Time-resolved emission from bright hot pixels of an active region observed in the EUV band with SDO/AIA and multi-stranded loop modeling

TL;DR

This study analyzes EUV light curves of an active solar region to understand coronal loop heating, using multi-stranded loop models and machine learning to identify nanoflare signatures and their properties.

Contribution

It introduces a combined modeling and AI approach to constrain nanoflare heating parameters in active region loops from high-resolution EUV observations.

Findings

Best fit with ~1000 short-duration nanoflares (<1 min)

Shallow power-law distribution of energy pulses (index 1.5)

Detection of heating excess prior to observed light curve bumps

Abstract

Evidence for small amounts of very hot plasma has been found in active regions and might be the indication of an impulsive heating, released at spatial scales smaller than the cross section of a single loop. We investigate the heating and substructure of coronal loops in the core of one such active region by analyzing the light curves in the smallest resolution elements of solar observations in two EUV channels (94 A and 335 A) from the Atmospheric Imaging Assembly on-board the Solar Dynamics Observatory. We model the evolution of a bundle of strands heated by a storm of nanoflares by means of a hydrodynamic 0D loop model (EBTEL). The light curves obtained from the random combination of those of single strands are compared to the observed light curves either in a single pixel or in a row of pixels, simultaneously in the two channels and using two independent methods: an artificial intelligent system (Probabilistic Neural Network, PNN) and a simple cross-correlation technique. We explore the space of the parameters to constrain the distribution of the heat pulses, their duration and their spatial size, and, as a feedback on the data, their signatures on the light curves. From both methods the best agreement is obtained for a relatively large population of events (1000) with a short duration (less than 1 min) and a relatively shallow distribution (power law with index 1.5) in a limited energy range (1.5 decades). The feedback on the data indicates that bumps in the light curves, especially in the 94 A channel, are signatures of a heating excess that occurred a few minutes before.