bssunfold 0.13.0

Python package for neutron spectrum unfolding from measurements obtained with Bonner Sphere Spectrometer (BSS)

BSSunfold - Neutron Spectrum Unfolding Package for Bonner Sphere Spectrometers

🔍 Overview

BSSUnfold is a Python package for neutron spectrum unfolding from measurements obtained with Bonner Sphere Spectrometers (BSS). The package implements several mathematical algorithms for solving the inverse problem of unfolding neutron energy spectra from detector readings, with applications in radiation protection, nuclear physics research, and accelerator facilities. Iterative solvers are accelerated with Numba JIT compilation for 3–50x speedups.

Contents

• Features
• Installation
• Quick start
• Available Unfolding Methods
• Spectrum Comparison
• Project structure
• Technical requirements
• Authors
• Citing
• Documentation
• Publications

📦 Features

• Multiple Unfolding Algorithms (26 methods):

  • Tikhonov-type: CVXPY, qpsolvers, Legendre basis, TSVD (truncated SVD)
  • Iterative: Landweber, MLEM (pure NumPy + ODL), GRAVEL, Doroshenko, Kaczmarz
  • Bayesian: D'Agostini iterative (Bayes), Bayes with spline regularization
  • Maximum Entropy: MAXED (primal log-space dual minimisation)
  • Statistical Regularization: Turchin's method (StatReg, Reconst — Fortran STREG1 port)
  • Optimization-based: lmfit (L1/L2/Elastic Net), Scipy direct solvers (CG, GMRES, LSQR)
  • Pipeline: Combined approach for chaining multiple methods
  • Parametric: FRUIT-style thermal/epithermal/fast model (lmfit, cvxpy SQP, qpsolvers SQP, combined); BON95 4-component model with directed-divergence iterations

• Numba JIT-Accelerated Iterative Solvers:

  • @njit(cache=True) compiled inner loops for Doroshenko, Kaczmarz, MLEM, GRAVEL
  • 3–50x speedup on iterative solvers (see Performance)
  • Automatic disk caching of compiled code; graceful fallback when numba is not installed

• Radiation Dose Calculations:

  • Effective dose calculations for different irradiation types based on conversion coefficients from 116 publication of International commission on radiological protection (ICRP)

• Comprehensive Data Management:

  • Automatic response function processing
  • Uncertainty quantification via Monte Carlo methods

• Advanced Visualization:

  • Spectrum plotting with uncertainty bands
  • Detector reading comparison

📥 Installation

Using uv (recommended)

uv add bssunfold

Using pip

pip install bssunfold

Using conda

conda install conda-forge::bssunfold

From Source

git clone https://github.com/radiationsafety/bssunfold.git
cd bssunfold
pip install -e .

Optional dependencies

# Basic installation (without additional solvers)
pip install bssunfold

# With numba JIT acceleration (recommended for iterative solvers)
pip install "bssunfold[numba]"

# With additional cross-platform solvers (recommended)
pip install "bssunfold[solvers-core]"

# All solvers (Unix/Linux/macOS)
pip install "bssunfold[all-solvers]"

# Windows (all except proxsuite)
pip install "bssunfold[windows]"

Install with all solvers (Unix/Linux/Mac):

pip install bssunfold[all-solvers]

For Windows is recommended to use the following command because of the problem with proxsuite:

pip install bssunfold[windows]

🎯 Quick Start

Open in interactive notebooks:

import pandas as pd
from bssunfold import Detector

# Load response functions
rf_df = pd.read_csv("../data/response_functions/rf_GSF.csv")

# Initialize detector
detector = Detector(rf_df)

# Provide detector readings [reading per second]
readings = {
    "0in": 0.0003,
    "2in": 0.0099,
    "3in": 0.0536,
    "5in": 0.1841,
    "6in": 0.2196,
    "8in": 0.2200,
    "10in": 0.172,
    "12in": 0.120,
    "15in": 0.066,
    "18in": 0.034,
}

# Unfold spectrum using convex optimization
result = detector.unfold_cvxpy(
    readings,
    regularization=1e-4,
    calculate_errors=True
)

# Visualize results
detector.plot_with_uncertainty(result, plot_style == 'errorbar')

# Calculate and display dose rates
print("Dose rates [pcSv/s]:", result['doserates'])

📊 Input Data Structure

Response Functions

Response functions must be provided as a CSV file with the following format:

E_MeV,0in,2in,3in,5in,8in,10in,12in
1.00E-09,0.001,0.005,0.01,0.02,0.03,0.04,0.05
1.00E-08,0.002,0.006,0.012,0.022,0.032,0.042,0.052
...

