{"title":"Generation of Grid Surface Detector Data in the Telescope Array Experiment Using Neural Networks","authors":"R. R. Fitagdinov, I. V. Kharuk","doi":"10.3103/S0027134924702138","DOIUrl":null,"url":null,"abstract":"<p>In this article, we talk about generating data obtained in the Telescope Array experiment. For this we are using Wasserstein’s generative adversarial networks. Wasserstein’s generative adversarial networks were trained on data obtained using the Monte Carlo method. To improve the quality of the generation, we add the loss function for the generator, which is based on the physics of the process of spreading an extensive air shower. In the future, this network can be used to search for anomalies and for faster data generation, compared with algorithms based on the Monte Carlo method.</p>","PeriodicalId":711,"journal":{"name":"Moscow University Physics Bulletin","volume":"79 2 supplement","pages":"S684 - S689"},"PeriodicalIF":0.4000,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Moscow University Physics Bulletin","FirstCategoryId":"101","ListUrlMain":"https://link.springer.com/article/10.3103/S0027134924702138","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PHYSICS, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
In this article, we talk about generating data obtained in the Telescope Array experiment. For this we are using Wasserstein’s generative adversarial networks. Wasserstein’s generative adversarial networks were trained on data obtained using the Monte Carlo method. To improve the quality of the generation, we add the loss function for the generator, which is based on the physics of the process of spreading an extensive air shower. In the future, this network can be used to search for anomalies and for faster data generation, compared with algorithms based on the Monte Carlo method.
期刊介绍:
Moscow University Physics Bulletin publishes original papers (reviews, articles, and brief communications) in the following fields of experimental and theoretical physics: theoretical and mathematical physics; physics of nuclei and elementary particles; radiophysics, electronics, acoustics; optics and spectroscopy; laser physics; condensed matter physics; chemical physics, physical kinetics, and plasma physics; biophysics and medical physics; astronomy, astrophysics, and cosmology; physics of the Earth’s, atmosphere, and hydrosphere.