IBIC2022 - List of Authors (Engel, S.E.)

Paper	Title	Page
WEP42	Application of Machine Learning towards Particle Counting and Identification	508
	S.E. Engel University of Essex, Physics Centre, Colchester, United Kingdom P. Boutachkov, R. Singh GSI, Darmstadt, Germany
	An exploration into the application of three machine learning (ML) approaches to identify and separate events in the detectors used for particle counting at the GSI Helmholtz Centre for Heavy Ion Research. A convolutional neural network (CNN), a shape-based template matching algorithm (STMF) and Peak Property-based Counting Algorithm (PPCA) were developed to accurately count the number of particles without domain-specific knowledge required to run the currently used algorithm. The three domain-agnostic ML algorithms are based on data from scintillation counters commonly used in beam instrumentation and represent proof-of-work for an automated particle counting system. The algorithms were trained on a labelled set of over 150 000 experimental particle data. The results of the three classification approaches were compared to find a solution that best mitigates the effects of particle pile-ups. The two best-achieving algorithms were the CNN and PPCA, achieving an accuracy of 99.8\%. This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under GA No 101004730.
	Poster WEP42 [1.370 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2022-WEP42
About •	Received ※ 11 September 2022 — Revised ※ 25 October 2022 — Accepted ※ 01 December 2022 — Issue date ※ 08 December 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Application of Machine Learning towards Particle Counting and Identification

508

S.E. Engel
University of Essex, Physics Centre, Colchester, United Kingdom
P. Boutachkov, R. Singh
GSI, Darmstadt, Germany

An exploration into the application of three machine learning (ML) approaches to identify and separate events in the detectors used for particle counting at the GSI Helmholtz Centre for Heavy Ion Research. A convolutional neural network (CNN), a shape-based template matching algorithm (STMF) and Peak Property-based Counting Algorithm (PPCA) were developed to accurately count the number of particles without domain-specific knowledge required to run the currently used algorithm. The three domain-agnostic ML algorithms are based on data from scintillation counters commonly used in beam instrumentation and represent proof-of-work for an automated particle counting system. The algorithms were trained on a labelled set of over 150 000 experimental particle data. The results of the three classification approaches were compared to find a solution that best mitigates the effects of particle pile-ups. The two best-achieving algorithms were the CNN and PPCA, achieving an accuracy of 99.8\%.
This project has received funding from the European Union’s Horizon 2020 Research and Innovation programme under GA No 101004730.

Poster WEP42 [1.370 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2022-WEP42

About •

Received ※ 11 September 2022 — Revised ※ 25 October 2022 — Accepted ※ 01 December 2022 — Issue date ※ 08 December 2022

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)