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Register a new userUnderstanding Heating in Active Region Cores through Machine Learning I. Numerical Modeling and Predicted Observables
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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 as observed by SDO/AIA 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 AIA observations of NOAA 1158 in terms of the underlying heating frequency.
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Constraining 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.
Using data from the Extreme-ultraviolet Imaging Spectrometer aboard Hinode, we have studied the coronal plasma in the core of two active regions. Concentrating on the area between opposite polarity moss, we found emission measure distributions having an approximate power-law form EM$propto T^{2.4}$ from $log,T = 5.5$ up to a peak at $log,T = 6.55$. We show that the observations compare very favorably with a simple model of nanoflare-heated loop strands. They also appear to be consistent with more sophisticated nanoflare models. However, in the absence of additional constraints, steady heating is also a viable explanation.
The properties expected of hot non-flaring plasmas due to nanoflare heating in active regions are investigated using hydrodynamic modeling tools, including a two-fluid development of the EBTEL code. Here we study a single nanoflare and show that while simple models predict an emission measure distribution extending well above 10 MK that is consistent with cooling by thermal conduction, many other effects are likely to limit the existence and detectability of such plasmas. These include: differential heating between electrons and ions, ionization non-equilibrium and, for short nanoflares, the time taken for the coronal density to increase. The most useful temperature range to look for this plasma, often called the smoking gun of nanoflare heating, lies between $10^{6.6}$ and $10^7$ K. Signatures of the actual heating may be detectable in some instances.
We present a study of the frequency of transient brightenings in the core of solar active regions as observed in the Fe XVIII line component of AIA/SDO 94 A filter images. The Fe XVIII emission is isolated using an empirical correction to remove the contribution of warm emission to this channel. Comparing with simultaneous observations from EIS/Hinode, we find that the variability observed in Fe XVIII is strongly correlated with the emission from lines formed at similar temperatures. We examine the evolution of loops in the cores of active regions at various stages of evolution. Using a newly developed event detection algorithm we characterize the distribution of event frequency, duration, and magnitude in these active regions. These distributions are similar for regions of similar age and show a consistent pattern as the regions age. This suggests that these characteristics are important constraints for models of solar active regions. We find that the typical frequency of the intensity fluctuations is about 1400s for any given line-of-sight, i.e. about 2-3 events per hour. Using the EBTEL 0D hydrodynamic model, however, we show that this only sets a lower limit on the heating frequency along that line-of-sight.
Despite its prediction over two decades ago, the detection of faint, high-temperature (hot) emission due to nanoflare heating in non-flaring active region cores has proved challenging. Using an efficient two-fluid hydrodynamic model, this paper investigates the properties of the emission expected from repeating nanoflares (a nanoflare train) of varying frequency as well as the separate heating of electrons and ions. If the emission measure distribution ($mathrm{EM}(T)$) peaks at $T = T_m$, we find that $mathrm{EM}(T_m)$ is independent of details of the nanoflare train, and $mathrm{EM}(T)$ above and below $T_m$ reflects different aspects of the heating. Below $T_m$ the main influence is the relationship of the waiting time between successive nanoflares to the nanoflare energy. Above $T_m$ power-law nanoflare distributions lead to an extensive plasma population not present in a monoenergetic train. Furthermore, in some cases characteristic features are present in $mathrm{EM}(T)$. Such details may be detectable given adequate spectral resolution and a good knowledge of the relevant atomic physics. In the absence of such resolution we propose some metrics that can be used to infer the presence of hot plasma.
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