HEATING MECHANISMS FOR INTERMITTENT LOOPS IN ACTIVE REGION CORES FROM AIA/<i>SDO</i>EUV OBSERVATIONS

Cadavid, A. C.; Lawrence, J. K.; Christian, D. J.; Jess, D. B.; Nigro, G.

doi:10.1088/0004-637x/795/1/48

Cited by 12 publications

(10 citation statements)

References 57 publications

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“…Though we have not shown them here, similar negative time lag signatures are present in nearly all of the other 131Å channel pairs as well. These results are consistent with that of Cadavid et al (2014) who found that in inter-moss regions of active region NOAA 11250, intensity variations in the 131Å channel preceded brightenings in all other EUV channels. In the two control cases, we do not find any negative time lags as the cross-correlations in the core are dominated by uninterrupted cooling from 211Å to the cool part of 131Å.…”

Section: Time Lag Mapssupporting

confidence: 93%

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: Time Lag Mapssupporting

confidence: 93%

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

Barnes

Bradshaw

Viall

2019

ApJ

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“…2006, 2012; Cadavid et al. 2014) and coronal mass ejections (Gosling 2010). Analogously, in the Earth’s magnetosphere (Drake et al.…”

Section: Introductionmentioning

confidence: 99%

“…In this scenario, the structures and phenomena related to turbulence, such as current sheets and reconnecting magnetic islands (Matthaeus & Lamkin 1986;Greco et al 2009a;Servidio et al 2011b), magnetic field topology changes and energy conversion (Parker 1957;Matthaeus, Ambrosiano & Goldstein 1984;Ambrosiano et al 1988;Servidio et al 2009Servidio et al , 2015, mainly occur due to dynamics in the plane perpendicular to the main field (Bruno & Carbone 2016). Currently, magnetic reconnection is considered one of the most effective mechanisms for particle acceleration and energization (Zank et al 2014), being crucial for explosive events in the solar atmosphere, like solar flares (Cargill et al 2006(Cargill et al , 2012Cadavid et al 2014) and coronal mass ejections (Gosling 2010). Analogously, in the Earth's magnetosphere (Drake et al 2006;Oka et al 2010;Birn et al 2012) and in the distant outer heliosphere (Lazarian & Opher 2009), magnetic reconnection is thought to be a very active mechanism.…”

Section: Introductionmentioning

confidence: 99%

Ion diffusion and acceleration in plasma turbulence

et al. 2018

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Particle transport, acceleration and energisation are phenomena of major importance for both space and laboratory plasmas. Despite years of study, an accurate theoretical description of these effects is still lacking. Validating models with self-consistent, kinetic simulations represents today a new challenge for the description of weakly-collisional, turbulent plasmas. We perform two-dimensional (2D) hybrid-PIC simulations of steadystate turbulence to study the processes of diffusion and acceleration. The chosen plasma parameters allow to span different systems, going from the solar corona to the solar wind, from the Earth's magnetosheath to confinement devices. To describe the ion diffusion, we adapted the Nonlinear Guiding Center (NLGC) theory to the 2D case. Finally, we investigated the local influence of coherent structures on particle energisation and acceleration: current sheets play an important role if the ions Larmor radii are on the order of the current sheets size. This resonance-like process leads to the violation of the magnetic moment conservation, eventually enhancing the velocity-space diffusion.PACS codes: Authors should not enter PACS codes directly on the manuscript, as these must be chosen during the online submission process and will then be added during the typesetting process (see http://www.aip.org/pacs/ for the full list of PACS codes)

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“…It is widely accepted that warm loops are heated by impulsive processes (Winebarger et al 2002b;Warren et al 2003;Cargill & Klimchuk 2004;Winebarger & Warren 2005;Klimchuk 2006Klimchuk , 2009Tripathi et al 2009;Ugarte-Urra et al 2009). However, for hot loops, both impulsive (Tripathi et al 2010;Viall & Klimchuk 2011; Ugarte-Urra & Warren 2014; Cadavid et al 2014) and steady heating (Warren et al 2010;Winebarger et al 2011) have been suggested.…”

Section: Introductionmentioning

confidence: 99%

COOL TRANSITION REGION LOOPS OBSERVED BY THEINTERFACE REGION IMAGING SPECTROGRAPH

Huang

Xia

et al. 2015

ApJ

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We report on the first Interface Region Imaging Spectrograph (IRIS) study of cool transition region loops. This class of loops has received little attention in the literature, mainly due to instrumental limitations. A cluster of such loops was observed on the solar disk in active region NOAA11934, in the Si iv 1402.8 Å spectral raster and 1400 Å slit-jaw (SJ) images. We divide the loops into three groups and study their dynamics and interaction. The first group comprises relatively stable loops, with 382-626 km cross-sections. Observed Doppler velocities are suggestive of siphon flows, gradually changing from −10 km s −1 at one end to 20 km s −1 at the other end of the loops. Nonthermal velocities from 15 km s −1 to 25 km s −1 were determined. These physical properties suggest that these loops are impulsively heated by magnetic reconnection occurring at the blue-shifted footpoints where magnetic cancellation with a rate of 10 15 Mx s −1 is found. The released magnetic energy is redistributed by the siphon flows. The second group corresponds to two footpoints rooted in mixed-magnetic-polarity regions, where magnetic cancellation occurred at a rate of 10 15 Mx s −1 and line profiles with enhanced wings of up to 200 km s −1 were observed. These are suggestive of explosive-like events. The Doppler velocities combined with the SJ images suggest possible anti-parallel flows in finer loop strands. In the third group, interaction between two cool loop systems is observed. Evidence for magnetic reconnection between the two loop systems is reflected in the line profiles of explosive events, and a magnetic cancellation rate of 3 × 10 15 Mx s −1 observed in the corresponding area. The IRIS observations have thus opened a new window of opportunity for in-depth investigations of cool transition region loops. Further numerical experiments are crucial for understanding their physics and their role in the coronal heating processes.

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HEATING MECHANISMS FOR INTERMITTENT LOOPS IN ACTIVE REGION CORES FROM AIA/SDOEUV OBSERVATIONS

Cited by 12 publications

References 57 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

Ion diffusion and acceleration in plasma turbulence

COOL TRANSITION REGION LOOPS OBSERVED BY THEINTERFACE REGION IMAGING SPECTROGRAPH

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