{
  "access": "open",
  "creators": [
    {
      "name": "薛景秦"
    }
  ],
  "curation": {
    "added": "2026-10-23",
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      "citations": 0,
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      "harvested": "2026-10-23",
      "query": "titles.title:neutrino AND publisher:Zenodo",
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    "source": "harvest:datacite (curated 2026-10-23)",
    "status": "curated"
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  "date_published": "2025-12-04",
  "description": "Reactor Neutrino Fitter\n\nA Python-based analysis framework for reactor neutrino experiments, specifically designed for Inverse Beta Decay (IBD) cross-section calculations and neutrino oscillation parameter fitting.\n\nProject Overview\n\nThis project provides tools for:\n\nComputing IBD differential cross-section matrices\n\nCalculating reactor neutrino expected spectra\n\nPerforming neutrino oscillation parameter fitting\n\nAnalyzing detector responses and energy reconstruction\n\nProject Structure\n\nreactor-neutrino-fitter/\n├── fitter/\n│   ├── src/fitter/\n│   │   ├── analysis/          # Analysis modules\n│   │   │   ├── fitter.py      # Main fitting class\n│   │   │   └── IBDXsec_Vissani.py  # IBD cross-section matrix generator\n│   │   ├── physics/            # Physics calculations\n│   │   │   ├── ibdxsec_Vissani.py      # IBD cross-section physics\n│   │   │   └── surprob_fit_sys.py      # Survival probability calculations\n│   │   ├── reactor/            # Reactor neutrino calculations\n│   │   │   └── reactor_expected.py     # Expected spectrum calculator\n│   │   ├── config/             # Configuration files\n│   │   │   ├── GlobalConfig.py         # Global configuration parameters\n│   │   │   └── ReactorConfig.py        # Reactor-specific settings\n│   │   └── common/             # Common utilities\n│   │       ├── cache_monitor.py        # Caching utilities\n│   │       ├── my_interpolator.py      # Interpolation functions\n│   │       └── nonlinearity_fit.py     # Nonlinearity corrections\n│   ├── data/                   # Data and output directory\n│   │   └── outputs/            # Generated cross-section matrices (large files, not in git)\n│   ├── lib/                    # Library files (e.g., matplotlib styles)\n│   └── paper_figure.ipynb      # Example analysis notebook\n└── README.md\n\nPrerequisites\n\nPython 3.x\n\nPyTorch\n\nNumPy\n\nSciPy\n\nMatplotlib\n\niminuit (for parameter fitting)\n\nuproot (for ROOT file handling)\n\nnumba (for performance optimization)\n\ntqdm (for progress bars)\n\nInstallation\n\nClone the repository:\n\ngit clone \ncd reactor-neutrino-fitter\n\nInstall required dependencies:\n\npip install torch numpy scipy matplotlib iminuit uproot numba tqdm\n\nUsage\n\nStep 1: Generate IBD Cross-Section Matrix\n\nImportant: Before running any analysis, you must first generate the IBD cross-section matrix. This file is too large (>200 MB) to be stored in GitHub, so it needs to be generated locally.\n\nGenerate the cross-section matrix by running:\n\npython3 fitter/src/fitter/analysis/IBDXsec_Vissani.py\n\nThis script will:\n\nCalculate the IBD differential cross-section matrix for neutrino energies from 1.8 to 13.0 MeV\n\nGenerate a matrix with configurable binning (default: 5600 bins, corresponding to ~0.002 MeV step size)\n\nSave the output to fitter/data/outputs/IBDXsec_matrix_enu5600_test.pt\n\nNote: This computation can be memory-intensive and time-consuming. The default configuration with 5600 bins produces a ~240 MB output file.\n\nYou can modify the binning configuration in the script by changing the bin_list parameter at the bottom of the file.