4.6 Article

A Comparative Analysis of Machine Learning Techniques for Muon Count in UHECR Extensive Air-Showers

期刊

ENTROPY
卷 22, 期 11, 页码 -

出版社

MDPI
DOI: 10.3390/e22111216

关键词

machine learning; Pierre Auger Observatory; muon count; regression; LSSVM

资金

  1. Spanish Ministry of Economy and Competitiveness-MINECO [FPA2015-70420-C2-2-R, FPA2017-85197-P, RTI2018-101674-B-I00]
  2. European Regional Development Fund-ERDF [FPA2015-70420-C2-2-R, FPA2017-85197-P, RTI2018-101674-B-I00]

向作者/读者索取更多资源

The main goal of this work is to adapt a Physics problem to the Machine Learning (ML) domain and to compare several techniques to solve it. The problem consists of how to perform muon count from the signal registered by particle detectors which record a mix of electromagnetic and muonic signals. Finding a good solution could be a building block on future experiments. After proposing an approach to solve the problem, the experiments show a performance comparison of some popular ML models using two different hadronic models for the test data. The results show that the problem is suitable to be solved using ML as well as how critical the feature selection stage is regarding precision and model complexity.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据