Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification

Solar extreme-ultraviolet (EUV) irradiance plays a crucial role in heating the Earth’s ionosphere, thermosphere, and mesosphere, affecting atmospheric dynamics over varying time scales. Although significant effort has been spent studying short-term EUV variations from solar transient events, there is little work to explore the long-term evolution of the EUV flux over multiple solar cycles. Continuous EUV flux measurements have only been available since 1995, leaving significant gaps in earlier data. In this study, we propose a Bayesian deep learning model, named SEMNet, to fill the gaps. We validate our approach by applying SEMNet to construct Solar and Heliospheric Observatory/Solar EUV Monitor EUV flux measurements in the period between 1998 and 2014 using Ca II K images from the Precision Solar Photometric Telescope. We then extend SEMNet through transfer learning to reconstruct solar EUV irradiance in the period between 1950 and 1960 using Ca II K images from the Kodaikanal Solar Observatory. Experimental results show that SEMNet provides reliable predictions along with uncertainty bounds, demonstrating the feasibility of Ca II K images as a robust proxy for long-term EUV fluxes. These findings contribute to a better understanding of solar influences on Earth’s climate over extended periods.