https://doi.org/10.1140/epjc/s10052-025-14501-6
Regular Article - Theoretical Physics
Anomalous electroweak physics unraveled via evidential deep learning
High Energy Physics Division, Argonne National Laboratory, 60439, Lemont, IL, USA
Received:
4
February
2025
Accepted:
7
July
2025
Published online:
19
August
2025
The ever-growing ecosystem of beyond standard model (BSM) calculations and parametrizations has motivated the development of systematic methods for making quantitative cross-comparisons over the wide range of possible models, especially with controllable uncertainties. In this setting, the language of uncertainty quantification (UQ) furnishes useful metrics for assessing statistical overlaps and discrepancies among BSM and related models. In this study, we leverage recent machine learning (ML) developments in evidential deep learning (EDL) for UQ to separate data (aleatoric) and knowledge (epistemic) uncertainties in a model-discrimination setting. We construct several potentially BSM-motivated scenarios for the anomalous electroweak interaction (AEWI) of neutrinos with nucleons in deep inelastic scattering (
DIS). These scenarios are then quantitatively mapped, as a demonstration, alongside Monte Carlo replicas of the CT18 PDFs used to calculate the 
statistic for a typical multi-GeV DIS experiment, CDHSW. Our framework effectively highlights areas of model agreement and provides a classification of out-of-distribution (OOD) samples. By offering the opportunity to quantitatively understand model overlaps, the approach presented in this work can help facilitate efficient BSM model exploration and exclusion for future New Physics searches.
© UChicago Argonne, LLC, Operator of Argonne National Laboratory, under exclusive licence to Springer-Verlag GmbH, DE 2025
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Funded by SCOAP3.
