Event Generator Tuning as a Robustness Test

Abstract: Neutrino oscillation experiments use Monte Carlo event generators to predict neutrino-nucleus interactions. Cross section uncertainties are typically implemented by varying the parameters of the model(s) used in the generator. We study the performance of two commonly-used model configurations of the GENIE generator (G18_10a_02_11a and AR23_0i_00_000) and their uncertainties by tuning parameters to cross section data, and then comparing the resulting tuned prediction to a suite of other measurements from T2K, MicroBooNE, and MINERvA. This reveals whether the model can simultaneously describe several datasets, as well as whether the uncertainties are adequately robust. We find that G18 and especially AR23 are reasonable in predicting lower-energy measurements from T2K and MicroBooNE, but unable to describe MINERvA data, and discuss the implications for short-baseline oscillation searches. We attempt to replicate a tuning procedure developed by MicroBooNE using several different measurements, and find substantially different results depending on which measurement is used, and that the MicroBooNE tune does not agree with other measurements. We conclude that the SBN experiment should not tune its generator to external data.