Exploring mutual information between IRIS spectral lines. I. Correlations between spectral lines during solar flares and within the quiet Sun
Brandon Panos, Lucia Kleint, Sviatoslav Voloshynovskiy

TL;DR
This study uses mutual information to analyze correlations between spectral lines during solar flares and quiet Sun conditions, revealing increased coupling during flares and linking spectral line correlations to energy input.
Contribution
It introduces a machine-learning based information-theoretic approach to quantify spectral line dependencies during solar activity, enhancing understanding of solar atmospheric thermodynamics.
Findings
Weak spectral line correlations in quiet Sun conditions.
Enhanced correlations during solar flares, with some line pairs showing high MI scores.
Correlation strength increases with energy input, indicating coupling in the solar atmosphere.
Abstract
Spectral lines allow us to probe the thermodynamics of the solar atmosphere, but the shape of a single spectral line may be similar for different thermodynamic solutions. Multiline analyses are therefore crucial, but computationally cumbersome. We investigate correlations between several chromospheric and transition region lines to restrain the thermodynamic solutions of the solar atmosphere during flares. We used machine-learning methods to capture the statistical dependencies between 6 spectral lines sourced from 21 large solar flares observed by NASA's Interface Region Imaging Spectrograph (IRIS). The techniques are based on an information-theoretic quantity called mutual information (MI), which captures both linear and nonlinear correlations between spectral lines. The MI is estimated using both a categorical and numeric method, and performed separately for a collection of quiet Sun…
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