IRIS performs solar observations over a large range of atmospheric heights, including the chromosphere where the majority of flare energy is dissipated. The strong Mg II h&k spectral lines are capable of providing excellent atmospheric diagnostics, but have not been fully utilized for flaring atmospheres. We aim to investigate whether the physics of the chromosphere is identical for all flare observations by analyzing if there are certain spectra that occur in all flares. To achieve this, we automatically analyze hundreds of thousands of Mg II h&k line profiles from a set of 33 flares, and use a machine learning technique which we call supervised hierarchical k-means, to cluster all profile shapes. We identify a single peaked Mg II profile, in contrast to the double-peaked quiet Sun profiles, appearing in every flare. Additionally, we find extremely broad profiles with characteristic blue shifted central reversals appearing at the front of fast-moving flare ribbons. These profiles occur during the impulsive phase of the flare, and we present results of their temporal and spatial correlation with non-thermal hard X-ray signatures, suggesting that flare-accelerated electrons play an important role in the formation of these profiles. The ratio of the integrated Mg II h&k lines can also serve as an opacity diagnostic, and we find higher opacities during each flare maximum. Our study shows that machine learning is a powerful tool for large scale statistical solar analyses.
With machine learning entering into the awareness of the heliophysics community, solar flare prediction has become a topic of increased interest. Although machine learning models have advanced with each successive publication, the input data has remained largely fixed on magnetic features. Despite this increased model complexity, results seem to indicate that photospheric magnetic field data alone may not be a wholly sufficient source of data for flare prediction. For the first time we have extended the study of flare prediction to spectral data. In this work, we use Deep Neural Networks to monitor the changes of several features derived from the strong resonant Mg II h&k lines observed by IRIS. The features in descending order of predictive capability are: The triplet emission at 2798.77 Å, line core intensity, total continuum emission between the h&k line cores, the k/h ratio, line-width, followed by several other line features such as asymmetry and line center. Regions that are about to flare generate spectra which are distinguishable from non-flaring active region spectra. Our algorithm can correctly identify pre-flare spectra approximately 35 minutes before the start of the flare, with an AUC of 86 % and an accuracy, precision and recall of 80 %. The accuracy and AUC monotonically increases to 90 % and 97 % respectively as we move closer in time to the start of the flare. Our study indicates that spectral data alone can lead to good predictive models and should be considered as an additional source of information alongside photospheric magnetograms.
Small reconnection events in the lower solar atmosphere can lead to its heating, but whether such heating can propagate into higher atmospheric layers and potentially contribute to coronal heating is an open question. We carry out a large statistical analysis of all IRIS observations from 2013 and 2014. We identified “IRIS burst” (IB) spectra using a k-means analysis that entails classifying and selecting Si IV spectra with superimposed blend lines on top of bursts, which indicate low atmospheric heating. We find that ∼8% of all observations show IBs with about 0.01% of all recorded IRIS spectra being IB spectra. We find varying blend absorption levels, which may indicate different depths of the reconnection event and heating. IRIS bursts are statistically visible with similar properties and timings in the spectral lines Mg II, C II, and Si IV, but invisible in Fe XXI. By statistically analyzing co-spatial AIA light curves, we found systematic enhancements in AIA 1600 and AIA 1700, but no clear response to bursts in all other AIA wavelengths (94, 131, 171, 193, 211, 304, 335) in a time-frame of ±6 min around the burst. This may indicate that heating due to IBs is confined within the lower atmosphere and dissipates before reaching temperatures or formation heights covered by the hotter AIA lines. Our developed methods are applicable for statistical analyses of any co-observed data sets and allow us to efficiently analyze millions of spectra and light curves simultaneously.
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