First Estimation of Model Parameters for Neutrino-Induced Nucleon Knockout Using Simulation-Based Inference

TL;DR

This paper demonstrates the application of simulation-based inference to estimate parameters in neutrino interaction models, improving fit quality and offering a new approach to complex simulation tuning in neutrino physics.

Contribution

It introduces the use of simulation-based inference for neutrino model parameter estimation, showing its effectiveness on GENIE and NuWro simulations compared to traditional tuning methods.

Findings

SBI can recover and slightly refine known physics parameters.

SBI achieves better fit to experimental data than previous tuned models.

The method can approximate alternative simulation models with limited shared parameters.

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 event 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.