Can a Long Nanoflare Storm Explain the Observed Emission Measure Distributions in Active Region Cores?

Abstract

All theories that attempt to explain the heating of the high temperature plasma observed in the solar corona are based on short bursts of energy. The intensities and velocities measured in the cores of quiescent active regions, however, can be steady over many hours of observation. One heating scenario that has been proposed to reconcile such observations with impulsive heating models is the "long nanoflare storm," where short duration heating events occur infrequently on many sub-resolutions strands; the emission of the strands is then averaged together to explain the observed steady structures. In this Letter, we examine the emission measure distribution predicted for such a long nanoflare storm by modeling an arcade of strands in an active region core. Comparisons of the computed emission measure distributions with recent observations indicate that that the long nanoflare storm scenario implies greater than 5 times more 1 MK emission than is actually observed for all plausible combinations of loop lengths, heating rates, and abundances. We conjecture that if the plasma had "super coronal" abundances, the model may be able to match the observations at low temperatures.

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• Inference of Heating Properties from "Hot" Non-flaring Plasmas in Active Region Cores. II. Nanoflare Trains
• Determining heating time scales in solar active region cores from AIA/SDO Fe XVIII images
• Patterns of Activity in a Global Model of a Solar Active Region
• The Evolution of the EM Distribution in the Core of an Active Region
• The role of radiative losses in the late evolution of pulse-heated coronal loops/strands
• Understanding Heating in Active Region Cores through Machine Learning I. Numerical Modeling and Predicted Observables
• Collisional and Radiative Processes in Optically Thin Plasmas
• Heating Mechanisms for Intermittent Loops in Active Region Cores from AIA/SDO EUV Observations
• Understanding Heating in Active Region Cores through Machine Learning II. Classifying Observations
• Temperature and Differential Emission Measure Profiles in Turbulent Solar Active Region Loops