Detector Readings

Readings should be provided as a dictionary mapping sphere names to measured values:

readings = {
    'sphere_0in': 150.2,   # Bare detector
    'sphere_2in': 120.5,   # 2-inch polyethylene sphere
    'sphere_3in': 95.7,    # 3-inch polyethylene sphere
    # ... additional spheres
}

📦 Built-in Response Functions

The package includes 7 built-in response function datasets for immediate use:

Dataset	Source	Detectors	Energy Range
RF_GSF	GSF (Germany)	10 (0in–18in)	1e-9 – 631 MeV
RF_PTB	PTB (Germany)	15 (0in–18in)	1e-9 – 631 MeV
RF_LANL	LANL (USA)	11 (3in–18in, + Pb-shielded)	1e-9 – 631 MeV
RF_JINR	JINR (Dubna, Russia)	9 (0in–12in, Cd0in, 10inPb)	1e-9 – 631 MeV
RF_FERMILAB	Fermilab (USA)	8 (0in–18in)	1e-9 – 631 MeV
RF_EURADOS	EURADOS round-robin	13 (0in–12in, Cd2in, 3.5in, 4.5in)	1e-9 – 20 MeV ⚠️
RF_IHEP	IHEP (Protvino, Russia)	12 (0in–18in, 15in)	1e-9 – 2000 MeV ⚠️

⚠️ Note: RF_EURADOS has a narrower energy range (max 20 MeV) and RF_IHEP has a wider range (max 2000 MeV) compared to the standard 631 MeV used by GSF/PTB/LANL/JINR/Fermilab. Use caution when comparing results across datasets.

from bssunfold import Detector, RF_JINR

# Use built-in response functions directly
detector = Detector(RF_JINR)
result = detector.unfold_cvxpy(readings, regularization=1e-4)

🔢 Dose Conversion Coefficients

The package includes 4 dose conversion coefficient datasets for flexible dose rate calculations:

Dataset	Standard	Quantities	Energy Range
ICRP116 (default)	ICRP-116	AP, PA, LLAT, RLAT, ISO, ROT	1e-9 – 631 MeV
ICRP74_effective	ICRP-74	AP, PA, RLAT, ROT, ISO	1e-9 – 398 MeV
NRB99_2009_effective	NRB99-2009	AP, ISO	25 eV – 20 MeV ⚠️
ICRP74_operational	ICRP-74	ADE, PDE0, PDE45, PDE60, PDE75	1e-9 – 398 MeV

⚠️ Note: NRB99_2009_effective covers a limited energy range (25 eV – 20 MeV). Values outside this range are set to zero.

from bssunfold import Detector, get_coefficients

# Method 1: Set on Detector (affects all subsequent unfolds)
detector = Detector(cc_type="ICRP74_effective")
result = detector.unfold_cvxpy(readings)

# Method 2: Change after creation
detector.set_dose_coefficients("ICRP74_operational")

# Method 3: Get coefficients directly for custom use
cc = get_coefficients("NRB99_2009_effective")
from bssunfold import interpolate_coefficients
cc_interp = interpolate_coefficients(cc, detector.E_MeV)

⚙️ Available Unfolding Methods

graph TD
    A[Unfolding Methods] --> B[Tikhonov-type]
    A --> C[Iterative]
    A --> D[Bayesian]
    A --> E[Maximum Entropy]
    A --> F[Statistical Regularization]
    A --> G[Optimization-based]
    A --> H[Pipeline]
    A --> I[Parametric]

