Unsupervised Machine Learning for the Identification of Preflare Spectroscopic Signatures

The study of the preflare environment is of great importance to understanding what drives solar flares. k-means clustering, an unsupervised machine-learning technique, has the ability to cluster large data set in a way that would be impractical or impossible for a human to do. In this paper we present a study using k-means clustering to identify possible preflare signatures in spectroscopic observations of the Mg ii h and k spectral lines made by NASA's Interface Region Imaging Spectrometer. Our analysis finds that spectral profiles showing single-peak Mg ii h and k and single-peaked emission in the Mg ii UV triplet lines are associated with preflare activity up to 40 minutes prior to flaring. Subsequent inversions of these spectral profiles reveal increased temperature and electron density in the chromosphere, which suggest that significant heating events in the chromosphere may be associated with precursor signals to flares.