Optimization of X-ray Flare Time Profile Parameters for Space Weather
Applications
Abstract
Solar X-ray flares often drive coronal mass ejections and solar
energetic particles (SEPs), which are key elements of space weather near
the Earth and beyond. Machine learning with capability to improve space
weather forecasting, prevents the adverse consequences of the space
weather phenomena. We study temporal parameters of the solar X-ray
flares associated with the SEP events. The temporal profile of the X-ray
flare is usually divided into rising and decaying phases each
approximated by a single functional representation. We observe a more
complex nature of the rising and decaying phase for number of X-ray
flares, which includes a pre-flare increase of intensity and a break in
the decay phase. We develop a method to define, derive, and provide an
optimized set of X-ray flare temporal profile parameters which takes
into account the complex nature of the flare. We create a set of X-ray
flare temporal properties which we next apply to the machine learning
algorithms for a better forecasting of X-ray flare evolution in time and
the associated SEP events.