    B --> B1[unfold_cvxpy]
    B --> B2[unfold_qpsolvers]
    B --> B3[unfold_tsvd]
    B --> B4[unfold_tikhonov_legendre]

    C --> C1[unfold_landweber]
    C --> C2[unfold_mlem]
    C --> C3[unfold_mlem_stop]
    C --> C4[unfold_mlem_odl]
    C --> C5[unfold_gravel]
    C --> C6[unfold_doroshenko]
    C --> C7[unfold_kaczmarz]

    D --> D1[unfold_bayes]
    D --> D2[unfold_bayes_spline_regularization]

    E --> E1[unfold_maxed]
    F --> F1[unfold_statreg]
    F --> F2[unfold_reconst]

    G --> G1[unfold_lmfit]
    G --> G2[unfold_scipy_direct_method]

    H --> H1[unfold_combined]

    I --> I1[unfold_parametric]
    I --> I2[unfold_parametric_cvxpy]
    I --> I3[unfold_parametric_qpsolvers]
    I --> I4[unfold_parametric_combined]
    I --> I5[unfold_parametric2]
    I --> I6[unfold_fruit_like]
    I --> I7[unfold_hybrid_parametric]
    I --> I8[unfold_bayesian_parametric]

    style A fill:#4a90d9,color:#fff
    style B fill:#e8f0fe
    style C fill:#e8f0fe
    style D fill:#e8f0fe
    style E fill:#e8f0fe
    style F fill:#e8f0fe
    style G fill:#e8f0fe
    style H fill:#e8f0fe
    style I fill:#e8f0fe

Method Reference Table

#	Method	Category	Unique Parameters	Dependencies	Description
1	unfold_cvxpy	Tikhonov	regularization, norm (1/2), solver, regularization_method	cvxpy	Convex optimization with Tikhonov regularization
2	unfold_qpsolvers	Tikhonov	regularization, norm (1/2), solver, smoothness_order, smoothness_weight, regularization_method	qpsolvers	QP-based unfolding with L1/L2/smoothness norms
3	unfold_tsvd	Tikhonov	method (l_curve/gcv/discrepancy/energy/median/donoho), k, threshold, noise_level	—	Truncated SVD with automatic k-selection
4	unfold_tikhonov_legendre	Tikhonov	delta, n_polynomials	—	Tikhonov regularization in Legendre polynomial basis
5	unfold_landweber	Iterative	max_iterations, tolerance	—	Landweber fixed-point iteration
6	unfold_mlem	Iterative	max_iterations, tolerance	—	Pure-NumPy MLEM (expectation maximization)
7	unfold_mlem_stop	Iterative	max_iterations, cps_crossover, j_threshold	—	MLEM with J-factor early stopping criterion (Montgomery et al. 2020)
8	unfold_mlem_odl	Iterative	max_iterations, tolerance	odl	MLEM via ODL operator framework
9	unfold_gravel	Iterative	max_iterations, tolerance, regularization	—	GRAVEL algorithm with relative entropy weighting
10	unfold_doroshenko	Iterative	max_iterations, tolerance, regularization	—	Coordinate-update iterative method
11	unfold_kaczmarz	Iterative	max_iterations, omega, tolerance	—	ART (Algebraic Reconstruction Technique)
12	unfold_bayes	Bayesian	max_iterations, tolerance	—	D'Agostini Bayesian iterative unfolding
13	unfold_bayes_spline_regularization	Bayesian	max_iterations, tolerance, spline_degree, spline_smooth	—	Bayes iteration with spline smoothing
14	unfold_maxed	MaxEnt	sigma_factor, max_iterations, tolerance	—	Maximum entropy deconvolution (Reginatto & Goldhagen)
15	unfold_statreg	Statistical Reg.	unfoldermethod (EmpiricalBayes/...), regularization, basis_name, boundary, derivative_degree	—	Turchin's statistical regularization
16	unfold_reconst	Statistical Reg.	alpha, beta, max_iter_alpha, max_iter_beta, tol_alpha, tol_beta	—	Fortran STREG1 port: auto α/β with discrepancy principle & ω-criterion
17	unfold_lmfit	Optimization	method (lbfgsb/leastsq/...), model_name (elastic/lasso/ridge), regularization, regularization2, l1_weight	lmfit	L1/L2/Elastic Net via lmfit
18	unfold_scipy_direct_method	Optimization	method (cg/gmres/lsqr/lsmr/minres), tolerance, max_iterations	—	Direct SciPy linear solvers
19	unfold_combined	Pipeline	pipeline (list of {method, params} dicts)	—	Sequential multi-method pipeline
20	unfold_parametric	Parametric	parametric_method, optimizer, solver_backend, initial_params	lmfit, cvxpy, qpsolvers	FRUIT-style thermal/epithermal/fast model
21	unfold_parametric_cvxpy	Parametric	parametric_method, initial_params, solver_backend	cvxpy	SQP solver using cvxpy for parametric fitting
22	unfold_parametric_qpsolvers	Parametric	parametric_method, initial_params, solver_backend	qpsolvers	SQP solver using qpsolvers backends
23	unfold_parametric_combined	Parametric	parametric_method, initial_params, solver_backend	lmfit, cvxpy, qpsolvers	lmfit first-pass + QP refinement
24	unfold_parametric2	Parametric	b_range, Tf_range, c_range, noise_level, max_iter, tol_chi2, optimizer, solver_backend	grid, cvxpy, qpsolvers, combined	BON95 4-component model + directed-divergence iterations
25	unfold_fruit_like	Parametric	initial_params, max_iterations, tolerance	—	FRUIT-like model: Maxwellian thermal + 1/E epithermal + evaporation fast
26	unfold_hybrid_parametric	Parametric	refinement_method (landweber/mlem), max_iterations, tolerance	—	Parametric initial guess refined by Landweber or MLEM
27	unfold_bayesian_parametric	Parametric	n_samples, burn_in, proposal_scale, prior_mean, prior_std	—	Metropolis-Hastings MCMC for spectral parameter estimation

