Statistical techniques for the detection and analysis of solar explosive events
Luis M. Sarro, Angel Berihuete

TL;DR
This paper introduces statistical methods to detect and analyze solar explosive events from spectral data, revealing differences in event characteristics across solar regions and informing reconnection models.
Contribution
It presents two novel statistical approaches combining PCA, wavelet, and ICA techniques for unbiased detection and characterization of solar explosive events.
Findings
Higher frequency of explosive events near active regions
Distinct profile characteristics in different solar regions
Implications for refining magnetic reconnection models
Abstract
Solar explosive events are commonly explained as small scale magnetic reconnection events, although unambiguous confirmation of this scenario remains elusive due to the lack of spatial resolution and of the statistical analysis of large enough samples of this type of events. In this work, we propose a sound statistical treatment of data cubes consisting of a temporal sequence of long slit spectra of the solar atmosphere. The analysis comprises all the stages from the explosive event detection to its characterization and the subsequent sample study. We have designed two complementary approaches based on the combination of standard statistical techniques (Robust Principal Component Analysis in one approach and wavelet decomposition and Independent Component Analysis in the second) in order to obtain least biased samples. These techniques are implemented in the spirit of letting the data…
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