03/07/2024
By Lynne Schaufenbil

The Lowell Center for Space Science and Technology is pleased to announce the virtual seminar, Prediction of Solar Transient Events with Machine Learning, which will be presented on Thursday, March 14 at 11 a.m.

Abstract: The term “Space Weather” serves as an overarching umbrella for describing complex interactions of the Earth and its magnetosphere and atmosphere with the radiation, plasmas, and energetic particle flows from the Sun. As for the regular weather on Earth, we experience mild breezes of the solar wind during the quiet periods (corresponding to the solar activity minimum) and severe geomagnetic and radiation storms during the ‘hurricane’ seasons (solar activity maximum). I will start this talk with an attempt to overview the core effects of space weather on the terrestrial environment, both the solar-cycle-long and caused by rapid solar transient events (such as solar flares, coronal mass ejections, and solar energetic particles, SEPs). I will then highlight the current developments in the field of the prediction of solar transient events, with a stronger focus on the development and utilization of novel Artificial Intelligence / Machine Learning (AI/ML) techniques for data-driven predictions of solar flares and SEPs and related data homogenization efforts. At the end of the talk, I will discuss the challenges associated with solar transient event forecasting (such as rare sampling of extreme events, limited observational data span, and forecasting operationalization and performance improvement) and the subjective views on how to address them.

Bio: Viacheslav Sadykov is an Assistant Professor at the Physics & Astronomy Department of Georgia State University. He obtained his Ph.D. degree from the New Jersey Institute of Technology in 2019, worked as a Research Scientist at BAERI / NASA ARC from 2019 until 2021, and joined the faculty of the Physics & Astronomy Department of Georgia State University in 2021. Viacheslav has wide research interests including the development and maintenance of databases of solar flares and flight radiation measurements, prediction of solar transient events using machine learning, machine learning-aided analysis of the spectroscopic observations of the Sun, modeling of solar spectral lines and EUV emission, and analysis of the realistic radiative MHD simulations of the quiet Sun.

Please contact Lynne_Schaufenbil@uml.edu if you are interested in attending.