Machine learning as a flaring storm warning machine: Was a warning machine for the September 2017 solar flaring storm possible?. (arXiv:2007.02425v1 [astro-ph.SR])
<a href="http://arxiv.org/find/astro-ph/1/au:+Benvenuto_F/0/1/0/all/0/1">Federico Benvenuto</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Campi_C/0/1/0/all/0/1">Cristina Campi</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Massone_A/0/1/0/all/0/1">Anna Maria Massone</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Piana_M/0/1/0/all/0/1">Michele Piana</a>
Machine learning is nowadays the methodology of choice for flare forecasting
and supervised techniques, in both their traditional and deep versions, are
becoming the most frequently used ones for prediction in this area of space
weather. Yet, machine learning has not been able so far to realize an operating
warning system for flaring storms and the scientific literature of the last
decade suggests that its performances in the prediction of intense solar flares
are not optimal.
The main difficulties related to forecasting solar flaring storms are
probably two. First, most methods are conceived to provide probabilistic
predictions and not to send binary yes/no indications on the consecutive
occurrence of flares along an extended time range. Second, flaring storms are
typically characterized by the explosion of high energy events, which are
seldom recorded in the databases of space missions; as a consequence,
supervised methods are trained on very imbalanced historical sets, which makes
them particularly ineffective for the forecasting of intense flares.
Yet, in this study we show that supervised machine learning could be utilized
in a way to send timely warnings about the most violent and most unexpected
flaring event of the last decade, and even to predict with some accuracy the
energy budget daily released by magnetic reconnection during the whole time
course of the storm. Further, we show that the combination of
sparsity-enhancing machine learning and feature ranking could allow the
identification of the prominent role that energy played as an Active Region
property in the forecasting process.
Machine learning is nowadays the methodology of choice for flare forecasting
and supervised techniques, in both their traditional and deep versions, are
becoming the most frequently used ones for prediction in this area of space
weather. Yet, machine learning has not been able so far to realize an operating
warning system for flaring storms and the scientific literature of the last
decade suggests that its performances in the prediction of intense solar flares
are not optimal.
The main difficulties related to forecasting solar flaring storms are
probably two. First, most methods are conceived to provide probabilistic
predictions and not to send binary yes/no indications on the consecutive
occurrence of flares along an extended time range. Second, flaring storms are
typically characterized by the explosion of high energy events, which are
seldom recorded in the databases of space missions; as a consequence,
supervised methods are trained on very imbalanced historical sets, which makes
them particularly ineffective for the forecasting of intense flares.
Yet, in this study we show that supervised machine learning could be utilized
in a way to send timely warnings about the most violent and most unexpected
flaring event of the last decade, and even to predict with some accuracy the
energy budget daily released by magnetic reconnection during the whole time
course of the storm. Further, we show that the combination of
sparsity-enhancing machine learning and feature ranking could allow the
identification of the prominent role that energy played as an Active Region
property in the forecasting process.
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