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Constraining the frequency of energy deposition in magnetically-closed activeregion cores requires sophisticated hydrodynamic simulations of the coronalplasma and detailed forward modeling of the optically-thin line-of-sightintegrated emission. However, understanding which set of model inputs bestmatches a set of observations is complicated by the need for any proposedheating model to simultaneously satisfy multiple observable constraints. Inthis paper, we train a random forest classification model on a set offorward-modeled observable quantities, namely the emission measure slope, thepeak temperature of the emission measure distribution, and the time lag andmaximum cross-correlation between multiple pairs of AIA channels. We then useour trained model to classify the heating frequency in every pixel of activeregion NOAA 1158 using the observed emission measure slopes, peak temperatures,time lags, and maximum cross-correlations and are able to map the heatingfrequency across the entire active region. We find that high-frequency heatingdominates in the inner core of the active region while intermediate frequencydominates closer to the periphery of the active region. Additionally, we assessthe importance of each observed quantity in our trained classification modeland find that the emission measure slope is the dominant feature in decidingwith which heating frequency a given pixel is most consistent. The techniquepresented here offers a very promising and widely applicable method forassessing observations in terms of detailed forward models given an arbitrarynumber of observable constraints.

Understanding Heating in Active Region Cores through Machine Learning II. Classifying Observations