Low-energy-section gamma proton high-energy particle identification method based on LHAASO experiment

Abstract

The invention discloses a low-energy-section gamma proton high-energy particle identification method based on an LHAASO experiment, and the method comprises the steps: carrying out the physical feature extraction of an LHAASO-KM2A array through feature engineering based on the original data of the LHAASO experiment, obtaining key physical information features, constructing an identification model based on the physical features through the key physical information features, and carrying out the identification of the high-energy-section gamma proton high-energy particles through the identification model. And carrying out training and feedback adjustment on the model by utilizing the processed key physical information characteristics to obtain an identification model for effectively identifying protons and gamma particles in a low-energy section. According to the method, other features having great influence on high-energy particle identification are obtained and applied to model training, the model identification capability is effectively improved, and the identification model can identify the high-energy particles under the influence of factors such as each energy segment, each order of magnitude and great difference between the number of protons and the number of gamma rays, so that the identification accuracy of the high-energy particles is improved. The method realizes identification in various complex environments, and has good accuracy and robustness.

Description

technical field [0001] The invention relates to the field of identification of cosmic ray high-energy particles, in particular to a method for identifying two types of high-energy particles, proton and gamma. Background technique [0002] In all ground-based cosmic ray observation experiments, the identification of cosmic ray components has always been one of the important topics. Cosmic rays are high-energy particles from cosmic space, which contain a large number of protons and various atomic nuclei. In addition, cosmic rays also contain a small amount of photons, electrons and neutrinos. Scientists have discovered natural high-energy particle beams through the study of cosmic rays. Cosmic rays provide a huge space for the development of the field of particle physics. Many new particles discovered by scientists are based on cosmic ray experiments. While the development of cosmic rays has enabled the development of the field of particle physics, it has also brought a serie...

AI Technical Summary

Problems solved by technology

[0005] In the prior art, the traditional method used in the identification and prediction of cosmic ray components is feature extraction based on artificial feature engineering, such as literature 1: Z.Tian, ​​Z.Wang, Y.Liu, Y.Guo, X.H.Ma, and H.B. Hu, "Study of theγ / pdiscrimination at 100TeV energy range with LHAASO experiment," Astroparticle Physics, Vol.99, pp43-50, Feb.2018. The technical solution is not enough to fully mine the potential feature variables and physical information in the data, so further improvement is needed

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