Poster Open Access

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

Serena Flint; Ivan Milic

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. 

Files (1.5 MB)
Name Size
1.5 MB Download
All versions This version
Views 5050
Downloads 4646
Data volume 71.1 MB71.1 MB
Unique views 4545
Unique downloads 4444


Cite as