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Study of MLP-based classification of multi-cluster TPC signals
- 1 New York University
- 2 Milton International School
- 3 Newchannel International School
- 4 Hefei No.8 Highschool
- 5 University of California
* Author to whom correspondence should be addressed.
Abstract
This study focuses on the classification of multi-cluster events based on a parameterization of data from a time projection chamber using machine learning. Samples containing a mixture of single and overlapping two-cluster events, both in one and two dimensions, were studied using multi-layer perceptrons and other MVA algorithms provided in the Scikit-learn package. The classification was based on various sets of features and classification accuracies of up to 97% for 1D clusters and 97% for 2D clusters were obtained. This study demonstrates that the efficient classification of signals for further processing through machine learning is feasible and efficient.
Keywords
Machine learning, Scikit-learn, Classification
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Cite this article
Liu,N.;Shao,D.;Tan,J.;Wan,Q.;Zhou,T. (2023). Study of MLP-based classification of multi-cluster TPC signals. Theoretical and Natural Science,5,627-637.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Computing Innovation and Applied Physics (CONF-CIAP 2023)
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