Abstract

A lot of effort has gone into the development of sound physics-based stochastic solar dynamo models capable of explaining a wide variety of characteristic phenomena such as periodic cycles and occurrences of Grand Minima. However, model parameters need to be first calibrated to measured data in a consistent framework such as the Bayesian one. Unfortunately, Bayesian parameter calibration with stochastic models is a computationally very challenging task, requiring sophisticated algorithms and appropriate hardware. Moreover, a reliable parameter calibration for solar dynamo models has been hindered so far by the lack of data over a time span comparable to solar dynamics time scales, as the only available direct observations of long-term solar magnetic activity are sunspot number records, covering only about 300 years. However, time-series of cosmogenic radionuclides (Brehm et al. 2021, Usoskin et al. 2021) are proxies for solar magnetic activity on a millennial time scale, which can potentially shed light on several open questions, e.g., occurrences of Grand Minima and long-period cycles, and boost our understanding and predictive capabilities of the solar dynamo. We present here our first attempts to apply advanced Bayesian inference methods to the calibration of a stochastic delay differential equation solar dynamo model, and discuss hardships, perspectives and dreams.