T2K Experiment: Muon Neutrino Cross-Section Data with Covariance Matrices
by Andrew Cudd / University of Colorado Boulder
Available on 1 platform
Sign in to view source links and access this dataset
Description
Version 1.0, dated 2023/03/24, contains extracted cross-section data points and Monte Carlo predictions for muon neutrino charged-current interactions on hydrocarbon without pions. The data release includes covariance matrices, flux histograms, and analysis binning information from the T2K experiment, provided by Andrew Cudd of the University of Colorado Boulder. Supporting files include ROOT and CSV formats, along with example scripts for chi-square calculation.
Use Cases
Validate neutrino interaction Monte Carlo predictions based on the provided cross-section data and nominal MC values.
Perform statistical analysis of cross-section uncertainties using the included covariance and inverted covariance matrices.
Analyze neutrino flux distributions for the ND280 and INGRID detectors using the provided fine and coarse binned histograms.
Reproduce the chi-square calculation from the associated paper using the example ROOT and Python scripts.
Strengths
Includes both extracted cross-section data and nominal Monte Carlo predictions for direct comparison.
Provides covariance and inverted covariance matrices for uncertainty analysis.
Contains flux histograms in two different binnings: a fine 220-bin histogram and a coarse 20-bin histogram.
Offers data in both ROOT format for high-energy physics workflows and CSV format for broader accessibility.
Limitations
Column-level documentation is absent; field semantics must be inferred after download.
Row count is unknown, which may limit suitability assessment for some statistical methods.
Last update date is unknown; freshness unverified beyond the release version date.
Provenance
Source
T2K (Tokai to Kamioka) long-baseline neutrino oscillation experiment.
Collection Method
Experimental measurement from multiple detectors with correlated energy spectra.
Freshness
Data release version 1.0 is dated 2023/03/24.
Requires ROOT software or Python with NumPy to utilize the full data release and example scripts.