\n\nStep 2: Configure Analysis Parameters\n\nEdit fitter/src/fitter/config/GlobalConfig.py to configure:\n\nData file paths\n\nEnergy binning parameters\n\nDetector parameters\n\nOscillation parameters\n\nSystematic uncertainties\n\nKey parameters:\n\ndata_path: Path to the generated IBD cross-section matrix\n\nfile_path: Path to input ROOT data files\n\nn_E_nu_bins: Number of neutrino energy bins (default: 5600)\n\nn_E_dep_bins: Number of deposited energy bins (default: 5600)\n\nStep 3: Run Analysis\n\nExample 1: Calculate Expected Spectrum\n\nfrom fitter.src.fitter.reactor.reactor_expected import ReactorExpected\nimport matplotlib.pyplot as plt\n\n# Initialize reactor expected spectrum calculator\nreactor = ReactorExpected(\n    n_E_nu_bins=5600,   # Neutrino energy bins (step ~0.002 MeV)\n    n_E_dep_bins=5600,  # Deposited energy bins (step ~0.002 MeV)\n    n_E_d_bins=560,     # Detector response bins\n    n_E_p_bins=560,     # Reconstructed energy bins\n)\n\n# Get reconstructed energy spectrum\nE_p_spectrum = reactor.get_E_p_spectrum()\n\n# Plot results\nimport matplotlib.pyplot as plt\nplt.plot(reactor.E_p, E_p_spectrum)\nplt.xlabel(\"Energy (MeV)\")\nplt.ylabel(\"Counts (/ 20keV / year)\")\nplt.title(\"Reconstructed Energy Spectrum\")\nplt.show()\n\nExample 2: Perform Parameter Fitting\n\nfrom iminuit import Minuit\nfrom fitter.src.fitter.analysis.fitter import Fitter\nfrom fitter.src.fitter.config import GlobalConfig as gcfg\n\n# Initialize fitter\nfitter = Fitter(year=1, MO=\"NO\")\n\n# Generate or load observed spectrum\nfitter.get_obs_spectrum(gseed=1)  # gseed=0 for Asimov, >0 for toy data\n\n# Set up Minuit for minimization\nm = Minuit(fitter.chi2, fitter.NO_params_NO, name=fitter.names)\nm.tol = 0.01\n\n# Perform fit\nresults = m.migrad()\nprint(results)\n\nKey Components\n\nIBDXsec_Vissani.py\n\nGenerates the IBD differential cross-section matrix. This is a computationally intensive step that must be run before any analysis. The output matrix maps neutrino energy to deposited energy probabilities.\n\nReactorExpected\n\nCalculates the expected reactor neutrino spectrum, including:\n\nNeutrino energy spectrum from reactors\n\nIBD interaction rates\n\nDetector response and energy reconstruction\n\nBackground contributions\n\nFitter\n\nMain analysis class for parameter fitting:\n\nChi-squared calculation with systematic uncertainties\n\nSupport for neutrino oscillation parameters (θ₁₂, θ₁₃, Δm²₂₁, Δm²₃₁)\n\nReactor and detector systematic uncertainties\n\nBackground rate and shape uncertainties\n\nConfiguration\n\nKey configuration files:\n\nGlobalConfig.py: Global analysis parameters, systematic uncertainties, test statistics\n\nReactorConfig.py: Reactor-specific parameters (power, distance, etc.)\n\nOutput Files\n\nGenerated files are stored in fitter/data/outputs/:\n\nIBD cross-section matrices (.pt format, PyTorch tensors)\n\nThese files are excluded from git due to their large size (>100 MB)\n\nNotes\n\nThe default energy binning (5600 bins) provides ~0.002 MeV resolution\n\nMemory requirements depend on the number of bins used\n\nThe framework supports both Normal Ordering (NO) and Inverted Ordering (IO)\n\nSystematic uncertainties include reactor correlations, detector effects, and background modeling",
  "distributions": [
    {
      "host": "zenodo",
      "role": "primary",
      "url": "https://doi.org/10.5281/zenodo.17810974"
    }
  ],
  "doi": "10.5281/zenodo.17810974",
  "id": "reactor-neutrino-fitter-xue",
  "license": "CC-BY-4.0",
  "license_url": "https://creativecommons.org/licenses/by/4.0/legalcode",
  "publisher": {
    "name": "Zenodo"
  },
  "short": "reactor-neutrino-fitter: Python IBD and oscillation fitting framework for reactor experiments",
  "software": {
    "development_status": "unknown",
    "interfaces": ["library"],
    "purposes": ["fitting"],
    "repository": "https://doi.org/10.5281/zenodo.17810974"
  },
  "sources": ["reactor"],
  "title": "xue-525/reactor-neutrino-fitter: First public release",
  "topics": ["oscillation"],
  "type": "software",
  "url_landing": "https://zenodo.org/doi/10.5281/zenodo.17810974",
  "version": "v1.0.0"
}