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

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dc.citation.articleNumber132en_US
dc.citation.issueNumber2en_US
dc.citation.journalTitleThe Astrophysical Journalen_US
dc.citation.volumeNumber919en_US
dc.contributor.authorBarnes, W.T.en_US
dc.contributor.authorBradshaw, S.J.en_US
dc.contributor.authorViall, N.M.en_US
dc.date.accessioned2021-11-08T15:46:34Zen_US
dc.date.available2021-11-08T15:46:34Zen_US
dc.date.issued2021en_US
dc.description.abstractConstraining the frequency of energy deposition in magnetically closed active region cores requires sophisticated hydrodynamic simulations of the coronal plasma and detailed forward modeling of the optically thin line-of-sight integrated emission. However, understanding which set of model inputs best matches a set of observations is complicated by the need for any proposed heating model to simultaneously satisfy multiple observable constraints. In this paper, we train a random forest classification model on a set of forward-modeled observable quantities, namely the emission measure slope, the peak temperature of the emission measure distribution, and the time lag and maximum cross-correlation between multiple pairs of AIA channels. We then use our trained model to classify the heating frequency in every pixel of active region NOAA 1158 using the observed emission measure slopes, peak temperatures, time lags, and maximum cross-correlations, and are able to map the heating frequency across the entire active region. We find that high-frequency heating dominates in the inner core of the active region while intermediate-frequency dominates closer to the periphery of the active region. Additionally, we assess the importance of each observed quantity in our trained classification model and find that the emission measure slope is the dominant feature in deciding with which heating frequency a given pixel is most consistent. The technique presented here offers a very promising and widely applicable method for assessing observations in terms of detailed forward models given an arbitrary number of observable constraints.en_US
dc.identifier.citationBarnes, W.T., Bradshaw, S.J. and Viall, N.M.. "Understanding Heating in Active Region Cores through Machine Learning. II. Classifying Observations." <i>The Astrophysical Journal,</i> 919, no. 2 (2021) IOP Publishing: https://doi.org/10.3847/1538-4357/ac1514.en_US
dc.identifier.doihttps://doi.org/10.3847/1538-4357/ac1514en_US
dc.identifier.urihttps://hdl.handle.net/1911/111630en_US
dc.language.isoengen_US
dc.publisherIOP Publishingen_US
dc.titleUnderstanding Heating in Active Region Cores through Machine Learning. II. Classifying Observationsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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