Citing#

If you used this codebase or any software making use of it in a scientific publication, we ask you to cite the following paper:

Nicuşan AL, Windows-Yule CR. Positron emission particle tracking using machine learning. Review of Scientific Instruments. 2020 Jan 1;91(1):013329. https://doi.org/10.1063/1.5129251

Because pept is a project bringing together the expertise of many people, it hosts multiple algorithms that were developed and published in other papers. Please check the documentation of the pept algorithms you are using in your research and cite the original papers mentioned accordingly.

References#

Papers presenting PEPT algorithms included in this library: 1, 2, 3.

1

Parker DJ, Broadbent CJ, Fowles P, Hawkesworth MR, McNeil P. Positron emission particle tracking-a technique for studying flow within engineering equipment. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 1993 Mar 10;326(3):592-607.

2

Nicuşan AL, Windows-Yule CR. Positron emission particle tracking using machine learning. Review of Scientific Instruments. 2020 Jan 1;91(1):013329.

3

Wiggins C, Santos R, Ruggles A. A feature point identification method for positron emission particle tracking with multiple tracers. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2017 Jan 21;843:22-8.