NEREIDS
Neutron rEsonance REsolved Imaging Data Analysis System
NEREIDS is a Rust-based library for neutron resonance imaging at the VENUS beamline, Spallation Neutron Source (SNS), Oak Ridge National Laboratory. It provides end-to-end analysis for time-of-flight neutron transmission imaging: input hyperspectral TOF data, output spatially resolved isotopic composition maps.
Features
- R-matrix cross-sections -- Reich-Moore, Breit-Wigner (single- and multi-level), R-Matrix Limited (LRF=7), Coulomb channels
- Unresolved Resonance Region -- energy-averaged Hauser-Feshbach cross-sections (width-fluctuation correction planned, not yet implemented)
- Doppler broadening -- Free Gas Model (crystal-lattice model planned, not yet implemented)
- Resolution broadening -- Gaussian (channel width + flight path) and tabulated instrument functions
- ENDF nuclear data -- automatic retrieval and caching (ENDF/B-VIII.0 and VIII.1 from NNDC with IAEA fallback; JEFF-3.3, JENDL-5, TENDL-2023, and CENDL-3.2 from IAEA)
- Spectrum fitting -- Levenberg-Marquardt and Poisson/KL divergence optimizers with analytical Jacobians
- Spatial mapping -- parallel per-pixel fitting via rayon for 2D isotopic density maps
- Detectability analysis -- energy-window optimization for trace element sensitivity
- Python bindings -- full API via PyO3, pip-installable
- Desktop GUI -- egui application with Guided and Studio modes
Installation
Python (recommended)
pip install nereids
Requires Python >= 3.10. Prebuilt wheels are available for Linux (x86_64), macOS (ARM), and Windows (x86_64).
Rust
Add individual crates to your Cargo.toml:
[dependencies]
nereids-core = "0.1"
nereids-endf = "0.1"
nereids-physics = "0.1"
GUI application
macOS (Homebrew):
brew tap ornlneutronimaging/nereids
brew install --cask nereids
macOS/Linux (pip):
pip install nereids-gui
nereids-gui
MCP server for AI agents
NEREIDS can run as a local Model Context Protocol server so AI agents can inspect neutron-resonance data, fit spectra, and run spatial density-map workflows through the Python bindings.
pip install "nereids[mcp]"
nereids-mcp
Example MCP client configuration:
{
"mcpServers": {
"nereids": {
"command": "nereids-mcp"
}
}
}
The server exposes low-level physics tools (load_endf,
compute_cross_sections, compute_transmission, detect_isotopes) and
workflow tools for agent-driven processing:
extract_resonance_manifest(dataset_path)reads an sMCP-style manifest.validate_resonance_dataset(dataset_path)checks data, isotope, and resolution configuration.process_resonance_dataset(dataset_path)runs asingle_spectrumordensity_mapworkflow and writes compact.npzresult artifacts.
Datasets can include manifest_intermediate.md, smcp_manifest.md,
nereids_manifest.md, nereids_mcp.json, or analysis.json. Markdown
manifests use frontmatter between --- delimiters; JSON frontmatter is
supported without extra YAML dependencies.
See the MCP server guide for supported data kinds, manifest fields, NeXus histogram handling, and output artifacts.
Minimal spectrum manifest:
---
{
"name": "venus-hf-spectrum",
"tool": "nereids",
"physics": "neutron-resonance",
"analysis": {
"mode": "single_spectrum",
"data": {
"kind": "counts_npz",
"path": "aggregated_hf_120min.npz",
"pc_ratio": 5.98
},
"isotopes": [
{"isotope": "Hf-177", "endf_file": "Hf-177.endf", "initial_density": 1e-5}
],
"fit": {"solver": "lm", "fit_domain": "transmission", "max_iter": 100},
"resolution": {
"kind": "gaussian",
"flight_path_m": 25.0,
"delta_t_us": 0.5,
"delta_l_m": 0.005
},
"output": {"directory": "output"}
}
}
---
From source
git clone https://github.com/ornlneutronimaging/NEREIDS.git
cd NEREIDS
cargo build --workspace --release
cargo test --workspace --exclude nereids-python
Python bindings require maturin:
pip install maturin
maturin develop --release -m bindings/python/Cargo.toml
Quick Start (Python)
import numpy as np
import nereids
# Load ENDF resonance data
u238 = nereids.load_endf(92, 238)
fe56 = nereids.load_endf(26, 56)
# Energy grid (1-200 eV covers the strong U-238 resonances)
energies = np.linspace(1.0, 200.0, 5000)
# Forward model: transmission through a mixed sample
transmission = nereids.forward_model(
energies,
isotopes=[(u238, 0.005), (fe56, 0.01)], # (data, areal density in at/barn)
temperature_k=293.6,
)
# Spatial mapping with typed API
trans_3d = transmission[:, None, None] * np.ones((1, 4, 4)) # (n_e, ny, nx)
sigma_3d = np.full_like(trans_3d, 0.01)
data = nereids.from_transmission(trans_3d, sigma_3d)
result = nereids.spatial_map_typed(data, energies, [u238, fe56])
print(f"Converged: {result.n_converged}/{result.n_total} pixels")
See the examples/notebooks/ directory for 18 tutorial notebooks covering foundations, building blocks, workflows, and applications.
