Anomalous electroweak physics unraveled via evidential deep learning
Abstract 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...
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SpringerOpen
2025-08-01
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| Series: | European Physical Journal C: Particles and Fields |
| Online Access: | https://doi.org/10.1140/epjc/s10052-025-14501-6 |
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| author | Brandon Kriesten T. J. Hobbs |
| author_facet | Brandon Kriesten T. J. Hobbs |
| author_sort | Brandon Kriesten |
| collection | DOAJ |
| description | Abstract 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 ( $$\nu $$ ν DIS). These scenarios are then quantitatively mapped, as a demonstration, alongside Monte Carlo replicas of the CT18 PDFs used to calculate the $$\varDelta \chi ^{2}$$ Δ χ 2 statistic for a typical multi-GeV $$\nu $$ ν 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. |
| format | Article |
| id | doaj-art-e6cdfbbc1bcb4655a16046039067ee5a |
| institution | Kabale University |
| issn | 1434-6052 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | European Physical Journal C: Particles and Fields |
| spelling | doaj-art-e6cdfbbc1bcb4655a16046039067ee5a2025-08-24T11:47:26ZengSpringerOpenEuropean Physical Journal C: Particles and Fields1434-60522025-08-0185811910.1140/epjc/s10052-025-14501-6Anomalous electroweak physics unraveled via evidential deep learningBrandon Kriesten0T. J. Hobbs1High Energy Physics Division, Argonne National LaboratoryHigh Energy Physics Division, Argonne National LaboratoryAbstract 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 ( $$\nu $$ ν DIS). These scenarios are then quantitatively mapped, as a demonstration, alongside Monte Carlo replicas of the CT18 PDFs used to calculate the $$\varDelta \chi ^{2}$$ Δ χ 2 statistic for a typical multi-GeV $$\nu $$ ν 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.https://doi.org/10.1140/epjc/s10052-025-14501-6 |
| spellingShingle | Brandon Kriesten T. J. Hobbs Anomalous electroweak physics unraveled via evidential deep learning European Physical Journal C: Particles and Fields |
| title | Anomalous electroweak physics unraveled via evidential deep learning |
| title_full | Anomalous electroweak physics unraveled via evidential deep learning |
| title_fullStr | Anomalous electroweak physics unraveled via evidential deep learning |
| title_full_unstemmed | Anomalous electroweak physics unraveled via evidential deep learning |
| title_short | Anomalous electroweak physics unraveled via evidential deep learning |
| title_sort | anomalous electroweak physics unraveled via evidential deep learning |
| url | https://doi.org/10.1140/epjc/s10052-025-14501-6 |
| work_keys_str_mv | AT brandonkriesten anomalouselectroweakphysicsunraveledviaevidentialdeeplearning AT tjhobbs anomalouselectroweakphysicsunraveledviaevidentialdeeplearning |