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process single-shot positron annihlation lifetime spectra

Project description

python tools for analysing single-shot positron annihilation lifetime spectra


Tested using Anaconda (Continuum Analytics) with Python 2.7 and 3.5.

Package dependencies:

  • scipy, numpy, pandas


via pip (recommended):

pip install sspals

alternatively, try the development version

git clone

and then run

python install


Single-shot positron annihilation lifetime spectroscopy (SSPALS) [Ref. 1] essentially consists of studying the number of annihilation gamma-rays measured as a function of time following implantation of a time-focused (~5 ns) positron bunch into a solid target material.

For certain materials a significant fraction of the positrons (~ 30%) will bind to electrons to form positronium (Ps), which can then be re-emitted to vacuum. Ps has a characteristic mean lifetime of 142 ns in vacuum, which makes it relatively easy to identify in SSPALS spectra.

This package includes a handful of useful tools for working with SSPALS data. The two main functions are used to: (i) combine data split across hi/ low gain channels of a digital oscilloscope, and (ii) to estimate the amount of Ps formed using the so-called delayed fraction.

sspals.chmx(hi, low) > Remove zero offset from hi and low gain data, invert and splice together by swapping saturated values from the hi-gain channel for those from the low-gain channel. Apply along rows of a 2D array.

sspals.sspals(arr, dt, limits=[A, B, C]) > Calculate the trigger time t0 (using a cfd) and the delayed fraction (DF) (integral B->C / integral A->C) for each row of a 2D array. Return a pandas DataFrame [(t0, AC, BC, DF)].

Raw data (hi, low) is expected to be 2D arrays of repeat measurements, where each row contains a single SSPALS waveform.

For examples see the IPython/ Jupter notebooks,


  1. D. B. Cassidy et al. (2006), Appl. Phys. Lett., 88, 194105.

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