Logo image
Back
Comprehensive machine learning model comparison for Cherenkov and Scintillation light separation due to particle interactions
Journal article   Open access   Peer reviewed

Comprehensive machine learning model comparison for Cherenkov and Scintillation light separation due to particle interactions

Merve Tas, Emrah Tiras, Dilara Kizilkaya, Muhammet Anil Yagiz and Mustafa Kandemir
Physica scripta, Vol.101(14), p.146003
04/10/2026
DOI: 10.1088/1402-4896/ae5132
Appears in  UI Libraries Support Open Access
url
https://doi.org/10.1088/1402-4896/ae5132View
Published (Version of record) Open Access

Abstract

The demand for novel detector mediums such as Water-based Liquid Scintillator (WbLS) has increased over the last few decades due to their capability for both low energy particle interactions and higher light yield. Recently, the usage of machine learning (ML) methods in high-energy physics has also been increasing. The ML and AI methods are used in many physics projects in the field since they provide effective and sensitive results. In this study, we aimed to develop a comprehensive analysis of water Cherenkov detectors and perform physics analyses to efficiently separate Cherenkov and scintillation photons with ML algorithms using the data from the WbLS detector environment. The main goal of this study was to produce more precise solutions to physics problems, such as signal classification, by applying ML techniques to the simulation and experimental data. Here, we trained more than 20 ML models, and our results revealed that three machine learning models, XGBoost, Light GBM, and Random Forest models, and their ensemble model gave us more than 95% accuracy for separating Cherenkov and scintillation photons with balanced and unbalanced datasets. This represents a significant increase in accuracy compared to the results of the classical method, which involves simple time cuts.
 UIOWA OA Agreement

Details

Metrics

1 Record Views
Logo image