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

Karla Tame-Narvaez, Steven Gardiner, Aleksandra Ćiprijanović, Giuseppe Cerati

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.

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

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.
Paper Structure (10 sections, 7 equations, 10 figures, 1 table)

This paper contains 10 sections, 7 equations, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. Training Data
  3. Methods
  4. Neural Posterior Estimation
  5. Model Architecture and Training
  6. Model performance and calibration
  7. Method validation and parameter measurement in data
  8. Summary and Conclusion
  9. Acknowledgments
  10. Additional bins from the T2K data set

Figures (10)

  • Figure 1: Neural Posterior Estimate of the four parameters for a single test event. The gray dashed lines indicate the true values while the blue contours represent the estimates at $68\%$ and $95\%$ confidence intervals.
  • Figure 2: Comparison of true and predicted values for each parameter is shown for a subset of 200 test events, including $1\sigma$ error bars. Overall, the predictions closely follow the true values with narrow uncertainty bands, with only $\theta_3$ exhibiting some scatter and larger visible error bars.
  • Figure 3: Histograms of pulls for 1000 test events for each parameter. All four parameter distributions (red solid line) are centered close to zero and somewhat narrower than the standard normal distribution (black dashed line), suggesting that the uncertainties may be overestimated.
  • Figure 4: Residual distribution (in percentages) of four parameters for 1000 test events. The gray dashed lines indicate the zero value (perfect prediction). The distributions of residuals are very narrow and show no visible biases, i.e, they are all centered around zero. Additionally, all deviations are within a few percent.
  • Figure 5: Posterior coverage of the $\theta_i$ parameters for $1000$ test events (colored solid lines). $D(\theta_i)$ and TARP curves show combined model performance over all parameters, and are plotted with solid and dashed black lines, respectively. The diagonal black dotted line indicates perfect uncertainty calibration. The gray regions indicate thresholds of 10% (dark gray) and 20% (light gray) uncertainty miscalibration. All curves are within the 20% band and are showing slight underconfidence, which is preferred compared to an overconfident model with too narrow error bars.
  • ...and 5 more figures