Inference of Heating Properties From “Hot” Non-Flaring Plasmas in Active Region Cores. Ii. Nanoflare Trains

Barnes, Will T.; Cargill, P. J.; Bradshaw, S. J.

doi:10.3847/1538-4357/833/2/217

Cited by 37 publications

(19 citation statements)

References 58 publications

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“…Due to the low-β nature of the corona, we can treat each field line traced from the field extrapolation as a thermally-isolated strand. We use the Enthalpy-based Thermal Evolution of Loops model (EBTEL, Klimchuk et al 2008;Cargill et al 2012a,b), specifically the two-fluid version of EBTEL (Barnes et al 2016a), to model the thermodynamic response of each strand. The two-fluid EBTEL code solves the timedependent, two-fluid hydrodynamic equations spatially-integrated over the corona for the electron pressure and temperature, ion pressure and temperature, and density.…”

Section: Hydrodynamic Modelingmentioning

confidence: 99%

Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables

Barnes

Bradshaw

Viall

2019

ApJ

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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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Section: Hydrodynamic Modelingmentioning

confidence: 99%

Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables

Barnes

Bradshaw

Viall

2019

ApJ

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“…Recent high-resolution observations of quiet-Sun magnetic fields are also favorable to the scenario for coronal heating by magnetic flux cancellation, in the context that a large amount of flux cancellation occurs frequently (i.e., at a rate of 10 15 Mx s −1 ) between dynamic, small-scale magnetic patches on the photosphere (e.g., Chitta et al 2017). The short-term variations seen in the 5.2 hr profile of dW/dt have timescales of a few to several tens of minutes, comparable to the waiting time between successive nanoflares as suggested by hydrodynamic simulations of impulsive coronal heating (e.g., Cargill 2014; Barnes et al 2016). It should be noted, however, that flux cancellation events such as the one examined here should be distributed all over the solar surface with a variety of temporal and spatial scales, and they ought to occur at suitable rates to contribute the required energy dissipation rate, or a significant portion thereof, in order to maintain the coronal temperature of the quiet Sun.…”

Section: Discussionmentioning

confidence: 82%

An Observational Test of Solar Plasma Heating by Magnetic Flux Cancellation

Park

2020

Preprint

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Recent observations suggest that magnetic flux cancellation may play a crucial role in heating the Sun's upper atmosphere (chromosphere, transition region, corona). Here, we intended to validate an analytic model for magnetic reconnection and consequent coronal heating, driven by a pair of converging and cancelling magnetic flux sources of opposite polarities. For this test, we analyzed photospheric magnetic field and multiwavelength UV/EUV observations of a small-scale flux cancellation event in a quiet-Sun internetwork region over a target interval of 5.2 hr. The observed cancellation event exhibits a converging motion of two opposite-polarity magnetic patches on the photosphere and red-shifted Doppler velocities (downflows) therein consistently over the target interval, with a decrease in magnetic flux of both polarities at a rate of 10 15 Mx s −1 . Several impulsive EUV brightenings, with differential emission measure values peaked at 1.6 -2.0 MK, are also observed in the shape of arcades with their two footpoints anchored in the two patches. The rate of magnetic energy released as heat at the flux cancellation region is estimated to be in the range of (0.2 -1)×10 24 erg s −1 over the target interval, which can satisfy the requirement of previously reported heating rates for the quiet-Sun corona. Finally, both short-term (a few to several tens of minutes) variations and long-term (a few hours) trends in the magnetic energy release rate are clearly shown in the estimated rate of radiative energy loss of electrons at temperatures above 2.0 MK. All these observational findings support the validity of the investigated reconnection model for plasma heating in the upper solar atmosphere by flux cancellation.

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“…In addition to computing the average coronal properties, EBTEL also determines the coronal and transition region Differential Emission Measures (DEMs) at each time step. Here we use the EBTEL++ implementation described in (Barnes et al 2016a) and available online at https://github.com/rice-solar-physics/ebtelPlusPlus.…”

Section: Ebtel Hydrodynamic Simulationsmentioning

confidence: 99%

Transition region contribution to AIA observations in the context of coronal heating

Schonfeld,

Klimchuk

2020

Preprint

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We investigate the relative contributions from the transition region and corona of coronal loops observed by the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO). Using EBTEL (Enthalpy-Based Thermal Evolution of Loops) hydrodynamic simulations, we model loops with multiple lengths and energy fluxes heated randomly by events drawn from power-law distributions with different slopes and minimum delays between events to investigate how each of these parameters influences observable loop properties. We generate AIA intensities from the corona and transition region for each realization. The variations within and between models generated with these different parameters illustrate the sensitivity of narrowband imaging to the details of coronal heating.We then analyze the transition region and coronal emission from a number of observed active regions and find broad agreement with the trends in the models. In both models and observations, the transition region brightness is significant, often greater than the coronal brightness in all six "coronal" AIA channels. We also identify an inverse relationship, consistent with heating theories, between the slope of the differential emission measure (DEM) coolward of the peak temperature and the observed ratio of coronal to transition region intensity. These results highlight the use of narrowband observations and the importance of properly considering the transition region in investigations of coronal heating.

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Inference of Heating Properties From “Hot” Non-Flaring Plasmas in Active Region Cores. Ii. Nanoflare Trains

Cited by 37 publications

References 58 publications

Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables

Understanding Heating in Active Region Cores through Machine Learning. I. Numerical Modeling and Predicted Observables

An Observational Test of Solar Plasma Heating by Magnetic Flux Cancellation

Transition region contribution to AIA observations in the context of coronal heating

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