Quantitative Assessment of Earth’s Radiation Belt Electron Dynamics Using Physics-Based and Machine-Learning Approaches

Wed@Hariri: Meet Our Fellows Series

Wen Li, Assistant Professor, Astronomy, CAS

When:
Wednesday, November 20, 2019
Networking and refreshments 2:45pm-3:00pm, talk 3:00pm-4:00pm, reception to follow

Where:
Astronomy Building, CAS Room 502, 725 Commonwealth Avenue

Abstract:
Earth’s Van Allen radiation belts, which extend from ~1,000 to ~60,000 km above the Earth’s surface, consist of “killer” electrons (several hundred keV to several MeV) that can potentially cause satellite damage and occasionally failures. Accurate specification and prediction of radiation belt electron dynamics is thus important from both a practical and a scientific perspective but remains an outstanding challenge. Over the past decade, our understanding of the physical processes driving radiation belt electron dynamics has advanced significantly, the computational radiation belt models have improved dramatically, and the volume of data collected from various satellite missions in the near-Earth space has been growing rapidly. This talk will present recent advances in understanding, quantifying, and predicting radiation belt electron dynamics by taking full advantage of state-of-the-art physics-based modeling and the novel machine-learning technique. The improved radiation belt models are critical for forecasting space weather, which has broad impacts on our technological systems and society.

Bio: Wen Li is an Assistant Professor in the Astronomy department at Boston University. She received her Ph.D. from the University of California, Los Angeles. She is a recipient of the NSF CAREER Award, Alfred P. Sloan Research Fellow in Physics, and James B. Macelwane Medal from the American Geophysical Union. Her research interests include the generation of various plasma waves and their effects on energetic particle dynamics in the magnetosphere of the Earth and Jupiter, as well as their relation to solar wind activity. Prof. Li’s group uses computational models to simulate energetic particle dynamics due to wave-particle interactions and is also interested in applying machine learning techniques to specify and predict the state of the space environment by taking full advantage of various satellite data.