PreprintCC BY 4.0arXiv
Incorporating deep neural networks into lepton-nucleus interaction modeling
Abstract
We present a scheme to incorporate precise inclusive cross-section predictions from a deep neural network trained on electron--carbon scattering data into a Monte Carlo event generator. The inclusive prediction is decomposed into independent exclusive interaction channels using the generator's theoretical models, and the resulting events are reweighted accordingly. We apply this scheme to investigate nuclear effects in inclusive and semi-exclusive electron-scattering measurements.
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BibTeX
@misc{prasad2026incorporating,
title = {{Incorporating deep neural networks into lepton-nucleus interaction modeling}},
author = {Hemant Prasad and Jan T. Sobczyk and Krzysztof M. Graczyk and Beata E. Kowal and Rwik Dharmapal Banerjee and J. Luis Bonilla},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.40912},
eprint = {2610.40912},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.40912},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Incorporating deep neural networks into lepton-nucleus interaction modeling
AU - Hemant Prasad
AU - Jan T. Sobczyk
AU - Krzysztof M. Graczyk
AU - Beata E. Kowal
AU - Rwik Dharmapal Banerjee
AU - J. Luis Bonilla
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.40912
UR - https://arxiv.org/abs/2610.40912
AB - We present a scheme to incorporate precise inclusive cross-section predictions from a deep neural network trained on electron--carbon scattering data into a Monte Carlo event generator. The inclusive prediction is decomposed into independent exclusive interaction channels using the generator's theoretical models, and the resulting events are reweighted accordingly. We apply this scheme to investigate nuclear effects in inclusive and semi-exclusive electron-scattering measurements.
ER -