Coronal Hole Detection and Open Magnetic Flux

Abstract

Many scientists use coronal hole (CH) detections to infer open magnetic flux. Detection techniques differ in the areas that they assign as open, and may obtain different values for the open magnetic flux. We characterize the uncertainties of these methods, by applying six different detection methods to deduce the area and open flux of a near-disk center CH observed on 2010 September 19, and applying a single method to five different EUV filtergrams for this CH. Open flux was calculated using five different magnetic maps. The standard deviation (interpreted as the uncertainty) in the open flux estimate for this CH ≈ 26%. However, including the variability of different magnetic data sources, this uncertainty almost doubles to 45%. We use two of the methods to characterize the area and open flux for all CHs in this time period. We find that the open flux is greatly underestimated compared to values inferred from in situ measurements (by 2.2–4 times). We also test our detection techniques on simulated emission images from a thermodynamic MHD model of the solar corona. We find that the methods overestimate the area and open flux in the simulated CH, but the average error in the flux is only about 7%. The full-Sun detections on the simulated corona underestimate the model open flux, but by factors well below what is needed to account for the missing flux in the observations. Under-detection of open flux in coronal holes likely contributes to the recognized deficit in solar open flux, but is unlikely to resolve it.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 31, 2021
Source ID
10.3847/1538-4357/ac090a

Entities

People

  • Bojan Vrsnak
  • Camilla Scolini
  • Charles Arge
  • Cooper Downs
  • Eleanna Asvestari
  • Evangelia Samara
  • Immanuel Christopher Jebaraj
  • J. A. Linker
  • Jens Pomoell
  • M. S. Madjarska
  • Manuela Temmer
  • Mathew Owens
  • Ronald M Caplan
  • Rui F. Pinto
  • Stefan J Hofmeister
  • Stephan G Heinemann
  • Véronique Delouille

Organizations

  • Air Force Office of Scientific Research

Tags

Fields of Study

  • Environmental science

Readers

  • Computational Modeling and Simulation
  • Quantum Chemistry
  • Solar Physics

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference