[Loops] Recent paper on nano flare heating classification

[Will Barnes, Contractor, Code7680]

Dear loops enthusiasts,

I hope this email finds you well and that we are able to see each other in person in the near future. I wanted to bring your attention to a paper that was recently published in ApJ by myself, Steve Bradshaw, and Nicki Viall, “Understanding Heating in Active Region Cores through Machine Learning II. Classifying Observations.” This is a follow up to our first paper in this series from 2019<https://doi.org/10.3847/1538-4357/ab290c> which presented the modeling portion of this work. It builds upon the results of our first paper by showing how our modeled emission measure slopes and time lags can be used to systematically classify observations of active region cores in terms of heating frequency.

The title, abstract, and arXiv and ApJ links are included below. Those of you who attended the last loops workshop in St Andrews in 2019 may recognize some of the results!

Title: Understanding Heating in Active Region Cores through Machine Learning. II. Classifying Observations
ApJ: https://doi.org/10.3847/1538-4357/ac1514
arXiv: https://arxiv.org/abs/2107.07612
Abstract: To adequately constrain the frequency of energy deposition in active region cores in the solar corona, systematic comparisons between detailed models and observational data are needed. In this paper, we describe a pipeline for forward modeling active region emission using magnetic field extrapolations and field-aligned hydrodynamic models. We use this pipeline to predict time-dependent emission from active region NOAA 1158 for low-, intermediate-, and high-frequency nanoflares. In each pixel of our predicted multi-wavelength, time-dependent images, we compute two commonly used diagnostics: the emission measure slope and the time lag. We find that signatures of the heating frequency persist in both of these diagnostics. In particular, our results show that the distribution of emission measure slopes narrows and the mean decreases with decreasing heating frequency and that the range of emission measure slopes is consistent with past observational and modeling work. Furthermore, we find that the time lag becomes increasingly spatially coherent with decreasing heating frequency while the distribution of time lags across the whole active region becomes more broad with increasing heating frequency. In a follow-up paper, we train a random forest classifier on these predicted diagnostics and use this model to classify real observations of NOAA 1158 in terms of the underlying heating frequency.

Best,

Will

Dr. Will Barnes
NRC Research Associate
Space Science Division
U.S. Naval Research Laboratory
Code 7680

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