Common parameters (shared by most methods): readings, initial_spectrum, calculate_errors, noise_level, n_montecarlo, save_result, random_state.

Basic Example

import pandas as pd
from bssunfold import Detector

detector = Detector(pd.read_csv("response_functions.csv"))
readings = {"0in": 0.0003, "2in": 0.0099, "3in": 0.0536, "5in": 0.1841}

# Convex optimization
result = detector.unfold_cvxpy(readings, regularization=1e-4, calculate_errors=True)

# Dose rates
print(result["doserates"])

# Plot with uncertainty
detector.plot_with_uncertainty(result)

Pipeline Example

result = detector.unfold_combined(
    readings=readings,
    pipeline=[
        {"method": "cvxpy", "params": {"regularization": 1e-4}},
        {"method": "landweber", "params": {"max_iterations": 2000}},
    ],
    calculate_errors=True,
)

Parametric Example

# FRUIT-style parametric model (thermal + epithermal + fast)
result = detector.unfold_parametric(
    readings=readings,
    parametric_method='thermal+epithermal+fast',
    optimizer='cvxpy',           # or 'lmfit', 'qpsolvers', 'combined'
    solver_backend='cvxpy:ECOS', # or 'qpsolvers:osqp'
    calculate_errors=True,
)

# The parametric model fit yields spectrum components
print(result['doserates'])

📊 5-Detector Comparison

The dose rate evaluation scripts compare results across 5 detector configurations:

Detector	Origin	Detectors	Energy Range
GSF	Germany	10 (0in–18in)	1e-9 – 631 MeV
PTB	Germany	15 (0in–18in)	1e-9 – 631 MeV
LANL	USA	11 (3in–18in, + Pb-shielded)	1e-9 – 631 MeV
JINR	Dubna, Russia	9 (0in–12in, Cd0in, 10inPb)	1e-9 – 631 MeV
FERMILAB	Fermilab, USA	8 (0in–18in)	1e-9 – 631 MeV

ISO scatter plots with per-detector color coding are generated in tests/iso_plots/ and tests/iaea_compendium_iso_plots/.

