Presenter:	Simone Ulzega
Affiliation:	Zurich University of Applied Sciences (ZHAW)
Title:	Shedding light on the solar dynamo using Bayesian data science
Authors:	Simone Ulzega and Carlo Albert
Abstract:	We apply state-of-the-art Bayesian inference methods to the calibration of a stochastic delay differential equation model for the solar dynamo. Besides its substantial computational challenges, parameter estimation for dynamo models has traditionally been hindered by the lack of data on time scales matching the long-term variability of the Sun, since direct observations of solar magnetic activity are limited to centennial sunspot records. Time-series of cosmogenic radionuclides, however, are excellent proxies for solar magnetic activity on millennial time scales, which can potentially shed light on several open questions, such as the occurrence of Grand Minima and long-period cycles. Moreover, a deeper understanding of the physical mechanisms underlying the solar dynamo may ultimately boost our ability to predict solar cycles. Although this line of research is not directly connected to the planned EST activities, it highlights our competence in modelling and in extracting relevant information from complex data, an expertise directly transferable to the EST framework.
Last update:	06-10-2025