Physics and Astronomy Thesis Defense - Sapna Shekhar - Dartmouth College

Dartmouth Events

Physics and Astronomy Thesis Defense - Sapna Shekhar - Dartmouth College

Title: Spatial Extent of Relativistic Electron Precipitation from the Radiation Belts"

Monday, July 17, 2017
3:00pm-4:00pm
Wilder 202
Intended Audience(s): Public
Categories: Lectures & Seminars
 
Abstract: There are several processes that can lead to energization and loss of particles into the atmosphere from the outer radiation belts. Understanding of these processes will help us gain better understanding of the radiation belt variability and space weather. Estimation of the spatial extent and spectra of relativistic electron precipitation is important to be able to determine the contribution of atmospheric precipitation to global loss of particles. However, the spatial extent of REP has not been explored on a statistical level before. Also, little is known about the evolution of energy spectra of precipitating particles. In this thesis, the prime focus will be spatial extent of REP events. The thesis starts with determination of a statistical spatial scale in L shell and MLT for REP events detected by NOAA POES satellites over a duration of 15 years (2000-2014) using both single and multiple satellite observations and studying variations with respect to geomagnetic conditions (submitted to JGR). Then a method to be able to determine energy spectra with NOAA POES data which suffers from cross species contamination data has been formulated and applied to the same selection of events. Spectral variations and comparisons have been studied on a statistical scale. Further, the results have been validated with detailed case studies of events complementing NOAA POES data with wave data from GOES and Van Allen Probes, particle data and X ray data from Colorado Student Space Weather Experiment (CSSWE) and BARREL respectively, to study spectral and spatial scales of REP. The work paves the way towards spatial as well as temporal evolution of REP events using machine learning techniques for which preliminary results have been presented.
 
For more information, contact:
Tressena Manning
603-646-2854

Events are free and open to the public unless otherwise noted.