📊 Spectrum Comparison

Compare two or more unfolded spectra using a comprehensive set of 25 metrics.

import numpy as np
from bssunfold import Detector

detector = Detector()

r1 = detector.unfold_qpsolvers(readings, save_result=False)
r2 = detector.unfold_cvxpy(readings, save_result=False)

# Compare two results (all 25 metrics)
result = detector.compare(r1, r2)
print(result['cosine_similarity'], result['mean_squared_error'])

# Compare with specific metrics
detector.compare(r1, r2, metrics=['cosine_similarity', 'kl_divergence'])

# Compare raw spectra
df = detector.compare(
    np.ones(detector.n_energy_bins),
    np.ones(detector.n_energy_bins) * 2,
    np.ones(detector.n_energy_bins) * 3,
    labels=['Ref', 'A', 'B'],
)
print(df)

# Visual comparison
detector.compare(r1, r2, plot=True, save_to='comparison.png')

# Independent usage
from bssunfold.utils.comparison import compare_spectra, kl_divergence
all_metrics = compare_spectra(s1, s2)
print(kl_divergence(s1, s2))

graph TD
    A[Comparison Metrics<br/>25 total] --> B[Entropy]
    A --> C[Distribution]
    A --> D[Correlation]
    A --> E[Error]
    A --> F[Similarity]
    A --> G[Chi-squared]
    A --> H[Statistical]

    B --> B1[kl_divergence]
    B --> B2[cross_entropy]
    B --> B3[entropy_difference_percent]

    C --> C1[wasserstein_dist]
    C --> C2[energy_dist]
    C --> C3[kolmogorov_smirnov_stat]

    D --> D1[pearson_r]
    D --> D2[spearman_r]

    E --> E1[mean_squared_error]
    E --> E2[root_mean_squared_error]
    E --> E3[mean_absolute_error]
    E --> E4[mape]
    E --> E5[r2_score]
    E --> E6[max_error]
    E --> E7[median_absolute_error]

    F --> F1[cosine_similarity]
    F --> F2[mmd_rbf]

    G --> G1[chi_squared]
    G --> G2[g_test]
    G --> G3[freeman_tukey]
    G --> G4[cressie_read]

    H --> H1[anderson_darling]
    H --> H2[wilcoxon_test]
    H --> H3[mannwhitneyu_test]
    H --> H4[standardized_mean_difference]

    style A fill:#4a90d9,color:#fff

All 25 Metrics

Category	Metric Key	Description	Range
Entropy	kl_divergence	Kullback-Leibler divergence D_KL(p‖q)	[0, ∞)
	cross_entropy	Cross-entropy H(p,q) = -∑p·log(q)	[0, ∞)
	entropy_difference_percent	Relative cross-entropy excess (%)	[0, ∞)
Distribution	wasserstein_dist	Earth mover's / Wasserstein distance	[0, ∞)
	energy_dist	Energy distance between distributions	[0, ∞)
	kolmogorov_smirnov_stat	Kolmogorov-Smirnov D-statistic	[0, 1]
Correlation	pearson_r	Pearson correlation coefficient	[-1, 1]
	spearman_r	Spearman rank correlation	[-1, 1]
Error	mean_squared_error	Mean squared error	[0, ∞)
	root_mean_squared_error	Root mean squared error	[0, ∞)
	mean_absolute_error	Mean absolute error	[0, ∞)
	mape	Mean absolute percentage error (%)	[0, 100]
	r2_score	R² (coefficient of determination)	(-∞, 1]
	max_error	Maximum residual error	[0, ∞)
	median_absolute_error	Median absolute error	[0, ∞)
Similarity	cosine_similarity	Cosine similarity cos(θ) = (p·q)/(‖p‖‖q‖)	[0, 1]
	mmd_rbf	Maximum Mean Discrepancy (RBF kernel)	[0, ∞)
Chi-squared	chi_squared	Pearson's chi-squared statistic	[0, ∞)
	g_test	G-test (log-likelihood ratio)	[0, ∞)
	freeman_tukey	Freeman-Tukey statistic	[0, ∞)
	cressie_read	Cressie-Read power divergence	[0, ∞)
Statistical	anderson_darling	Anderson-Darling k-sample statistic	[0, ∞)
	wilcoxon_test	Wilcoxon signed-rank test statistic	[0, ∞)
	mannwhitneyu_test	Mann-Whitney U test statistic	[0, ∞)
	standardized_mean_difference	Cohen's d (SMD)	(-∞, ∞)

All metrics are implemented with pure NumPy/SciPy — no extra dependencies required.

