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}.
Related
- Anomalous Matter Potentials in Earth's Inner Core: A Phenomenological Probe of Dark Matter and Light Sterile Neutrinos
- Can Nuclear Recoils and Solar Neutrinos Resolve the Composition of Singlino-Higgsino Dark Matter?
- Fast and Differentiable MHD Surrogate for Solar Wind using Neural Operator
- Testing Higgs-Coupled Minimal Dark Matter with Solar Neutrinos after the LZ High-Recoil Event
- Searches for Light Dark Matter and Evidence of Coherent Elastic Neutrino-Nucleus Scattering of Solar Neutrinos with the LUX-ZEPLIN (LZ) Experiment
BibTeX
@misc{ansarifard2026fast,
title = {{Fast Surrogate for the Earth Matter Effect on Solar Neutrinos}},
author = {Saeed Ansarifard},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.25229},
eprint = {2609.25229},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.25229},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Fast Surrogate for the Earth Matter Effect on Solar Neutrinos
AU - Saeed Ansarifard
PY - 2026
DA - 2026/09/21
PB - arXiv
DO - 10.48550/arxiv.2609.25229
UR - https://arxiv.org/abs/2609.25229
AB - 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}.
ER -