Using the data taken from Tibet II High Density (HD) Array (1997 February-1999 and Tibet-III array (1999 November-2005, our previous northern sky survey for TeV γ−ray point sources has now been updated by a factor of 2.8 improved statistics. From 0.0• to 60.0 • in declination (Dec) range, no new TeV γ−ray point sources with sufficiently high significance were identified while the well-known Crab Nebula and Mrk421 remain to be the brightest TeV γ−ray sources within the field of view of the Tibet air shower array. Based on the currently available data and at the 90% confidence level (C.L.), the flux upper limits for different power law index assumption are re-derived, which are approximately improved by 1.7 times as compared with our previous reported limits.
Machine learning algorithms can learn the rules and patterns of big data through computers, mining potential information from them, and is widely used to solve classification, regression, clustering, and other issues. Firstly, the paper used CORSIKA software to simulate the process of cosmic rays cascade shower in the atmosphere, generating information such as the initial energy, zenith angle, azimuth angle of cosmic ray particles. Then,used the Geant4 toolkit to conduct thermal neutron detector response simulation, generated 2000 particles each in Proton,Helium,CNO,MgAlSi and Iron.Based on the experimental simulation data of thermal neutron detector,this paper constructs machine learning models for cosmic ray particles identification using decision tree (DT), random forest (RF) and BP neural network(BP NN) respectively.For each particle,all the machine learning algorithms were used for model training based on the simulation data.The cross grid search method was used to adjust the hyperparameters of each machine learning algorithm. The AUC value and Q quality factor value of each algorithm were used as evaluation indexes for particle compositions identification.The AUC value is a general indicator for evaluating algorithm performance in machine learning and the Q quality factor value is a evaluation index commonly used in the field of high energy physics.The Experimental results show that different machine learning models have great influence on particles prediction accuracy and the random forest cosmic ray particles identification model has sufficient accuracy and generalization capability. In the test, the decision tree algorithm adjusted by cross grid search method is sensitive to the medium components (CNO and MgAlSi). The AUC values of the algorithm are all above 0.95 and the Q quality factor values all above 6. The random forest algorithm adjusted by the cross grid search method has the best effect on the identification of cosmic ray particles. The AUC value of the algorithm are all more than 0.92 and the Q quality factor values are all more than 4. BP neural network algorithm is only sensitive to Proton and Iron. This study provides a new method and selection for the identification and screening of cosmic ray particles and it also provides a new idea for the following measurement of cosmic ray energy spectrum by thermal neutron detector.
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