📈 Output Data

The package provides comprehensive output in standardized formats:

Spectrum Results

• Energy grid in MeV
• Unfolded neutron spectrum for the grid of energy bins
• Uncertainty estimates (if calculated)

Dose Calculations

• Effective dose rates for different geometries:
  • AP (Anterior-Posterior)
  • PA (Posterior-Anterior)
  • LLAT (Left Lateral)
  • RLAT (Right Lateral)
  • ROT (Rotational)
  • ISO (Isotropic)

Quality Metrics

• Residual norm
• Iteration counts

📝 Application Areas

Nuclear Research Facilities

• Neutron spectroscopy at particle accelerators
• Reactor neutron field characterization
• Fusion device diagnostics

Radiation Protection

• Workplace monitoring at nuclear power plants
• Medical accelerator facilities
• Industrial radiography installations

Scientific Research

• Space radiation studies
• Cosmic ray neutron measurements
• Nuclear physics experiments

🔬 Advanced Features

Result Management

# List all saved results
results = detector.list_results()
print(f"Available results: {results}")

# Retrieve specific result
result = detector.get_result('20240115_143022_cvxpy')

# Create comprehensive report
report = detector.create_summary_report(
    save_path='unfolding_report.json'
)

# Clear results history
detector.clear_results()

Custom Uncertainty Analysis

# Custom Monte Carlo parameters
result = detector.unfold_cvxpy(
    readings,
    calculate_errors=True,
    n_montecarlo=500,      # Number of samples
    noise_level=0.02       # 2% measurement noise
)

# Access uncertainty data
uncert_mean = result['spectrum_uncert_mean']

📂 Project Structure

bssunfold/
├── CHANGELOG.md
├── CITATION.cff
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── LICENSE
├── README.md
├── SECURITY.md
├── TESTS_AND_DOCS.md
├── pyproject.toml
├── uv.lock
├── environment.yml
├── assets/                      # Logos, images
├── conda.recipe/                # Conda build recipe
├── docs/                        # Sphinx documentation
│   ├── index.rst
│   ├── overview.rst             # Methods & metrics overview
│   ├── detector.rst             # Full API reference
│   ├── examples.rst
│   ├── conf.py
│   └── requirements.txt
├── examples/                    # Jupyter notebooks
├── tests/                       # 910 tests across 16 files
│   ├── test_all.py
│   ├── test_comparison.py
│   ├── test_coverage.py         # ~205 edge-case & fallback tests
│   ├── test_detector.py
│   ├── test_dose_coefficients.py
│   ├── test_iaea_validation.py
│   ├── test_improvements.py     # 110 new tests (validators, metrics, MC)
│   ├── test_methods2.py
│   ├── test_mlem.py
│   ├── test_new_methods.py
│   ├── test_new_methods_fixed.py
│   ├── test_new_metrics.py
│   ├── test_readings.py
│   ├── test_refactored_fixed.py
│   ├── test_response_functions.py
│   ├── test_unfold_parametric.py
│   └── test_unfold_parametric2.py
└── src/
    └── bssunfold/
        ├── __init__.py          # Public API: Detector
        ├── constants.py         # ICRP-116 dose coefficients
        ├── logging_config.py
        ├── platform_check.py    # Solver availability checks
        ├── core/
        │   ├── __init__.py
        │   ├── _base_unfolder.py
        │   ├── _matrix_utils.py # SVD, derivative matrix
        │   ├── _montecarlo.py   # MC uncertainty (optimized)
        │   ├── _numba_jit.py    # Numba JIT inner loops ⚡
        │   ├── detector.py      # Main Detector class
        │   ├── dose_calculation.py
        │   ├── regularization.py   # L-curve, GCV, DP
        │   ├── unfold_cvxpy.py
        │   ├── unfold_qpsolvers.py
        │   ├── unfold_tsvd.py
        │   ├── unfold_tikhonov_legendre.py
        │   ├── unfold_landweber.py
        │   ├── unfold_mlem.py
        │   ├── unfold_mlem_odl.py
        │   ├── unfold_gravel.py
        │   ├── unfold_doroshenko.py
        │   ├── unfold_kaczmarz.py
        │   ├── unfold_bayes.py
        │   ├── unfold_bayes_spline_regularization.py
        │   ├── unfold_maxed.py
        │   ├── unfold_statreg.py
        │   ├── unfold_reconst.py
        │   ├── unfold_lmfit.py
        │   ├── unfold_scipy_direct_method.py
        │   ├── unfold_combined.py
        │   ├── unfold_parametric.py
        │   ├── unfold_parametric2.py
        │   ├── unfold_fruit_like.py
        │   ├── unfold_hybrid_parametric.py
        │   └── unfold_bayesian_parametric.py
        └── utils/
            ├── __init__.py
            ├── comparison.py    # 25 spectrum metrics
            ├── converters.py
            ├── interpolation.py
            ├── plotting.py
            └── validators.py

