Investigating Whistler‐Mode Wave Intensity Along Field Lines Using Electron Precipitation Measurements

Abstract

Electron fluxes in Earth's radiation belts are significantly affected by their resonant interaction with whistler‐mode waves. This wave‐particle interaction often occurs via first cyclotron resonance and, when intense and nonlinear, can accelerate subrelativistic electrons to relativistic energies while also scattering them into the atmospheric loss cone. Here, we model Electron Losses and Fields INvestgation’s (ELFIN) low‐altitude satellite measurements of precipitating electron spectra with a wave‐electron interaction model to infer the profiles of whistler‐mode intensity along magnetic latitude assuming realistic waveforms and statistical models of plasma density. We then compare these profiles with a wave power spatial distribution along field lines from an empirical model. We find that this empirical model is consistent with observations of subrelativistic (200 keV) electron precipitation events at all MLTs, especially on the nightside. This may be due to the sparse coverage of wave measurements at mid‐to‐high latitudes which causes statistically averaged wave power to be likely underestimated in current empirical wave models. As a result, this discrepancy suggests that intense waves likely do propagate to higher latitudes, although further investigation is required to quantify how well this high‐latitude population can account for the observed relativistic electron precipitation.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 01, 2023
Source ID
10.1029/2023ja031578

Entities

People

  • Anton V. Artemyev
  • Ethan Tsai
  • Vassilis Angelopoulos
  • Xiao-Jia Zhang

Organizations

  • Air Force Office of Scientific Research
  • National Aeronautics and Space Administration
  • National Science Foundation
  • University of California, Los Angeles
  • University of Texas at Dallas

Tags

Fields of Study

  • Physics

Readers

  • Computational Modeling and Simulation
  • Solar Physics
  • Space/Atmospheric Physics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Microelectronics
  • Space