PyPAS: Positron annihilation spectroscopy in Python

Modern, modular Doppler Broadening (DB) and Coincidence Doppler Broadening (CDB) analysis for Positron Annihilation Spectroscopy (PAS) — with implantation profiles, a finite‑difference diffusion solver, and VEDB fitting.

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Overview

PyPAS unifies PAS spectroscopy and depth profiling in a single Python workflow.
It handles:

• DB/CDB spectrum processing and S/W parameter extraction via direct summation with robust background subtraction
• Thermal implantation profiles (Makhovian and Ghosh)
• 1D positron diffusion equation solving (including drift in electric fields)
• Diffusion length fitting for VEDB measurements

Modular, open‑source, and built on standard scientific Python libraries for transparent, reproducible analysis.

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Install

• Requirements: Python 3.9+, and the rest are listed in requirements.txt. To install dependencies, run:

pip install -r requirements.txt

• Latest (GitHub):

pip install "git+https://github.com/achiyaAmrusi/pyPAS"

• Dev install (editable):

git clone https://github.com/achiyaAmrusi/pyPAS cd pyPAS pip install -e ".[dev]"

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Features

• DB/CDB analysis: Direct S/W extraction with robust background; 2D CDB histograms; DB and resolution projections
• Event filtering: Flexible time‑energy filtering for synchronized detector pairs with pluggable strategies
• Implantation profiles: Makhovian and Ghosh models; multilayer cumulative stitching; support for custom/external profiles
• Diffusion solver: Finite‑difference 1D solver with drift ( v(z) = μ(z)E(z) ); radiative boundary conditions; cell‑centered scheme
• Layered models: Sample/Layer descriptors with depth‑dependent transport coefficients, annihilation rates, and electric fields
• VEDB fitting: Diffusion‑length estimation with confidence intervals; model comparison (e.g., AIC) to guard against overfitting
• Ecosystem: NumPy/SciPy/xarray core; integrates with PySpectrum for spectrum I/O, peak metrics, and uncertainty propagation

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Examples and documentation

• Examples: See the examples/ directory for notebooks covering DB/CDB analysis, implantation profiles, diffusion solutions, and VEDB fitting
• API docs: Docstrings throughout the codebase; browse in your IDE or with Python’s help()

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Project status and roadmap

• Validation: Benchmarked against analytical solutions and SciPy solvers, showing sub‑percent relative errors under appropriate resolution
• Roadmap: Planned extensions include positron lifetime analysis and additional Bayesian workflows for model comparison and uncertainty quantification

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License, contributions, and citation

• License: MIT license
• Contributions: Issues and PRs are welcome. Please include minimal reproducible examples.
• Citation: If PyPAS contributes to your work, please mention the package https://doi.org/10.48550/arXiv.2509.08023.