Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Real-time flare prediction based on distinctions between flaring and
  non-flaring active region spectra   With machine learning entering into the awareness of the heliophysics
community, solar flare prediction has become a topic of increased interest.
Although machine learning models have advanced with each successive
publication, the input data has remained largely fixed on magnetic features.
Despite this increased model complexity, results seem to indicate that
photospheric magnetic field data alone may not be a wholly sufficient source of
data for flare prediction. For the first time we have extended the study of
flare prediction to spectral data. In this work, we use Deep Neural Networks to
monitor the changes of several features derived from the strong resonant Mg II
h\&k lines observed by IRIS. The features in descending order of predictive
capability are: The triplet emission at 2798.77 $\text{\AA}$, line core
intensity, total continuum emission between the h\&k line cores, the k/h ratio,
line-width, followed by several other line features such as asymmetry and line
center. Regions that are about to flare generate spectra which are
distinguishable from non-flaring active region spectra. Our algorithm can
correctly identify pre-flare spectra approximately 35 minutes before the start
of the flare, with an AUC of 86 \% and an accuracy, precision and recall of 80
\%. The accuracy and AUC monotonically increases to 90 \% and 97 \%
respectively as we move closer in time to the start of the flare. Our study
indicates that spectral data alone can lead to good predictive models and
should be considered as an additional source of information alongside
photospheric magnetograms.

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The categories of this paper are: solv-int
32
The categories of this paper are: atom-ph
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The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
36
The categories of this paper are: cmp-lg
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The categories of this paper are: funct-an
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The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an