Uncovering Heterogeneity of Solar Flare Mechanism With Mixture Models. (arXiv:2401.14345v1 [astro-ph.SR])
<a href="http://arxiv.org/find/astro-ph/1/au:+Do_B/0/1/0/all/0/1">Bach Viet Do</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Chen_Y/0/1/0/all/0/1">Yang Chen</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Nguyen_X/0/1/0/all/0/1">XuanLong Nguyen</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Manchester_W/0/1/0/all/0/1">Ward Manchester</a>
The physics of solar flares occurring on the Sun is highly complex and far
from fully understood. However, observations show that solar eruptions are
associated with the intense kilogauss fields of active regions, where free
energies are stored with field-aligned electric currents. With the advent of
high-quality data sources such as the Geostationary Operational Environmental
Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and
Magnetic Imager (HMI), recent works on solar flare forecasting have been
focusing on data-driven methods. In particular, black box machine learning and
deep learning models are increasingly adopted in which underlying data
structures are not modeled explicitly. If the active regions indeed follow the
same laws of physics, there should be similar patterns shared among them,
reflected by the observations. Yet, these black box models currently used in
the literature do not explicitly characterize the heterogeneous nature of the
solar flare data, within and between active regions. In this paper, we propose
two finite mixture models designed to capture the heterogeneous patterns of
active regions and their associated solar flare events. With extensive
numerical studies, we demonstrate the usefulness of our proposed method for
both resolving the sample imbalance issue and modeling the heterogeneity for
rare energetic solar flare events.
The physics of solar flares occurring on the Sun is highly complex and far
from fully understood. However, observations show that solar eruptions are
associated with the intense kilogauss fields of active regions, where free
energies are stored with field-aligned electric currents. With the advent of
high-quality data sources such as the Geostationary Operational Environmental
Satellites (GOES) and Solar Dynamics Observatory (SDO)/Helioseismic and
Magnetic Imager (HMI), recent works on solar flare forecasting have been
focusing on data-driven methods. In particular, black box machine learning and
deep learning models are increasingly adopted in which underlying data
structures are not modeled explicitly. If the active regions indeed follow the
same laws of physics, there should be similar patterns shared among them,
reflected by the observations. Yet, these black box models currently used in
the literature do not explicitly characterize the heterogeneous nature of the
solar flare data, within and between active regions. In this paper, we propose
two finite mixture models designed to capture the heterogeneous patterns of
active regions and their associated solar flare events. With extensive
numerical studies, we demonstrate the usefulness of our proposed method for
both resolving the sample imbalance issue and modeling the heterogeneity for
rare energetic solar flare events.
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