Architecture
NEREIDS is organized as a Rust workspace with layered crates:
endf-mat ENDF MAT lookup tables (no deps of its own; used by core + endf)
nereids-core Shared types, physical constants, isotope registry
|
nereids-endf ENDF file retrieval, parsing, resonance data
|
nereids-physics Cross-sections, broadening, transmission model
|
nereids-fitting LM and Poisson/KL optimizers
|
nereids-io TIFF/NeXus I/O, TOF normalization, rebinning
|
nereids-pipeline End-to-end orchestration, spatial mapping (rayon)
|
+-- nereids-python PyO3 Python bindings (bindings/python)
+-- nereids-gui egui desktop application (apps/gui)
| Crate | Description |
|---|---|
endf-mat |
ENDF MAT number lookup, element symbols, natural isotopic abundances |
nereids-core |
Core types, physical constants, traits |
nereids-endf |
ENDF file retrieval, caching, resonance parameter parsing |
nereids-physics |
Cross-section calculation, broadening, transmission model |
nereids-io |
TIFF/NeXus data I/O, VENUS normalization |
nereids-fitting |
Optimization engine (LM, Poisson/KL) |
nereids-pipeline |
End-to-end orchestration and spatial mapping |
nereids-python |
PyO3 Python bindings for Jupyter |
nereids-gui |
egui desktop application |
Documentation
- User Guide -- Installation, quickstart, architecture
- API Reference -- Rustdoc for all crates
- Jupyter Notebooks -- 18 tutorials organized by complexity
Troubleshooting
Log files
The NEREIDS desktop GUI (nereids-gui) writes daily-rotated log files
to a platform-specific data directory, retaining the last 7 days. Each
day's file is named nereids-gui.YYYY-MM-DD.log (UTC date):
| Platform | Log directory |
|---|---|
| macOS | ~/Library/Application Support/NEREIDS/logs/ |
| Linux | ~/.local/share/NEREIDS/logs/ (honours $XDG_DATA_HOME) |
| Windows | %APPDATA%\NEREIDS\logs\ |
From the running app, use Help → Open log folder (reveals today's log file in your platform's file manager) or Help → Copy log path to copy the full path to the clipboard.
The default log level is info. Override with the NEREIDS_LOG
environment variable (or the standard RUST_LOG), e.g.:
NEREIDS_LOG=debug nereids-gui
NEREIDS_LOG="nereids_pipeline=trace,info" nereids-gui
NEREIDS_LOG takes precedence over RUST_LOG. Panics are captured to
the log file with a backtrace before the process exits — please attach
the relevant log file when reporting bugs.
Citation
If you use NEREIDS in your research, please cite:
@software{nereids2026,
author = {Zhang, Chen and Bilheux, Jean-Christophe and Tang, Shiming and Bilheux, Hassina},
title = {{NEREIDS}: Neutron Resonance Resolved Imaging Data Analysis System},
year = {2026},
publisher = {Zenodo},
doi = {10.5281/zenodo.18973054},
url = {https://doi.org/10.5281/zenodo.18973054}
}
Contributing
See CONTRIBUTING.md for development setup, coding standards, and the PR process.
License
MIT. See LICENSE for details.
Copyright (c) 2025-2026, UT-Battelle, LLC (Oak Ridge National Laboratory).
Release files for nereids 0.3.0
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