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Register a new userInference of Heating Properties from Hot Non-flaring Plasmas in Active Region Cores. II. Nanoflare Trains
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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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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.
The time-dependence of heating in solar active regions can be studied by analyzing the slope of the emission measure distribution cool-ward of the peak. In a previous study we showed that low-frequency heating can account for 0% to 77% of active region core emission measures. We now turn our attention to heating by a finite succession of impulsive events for which the timescale between events on a single magnetic strand is shorter than the cooling timescale. We refer to this scenario as a nanoflare train and explore a parameter space of heating and coronal loop properties with a hydrodynamic model. Our conclusions are: (1) nanoflare trains are consistent with 86% to 100% of observed active region cores when uncertainties in the atomic data are properly accounted for; (2) steeper slopes are found for larger values of the ratio of the train duration $Delta_H$ to the post-train cooling and draining timescale $Delta_C$, where $Delta_H$ depends on the number of heating events, the event duration and the time interval between successive events ($tau_C$); (3) $tau_C$ may be diagnosed from the width of the hot component of the emission measure provided that the temperature bins are much smaller than 0.1 dex; (4) the slope of the emission measure alone is not sufficient to provide information about any timescale associated with heating - the length and density of the heated structure must be measured for $Delta_H$ to be uniquely extracted from the ratio $Delta_H/Delta_C$.
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.
We use coronal imaging observations with SDO/AIA, and Hinode/EIS spectral data, to explore the potential of narrow band EUV imaging data for diagnosing the presence of hot (T >~5MK) coronal plasma in active regions. We analyze observations of two active regions (AR 11281, AR 11289) with simultaneous AIA imaging, and EIS spectral data, including the CaXVII line (at 192.8A) which is one of the few lines in the EIS spectral bands sensitive to hot coronal plasma even outside flares. After careful coalignment of the imaging and spectral data, we compare the morphology in a 3 color image combining the 171, 335, and 94A AIA spectral bands, with the image obtained for CaXVII emission from the analysis of EIS spectra. We find that in the selected active regions the CaXVII emission is strong only in very limited areas, showing striking similarities with the features bright in the 94A (and 335A) AIA channels and weak in the 171A band. We conclude that AIA imaging observations of the solar corona can be used to track hot plasma (6-8MK), and so to study its spatial variability and temporal evolution at high spatial and temporal resolution.
In this work we investigate the thermal structure of an off-limb active region in various non-flaring areas, as it provides key information on the way these structures are heated. In particular, we concentrate in the very hot component (>3 MK) as it is a crucial element to discriminate between different heating mechanisms. We present an analysis using Fe and Ca emission lines from both SOHO/SUMER and HINODE/EIS. A dataset covering all ionization stages from Fe X to Fe XIX has been used for the thermal analysis (both DEM and EM). Ca XIV is used for the SUMER-EIS radiometric cross-calibration. We show how the very hot plasma is present and persistent almost everywhere in the core of the limb AR. The off-limb AR is clearly structured in Fe XVIII. Almost everywhere, the EM analysis reveals plasma at 10 MK (visible in Fe XIX emission) which is down to 0.1% of EM of the main 3 MK plasma. We estimate the power law index of the hot tail of the EM to be between -8.5 and -4.4. However, we leave an open question on the possible existence of a small minor peak at around 10 MK. The absence in some part of the AR of Fe XIX and Fe XXIII lines (which fall into our spectral range) enables us to determine an upper limit on the EM at such temperatures. Our results include a new Ca XIV 943.59 AA~ atomic model.
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