Fast Surrogate for the Earth Matter Effect on Solar Neutrinos

Abstract

We present a deep-learning surrogate for the Earth matter effect on solar neutrinos. The model uses a residual convolutional network conditioned on the solar neutrino oscillation parameters and is trained on numerical solutions computed over a grid of parameter values. It predicts the Earth-induced transition probabilities across neutrino energy and zenith angle, providing a fast approximation to the direct numerical calculation. For the reference electron-neutrino survival probability, the surrogate achieves a pointwise relative accuracy of approximately $2\%$ and a speed-up of about a factor of 60 in a laptop-CPU test. Since the network learns only the Earth-crossing propagation, it can be applied without retraining to new-physics scenarios that leave this propagation unchanged. The surrogate can also be retrained using alternative numerical implementations or extended parameter sets, providing a flexible and computationally efficient approach for solar-neutrino analyses. The code and simulation data are publicly available at \href{https://github.com/AI-Driven-HEP/NuMatterSurrogate}{\faGithub}.