🔧 Technical Requirements

Core Requirements

• Python 3.11+
• NumPy, SciPy, Pandas, Matplotlib
• cvxpy[ecos] — convex optimisation framework (CVXPY-based methods)

Optional Backends

• numba — JIT compilation for iterative solvers (3–50x speedup)
• pytikhonov — L-curve / GCV / DP regularisation (Tikhonov-type methods)
• qpsolvers[solvers-core] — QP solvers (unfold_qpsolvers)
• lmfit — L1/L2/Elastic Net regularisation (unfold_lmfit)
• odl — Operator Discretization Library (unfold_mlem_odl)

All other methods (GRAVEL, MAXED, Bayes, StatReg, Reconst, TSVD, ScipyDirect, Landweber, Kaczmarz, Doroshenko, MLEM, TikhonovLegendre) have no extra dependencies beyond NumPy/SciPy.

See pyproject.toml for version constraints.

Performance

All iterative solvers use Numba JIT-compiled inner loops when numba is installed, with automatic fallback to pure Python.

Solver	Before	After	Speedup
Doroshenko	40.6 ms	0.8 ms	50x
Kaczmarz	1.4 ms	0.1 ms	14x
MLEM	2.7 ms	0.4 ms	7x
GRAVEL	~2 ms	0.6 ms	3x
cvxpy	84 ms	78 ms	~1x (external solver)
qpsolvers	1.7 ms	1.6 ms	~1x (external solver)

Benchmarks on 60-bin energy grid, 500 iterations, macOS arm64.

Install numba for the best performance:

pip install bssunfold[numba]

📖 Citation

If you use BSSUnfold in your research, please cite paper:

@article{chizhov2024neutron,
  title={Neutron spectra unfolding from Bonner spectrometer readings by the regularization method using the Legendre polynomials},
  author={Chizhov, K and Beskrovnaya, L and Chizhov, A},
  journal={Physics of Particles and Nuclei},
  volume={55},
  number={3},
  pages={532--534},
  year={2024},
  publisher={Springer}
}

or software:

@misc{konstantin_radiationsafetybssunfold_2025,
	title = {Radiationsafety/bssunfold},
	copyright = {GNU General Public License v3.0 only},
	shorttitle = {Radiationsafety/bssunfold},
	url = {https://zenodo.org/doi/10.5281/zenodo.18056376},
	abstract = {first published version of package},
	urldate = {2026-01-12},
	publisher = {Zenodo},
	author = {Chizhov, Konstantin},
	month = dec,
	year = {2025},
	doi = {10.5281/ZENODO.18056376},
}

💬 Contributing

Contributions are welcome! Please see CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Ensure all tests pass
5. Submit a pull request

📘 Documentation

Documentation and API reference is available in /docs folder. Theory and methodology in the research paper, examples of usage in /examples folder. Check the https://bssunfold.readthedocs.io/en/latest/

