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First estimation of model parameters for neutrino-induced nucleon knockout using simulation-based inference

Karla Tame-Narvaez1, Steven Gardiner1, Aleksandra Ćiprijanović1,2,3, and Giuseppe Cerati1

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Phys. Rev. D 114, 052014 – Published 23 September, 2026

DOI: https://doi.org/10.1103/tzhn-7tcq

Abstract

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 the suitability of simulation-based inference (SBI) for this physics application, in this paper we revisit a tuned configuration of the genie neutrino configuration generator that was originally developed by the MicroBooNE collaboration. Despite closely reproducing the adopted values of four physics parameters when confronted with the tuned cross-section predictions as input, we find that our trained SBI algorithm prefers modestly different values (within MicroBooNE’s assigned uncertainties) and achieves slightly better goodness-of-fit when inference is run on the experimental data set originally used by MicroBooNE. We also find that our trained algorithm can create a fair approximation of an alternative neutrino scattering simulation, nuwro, that shares only a subset of its physics model parameters with genie.

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Phys. Rev. D 114, 052014– Published 23 September, 2026
  • Received 18 March 2026
  • Revised 14 August 2026
  • Accepted 18 August 2026
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI. Funded by SCOAP3.

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