First Estimation of Model Parameters for Neutrino-Induced Nucleon Knockout Using Simulation-Based Inference
To enable an accurate determination of oscillation parameters, accelerator-based neutrino experiments require detailed simulations of nuclear interaction physics in the GeV regime. While substantial effort from both theory and experiment is currently being invested to improve the fidelity of these simulations, their present deficiencies typically oblige experimental collaborations to resort to empirical tuning of simulation model parameters. As the precision requirements of the field continue to become more stringent, machine learning techniques may provide a powerful means of handling corresponding growth in the complexity of future neutrino interaction model tuning exercises. To study t...
| Authors | Karla Tame-Narvaez, Steven Gardiner, Aleksandra Ćiprijanović et al. |
|---|---|
| Primary category | hep-ph |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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