Compression of Solar Spectroscopic Observations: A Case Study of Mg II k Spectral Line Profiles Observed by NASA's IRIS Satellite

Viacheslav M. Sadykov, Irina N. Kitiashvili, Alberto Sainz Dalda, Vincent Oria, Alexander G. Kosovichev, Egor Illarionov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this study we extract the deep features and investigate the compression of the Mg II k spectral line profiles observed in quiet Sun regions by NASA's IRIS satellite. The data set of line profiles used for the analysis was obtained on April 20th, 2020, at the center of the solar disc, and contains almost 300,000 individual Mg II k line profiles after data cleaning. The data are separated into train and test subsets. The train subset was used to train the autoencoder of the varying embedding layer size. The early stopping criterion was implemented on the test subset to prevent the model from overfitting. Our results indicate that it is possible to compress the spectral line profiles more than 27 times (which corresponds to the reduction of the data dimensionality from 110 to 4) while having a 4 DN (Data Number) average reconstruction error, which is comparable to the variations in the line continuum. The mean squared error and the reconstruction error of even statistical moments sharply decrease when the dimensionality of the embedding layer increases from 1 to 4 and almost stop decreasing for higher numbers. The observed occasional improvements in training for values higher than 4 indicate that a better compact embedding may potentially be obtained if other training strategies and longer training times are used. The features learned for the critical four-dimensional case can be interpreted. In particular, three of these four features mainly control the line width, line asymmetry, and line dip formation respectively. The presented results are the first attempt to obtain a compact embedding for spectroscopic line profiles and confirm the value of this approach, in particular for feature extraction, data compression, and denoising.

Original languageEnglish (US)
Title of host publication2021 International Conference on Content-Based Multimedia Indexing, CBMI 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665442206
DOIs
StatePublished - Jun 28 2021
Event18th International Conference on Content-Based Multimedia Indexing, CBMI 2021 - Virtual, Lille, France
Duration: Jun 28 2021Jun 30 2021

Publication series

NameProceedings - International Workshop on Content-Based Multimedia Indexing
Volume2021-June
ISSN (Print)1949-3991

Conference

Conference18th International Conference on Content-Based Multimedia Indexing, CBMI 2021
Country/TerritoryFrance
CityVirtual, Lille
Period6/28/216/30/21

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Information Systems

Keywords

  • Data compaction and compression
  • Feature extraction or construction
  • Machine learning
  • Neural nets

Fingerprint

Dive into the research topics of 'Compression of Solar Spectroscopic Observations: A Case Study of Mg II k Spectral Line Profiles Observed by NASA's IRIS Satellite'. Together they form a unique fingerprint.

Cite this