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

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Area of Science:

  • Solar physics
  • Plasma astrophysics
  • Magnetohydrodynamics

Background:

  • Understanding energy deposition in solar active regions is crucial for solar corona physics.
  • Systematic comparisons between models and observations are needed to constrain heating frequencies.

Purpose of the Study:

  • To develop a forward modeling pipeline for active region emission.
  • To predict time-dependent emission for different nanoflare frequencies.
  • To analyze common diagnostics (emission measure slope, time lag) for signatures of heating frequency.

Main Methods:

  • Utilized magnetic field extrapolations and field-aligned hydrodynamic models.
  • Simulated emission from active region NOAA 1158 for low, intermediate, and high-frequency nanoflares.
  • Computed emission measure slope and time lag in predicted multi-wavelength, time-dependent images.

Main Results:

  • Heating frequency signatures were found in both emission measure slope and time lag diagnostics.
  • Emission measure slope distribution narrows and mean decreases with decreasing heating frequency.
  • Time lag becomes more spatially coherent with decreasing heating frequency and broader across the region with increasing frequency.

Conclusions:

  • The developed pipeline successfully predicts observable diagnostics sensitive to nanoflare heating frequency.
  • Results provide a basis for classifying real solar observations using these diagnostics.
  • This work contributes to understanding energy transport and deposition mechanisms in the solar corona.