An Open-Source Iterative Python Module for the Automated Identification of Photopeaks in Photon Spectra

Sam J Fearn*, Suresh Kaluvan, Thomas Bligh Scott , Peter George Martin

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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The UK, and other countries worldwide, have benefited from nuclear energy to provide a low-carbon power source to fuel their increasing populations and industrial growth. In support of the extensive end-of-life decommissioning activities ongoing globally, as well as to enable accident clean-up and nuclear security/monitoring provisions; systems are necessary to rapidly and accurately detect and attribute the nature of any nuclear and/or radioactive materials. To facilitate the utilisation of the increasing suite of miniaturised radiation sensor systems for a range of largely robotic (whether aerial, underwater or ground-based) deployment applications, without the issue of being ’tethered’ to a specific vendor or system, an open-source and compact python module has been developed. Within this readily integrable code-base designed for incorporation into wider software architectures (such as the Robotic Operating System, or ROS), gamma-ray spectroscopy data are recorded in real-time and processed with a peak identification procedure once sufficient data has been recorded. Iterative peak-fitting is applied to determine the isotopic compositions of the incident radiation. The stand-alone application comprises two connected components: a small detector-specific module (or wrapper) that translates a detector’s serial output into the desired format, ahead of the main analysis function. Second, a photopeak identification is performed through an algorithm which uses the second derivative of the spectrum. The peaks identified are subsequently labelled by the program, utilizing the properties of all the mathematically detected/derived peaks, and finally output in a user-defined format for subsequent usage.
Original languageEnglish
Article number2020014
Pages (from-to)193-214
Number of pages18
Publication statusPublished - 25 Apr 2022


  • python
  • software
  • photopeak
  • identification
  • gamma-ray
  • spectroscopy


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