To join this seminar virtually: Please request Zoom connection details from ea [at] stat.ubc.ca.
Abstract: Our Sun is dynamic with solar activity peaking approximately every 11 years. This rise in activity increases the chance of rare solar wind events (e.g. coronal mass ejections). The most recognizable effect of this rise in solar activity is the increase of planetary aurorae. We are currently approaching the peak of solar cycle 25 and in the last few months have observed several large coronal mass ejections arrive at Earth, spawning visible low-latitude aurora (including over Vancouver).
Spacecraft assets throughout the solar system observe our Sun and the solar wind. However, these datasets can only provide discontinuous spatiotemporal observations of a very large and dynamic system. Traditionally these observational assets are combined with high-fidelity physical models (e.g. magnetohydrodynamics). But these models are computationally expensive which limits the potential number of simulations. This bottleneck (sparse datasets, expensive forward physics-based models) is a ubiquitous challenge for inverse problems in the Earth and planetary sciences. In the case of Mars, this methodological bottleneck has limited our understanding of how the rise and fall of solar cycle activity affects planetary habitability.
In this presentation, I will pose estimating the solar wind during a recent rare solar wind event at Mars as an inverse problem. I will then discuss a Bayesian approach to this inverse problem using Gaussian processes as a low-fidelity emulator of a physics-based model and the scientific conclusions we are gaining about Mars from this effort. I will conclude with an outlook for simulation-based inference in Earth and planetary sciences.
Bio: Dr. Abigail (Abby) Azari is a Data Science Post-Doctoral Fellow in the Department of Earth, Ocean and Atmospheric Sciences where she works with Dr. Catherine Johnson (EOAS), Dr. Lindsey Heagy (EOAS), and Dr. Frank Wood (CS). Dr. Azari is a member of the NASA MAVEN Science Team; a spacecraft that has orbited Mars since 2014. Her research generally focuses on machine learning for scientific insights about planetary space environments. She was previously a postdoc at the UC Berkeley’s Space Sciences Lab. She received her Ph.D. in 2020 from the University of Michigan where she was an NSF Graduate Research Fellow and a NASA Earth and Space Sciences Fellow.
In January 2025, Dr. Azari will be joining the University of Alberta’s Physics and Electrical and Computer Engineering departments as an incoming faculty member and fellow of the Alberta Machine Intelligence Institute.