📄 License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

💬 Support

For questions, bug reports, or feature requests:

• Open an issue on GitHub
• Contact: [e-mail]

💻 Authors

• Konstantin Chizhov
• Alexei Chizhov
• Dmitry Borschev
• Maria Akimochkina

🌐 Acknowledgments

• ICRP and IAEA for data
• Contributors and testers
• Joint Institure for Nuclear Research (JINR)
• University "Dubna", School of Big Data Analytics

🎓 Publications

1. Чижов К.А., Чижов А.В., Борщев Д.С., Акимочкина М.А. Методы решения обратных задач для обработки результатов измерений на примере восстановления спектра нейтронов, Тридцать третья международная конференция "Математика. Компьютер. Образование, г. Дубна, 26 – 31 января 2026 г., https://mce.su
2. Chizhov, K., Chizhov, A. Optimization of the Neutron Spectrum Unfolding Algorithm Using Shifted Legendre Polynomials Based on Weighted Tikhonov Regularization. Phys. Part. Nuclei 56, 1395–1399 (2025). https://doi.org/10.1134/S106377962570056X
3. Chizhov K., Beskrovnaya L., Chizhov A. Neutron spectrum unfolding method based on shifted Legendre polynomials, its application to the IREN facility // Phys. Part. Nucl. Lett. — 2025. — V. 22, no. 2. — P. 337–340. — DOI: https://doi.org/10.1134/S154747712470239X
4. Chizhov K., Beskrovnaya L., Chizhov A. Neutron spectra unfolding from Bonner spectrometer readings by the regularization method using the Legendre polynomials // Phys. Part. Nucl. — 2024. — V. 55. — P. 532–534. — DOI: https://doi.org/10.1134/S1063779624030298
5. Chizhov K., Chizhov A. Optimization approach to neutron spectra unfolding with Bonner multi-sphere spectrometer // Math. Model. — 2024. — V. 7. — P. 89–90.
6. Чижов А. В., Чижов К. А. Восстановление спектров опорных нейтронных полей на Фазотроне (ОИЯИ) на основе показаний многошарового спектрометра Боннера методом усеченного сингулярного разложения Тезисы Трудов LXI Всероссийской конференции по физике РУДН 19 - 23 мая 2025.
7. Chizhov, K., Chizhov, A., TSVD-based neutron spectra unfolding by Bonner multi-sphere spectrometer readings with iteration procedure, proceedings of the International Conference "Distributed Computing and Grid-technologies in Science and Education".
8. Белый А.А., Стариковская М.Д., Чижов К.А. Разработка веб-приложения для эксперимента по восстановлению спектра нейтронов с применением алгоритмов нейронный сетей. Системный анализ в науке и образовании. 2025;(2):49–57.
9. Starikovskaya MD, Chizhov KA. Neutron spectrum unfolding based on random forest algorithm and generated training sample. In Российский университет дружбы народов им. П. Лумумбы; 2025 [cited 2025 Dec 25]. p. 389–94. Available from: https://www.elibrary.ru/item.asp?id=83014906
10. Chizhov KA, Bely AA, Starikovskaia MD, Volkov EN. Восстановление энергетического спектра потока нейтронного излучения с помощью алгоритма машинного обучения «случайный лес». Современные информационные технологии и ИТ-образование. 2024 Dec 15 [cited 2025 Apr 9]; 20(4). Available from: http://sitito.cs.msu.ru/index.php/SITITO/article/view/1167

📘 References

1. Compendium of neutron spectra and detector responses for radiation protection purposes: supplement to technical reports series no. 318. — Vienna: International Atomic Energy Agency, 2001. — Technical reports series no. 403. — STI/DOC/010/403. — ISBN 92-0-102201-8.
2. Diamond, S. and Boyd, S., 2016. CVXPY: A Python-embedded modeling language for convex optimization. Journal of Machine Learning Research, 17(83), pp.1-5.

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BSSUnfold - Professional neutron spectrum unfolding for radiation science and nuclear applications.