Real-time flare prediction based on distinctions between flaring and non-flaring active region spectra. (arXiv:1911.12621v1 [astro-ph.SR])
<a href="http://arxiv.org/find/astro-ph/1/au:+Panos_B/0/1/0/all/0/1">Brandon Panos</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Kleint_L/0/1/0/all/0/1">Lucia Kleint</a>
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 {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 80% accuracy, precision and recall. This accuracy monotonically increases to
90% 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.
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 {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 80% accuracy, precision and recall. This accuracy monotonically increases to
90% 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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