spectra.


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