Application of machine learning in Cosmic ray particle identification
نویسندگان
چکیده
Machine learning algorithms can learn the rules and patterns of big data through computers, excavate potential information hidden behind data, be widely used to solve classification, regression, clustering, other problems. Firstly, this paper uses CORSIKA software simulate process cosmic ray cascade shower in atmosphere, generating such as initial energy, zenith angle, azimuth angle particles. Then, Geant4 toolkit conduct thermal neutron detector response simulation, 4000 particles each proton, helium, CNO, MgAlSi iron. Based on experimental simulation detector, constructs machine models for identifying by using decision tree (DT), random forest (RF) BP neural network (BP NN) respectively. For particle, all are model training based data. The cross grid search method is adjust hyper parameters algorithm. AUC value <i>Q</i> quality factor algorithm evaluation indexes particle composition identification. a general indicator evaluating performance an index commonly field high energy physics. Experimental results show that different have great influence prediction accuracy, identification has sufficient accuracy generalization capability. In test, adjusted sensitive medium components (CNO MgAlSi). values above 0.95 6. best effect more than 0.92 4. only proton This study provides new selection screening it also idea following measurement spectrum detector.
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ژورنال
عنوان ژورنال: Chinese Physics
سال: 2023
ISSN: ['1000-3290']
DOI: https://doi.org/10.7498/aps.72.20230334