Serena Flint
Ivan Milic
2021-02-26
<p>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. </p>
https://doi.org/10.5281/zenodo.4565921
oai:zenodo.org:4565921
eng
Zenodo
https://zenodo.org/communities/coolstars20half
https://doi.org/10.5281/zenodo.4565920
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
spectroscopy
photosphere
sun
machine learning
Sparse Representation of HINODE/SOT/SP Spectra Using Convolutional Neural Networks
info:eu-repo/semantics/conferencePoster