1. Improving the detection efficiency of IRAND based on machine learning.
- Author
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Karimi, Javad, Rahmani, Faezeh, and Jia, S. Bijan
- Subjects
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MACHINE learning , *DATA mining , *RANDOM forest algorithms , *BETA decay , *ANTINEUTRINOS - Abstract
The challenges in detecting antineutrinos by segmented antineutrino detectors (AD) pushed us to get more exact results using new methods, including data mining and machine learning (ML). In this research, IRAND (a segmented plastic scintillator IRan ANtineutrino Detector; a 10 × 10 array of plastic scintillation) has been simulated using Geant4 Toolkit. Several features of energy deposition including behavior of time, energy, position have been collected and stored in detail from each event to provide the complete dataset with a minor loss for use in powerful ML packages. We utilized a variety of classifiers in ML in which Random Forest shows the best performance. Also, a Geant4-based dedicated code (IRAND-Sim) has been provided for IRAND. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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