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Simulation-Based Inference for Neutrino Interaction Model Tuning

RESOURCE

Publicly Accessible Repository
https://github.com/karlaTame/Neutrino_SBI
https://doi.org/10.11578/dc.20251021.1

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Abstract

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.
Developers:
ORCID [1]
  1. Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Release Date:
2025-10-21
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Programming Languages:
Python
Licenses:
Apache License 2.0
Sponsoring Org.:
Code ID:
167867
Site Accession Number:
FERMILAB-CODE-2025-17-CSA
Research Org.:
Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/karlaTame/Neutrino_SBI
https://doi.org/10.11578/dc.20251021.1

SAVE / SHARE



FNAL Badge

Citation Formats

Tame-Narvaez, Karla M. Simulation-Based Inference for Neutrino Interaction Model Tuning. Computer Software. https://github.com/karlaTame/Neutrino_SBI. USDOE Office of Science (SC), High Energy Physics (HEP). 21 Oct. 2025. Web. doi:10.11578/dc.20251021.1.
Tame-Narvaez, Karla M. (2025, October 21). Simulation-Based Inference for Neutrino Interaction Model Tuning. [Computer software]. https://github.com/karlaTame/Neutrino_SBI. https://doi.org/10.11578/dc.20251021.1.
Tame-Narvaez, Karla M. "Simulation-Based Inference for Neutrino Interaction Model Tuning." Computer software. October 21, 2025. https://github.com/karlaTame/Neutrino_SBI. https://doi.org/10.11578/dc.20251021.1.
@misc{ doecode_167867,
title = {Simulation-Based Inference for Neutrino Interaction Model Tuning},
author = {Tame-Narvaez, Karla M.},
abstractNote = {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.},
doi = {10.11578/dc.20251021.1},
url = {https://doi.org/10.11578/dc.20251021.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20251021.1}},
year = {2025},
month = {oct}
}