The Artificial Neural Networks as a tool for analysis of the individual Extensive Air Showers data

TL;DR

This paper explores how artificial neural networks can analyze individual Extensive Air Showers data, enabling classification and parameter estimation in complex detector systems.

Contribution

It demonstrates the application of new computational methods, specifically ANNs, for analyzing EAS data to classify primary particles and estimate key parameters.

Findings

ANN can classify EAS by primary particle mass

ANN can estimate EAS parameters like muon number

New computational methods are effective for complex detector data

Abstract

In that paper we discuss possibilities of using the Artificial Neural Network technic for the individual Extensive Air Showers data evaluation. It is shown that the recently developed new computational methods can be used in studies of EAS registered by very large and complex detector systems. The ANN can be used to classify showers due to e.g. primary particle mass as well as to find a particular EAS parameter like e.g. total muon number. The examples of both kinds of analysis are given and discussed.