Speaker
Description
Heliophysics was born as a data-driven scientific field. From the discovery of the radiation belts and the confirmation of the existence of the solar wind, with the beginning of the space age, and until the latest discoveries in solar, magnetospheric, and ionospheric dynamics and coupling mechanisms, our field has always relied on the use of abundant in-situ and remote measurements to confirm physical processes and theoretical ideas, and to discover new phenomena. Thus, with every leap in technology, the field has greatly advanced. In recent years, the abundant data generated by ground and space-based and ground instruments, coupled with the increasing availability of such data in both long-term repositories and near-real time, has created both challenges and opportunities for new science and predictive modelling. In particular, the explosion of Data Science, Machine Learning Methods, and Artificial Intelligence algorithms to handle large datasets has been crucial to enable new scientific discoveries and develop low-cost data-driven models for Space Weather applications. While we are still in the beginning of the data-driven era of Space Weather, significant advancements have occurred in the past years, in part, due to the lower cost of running models in real time compared to the more traditional physics-based methods that usually require large data centers. This talk discusses the pivotal role of Machine Learning (ML) and Artificial Intelligence in navigating this data-rich era. I will present an overview of the latest scientific results in solar wind-magnetosphere coupling, with a focus on radiation belt processes. In particular, I will discuss recent results in MeV electron acceleration and losses during geomagnetically active times, as well as the effects of solar-wind driven perturbations in the inner-magnetosphere dynamics. The scientific results will then be put in the context of the recent efforts that have been made in using this physical knowledge and data availability for creating better predictive models in the Earth's inner magnetosphere.