Simulation-Based Inference for Neutrino Interaction Model Tuning
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
This project demonstrates, for the first time, the application of simulation-based inference (SBI) techniques to tune neutrino–nucleus interaction models. Using a mock dataset based on the MicroBooNE tuning of the GENIE event generator, our approach employs a Neural Posterior Estimator (NPE) with Masked Autoregressive Flows (MAF) to infer the posterior distributions of key GENIE parameters directly from simulated histograms. The workflow provides a scalable and amortized framework for performing likelihood-free inference in high-dimensional parameter spaces, offering a pathway to more efficient and uncertainty-aware model tuning for next-generation neutrino experiments such as DUNE and SBND.
- Short Name / Acronym:
- Neutrino_SBI
- Site Accession Number:
- FERMILAB-CODE-2025-17-CSA
- Software Type:
- Scientific
- License(s):
- Apache License 2.0
- Programming Language(s):
- Python
- Research Organization:
- Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP)Primary Award/Contract Number:89243024CSC000002
- DOE Contract Number:
- 89243024CSC000002
- Code ID:
- 167867
- OSTI ID:
- code-167867
- Country of Origin:
- United States
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Simulation-based inference for neutrino interaction model parameter tuning