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Machine learning model for neutrino events calibrated with experimental data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a machine-learning-based generative model for neutrino-nucleus scattering events. This model can be calibrated using experimental measurements of differential cross sections, even without needing event-level data. By fine-tuning the model to a specific measurement from the MicroBooNE experiment, the researchers demonstrated improved accuracy for observables not included in the calibration. The calibrated model also proved effective for different neutrino fluxes and beam configurations, maintaining accurate predictions for outgoing muon kinematics. AI

IMPACT This research demonstrates a novel application of machine learning for scientific simulation and calibration, potentially improving the efficiency and accuracy of physics experiments.

RANK_REASON The cluster contains an arXiv preprint detailing a new research methodology in physics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning model for neutrino events calibrated with experimental data

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The cluster contains an arXiv preprint detailing a new research methodology in physics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jose Luis Bonilla, Krzysztof M. Graczyk, Rwik Dharmapal Banerjee, Beata E. Kowal, Hemant Prasad, Jan T. Sobczyk ·

    Machine-learning-based neutrino-event generation constrained by experimental measurements

    arXiv:2610.20870v1 Announce Type: cross Abstract: We present a generative surrogate for charged-current \(\nu_\mu\)-nucleus scattering that can be calibrated to measured differential cross sections without event-level experimental samples. Starting from a theoretical interaction …

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