Reconstruction of Solar EUV Irradiance Using CaII K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification

2025-08-09

AI Summary

A critical gap exists in direct solar extreme ultraviolet (EUV) irradiance observations prior to 1995, hindering long-term studies of solar radiative variability across multiple solar cycles. Method: We propose SEMNet, the first Bayesian deep learning model integrating cross-era transfer learning, jointly trained on ground-based Ca II K full-disk images (1950–2014) and space-based SOHO/SEM EUV measurements (1996–2014). It enables high-fidelity data reconstruction with rigorous uncertainty quantification. Contribution/Results: SEMNet successfully reconstructs the SOHO/SEM EUV time series for 1998–2014 and robustly extrapolates it back to 1950–1960, yielding a physically consistent, statistically verifiable multi-cycle EUV evolution record with well-defined confidence intervals. This constitutes the first deep probabilistic proxy dataset for solar EUV irradiance, providing essential input for long-term solar radiation studies and space weather–climate modeling.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Machine Learning: Semi-Supervised Learning
• Data Mining & Knowledge Management: Semantic Web

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Models for Web evolution

Abstract

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 SOHO/SEM EUV flux measurements in the period between 1998 and 2014 using CaII 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 CaII K images from the Kodaikanal Solar Observatory. Experimental results show that SEMNet provides reliable predictions along with uncertainty bounds, demonstrating the feasibility of CaII 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.

Problem

• Reconstruct long-term solar EUV irradiance using historical data
• Fill gaps in pre-1995 EUV flux measurements
• Quantify uncertainty in EUV predictions with Bayesian deep learning

Innovation

• Bayesian deep learning for EUV reconstruction
• Transfer learning extends historical EUV data
• Uncertainty quantification in solar irradiance predictions