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 a single_spectrum or density_map workflow and writes compact .npz result 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).