Self-Organising Networks for Classification: developing Applications to Science Analysis for Astroparticle Physics

TL;DR

This paper discusses the development of self-organising neural networks aimed at automatic classification in astroparticle physics, facilitating the recognition of new phenomena across diverse detector data sources.

Contribution

It introduces a novel application of self-organising networks for classifying astrophysical events, integrating multi-source data for improved analysis.

Findings

Enhanced classification accuracy demonstrated in simulated datasets

Effective integration of multi-detector data sources

Potential for real-time analysis in future experiments

Abstract

Physics analysis in astroparticle experiments requires the capability of recognizing new phenomena; in order to establish what is new, it is important to develop tools for automatic classification, able to compare the final result with data from different detectors. A typical example is the problem of Gamma Ray Burst detection, classification, and possible association to known sources: for this task physicists will need in the next years tools to associate data from optical databases, from satellite experiments (EGRET, GLAST), and from Cherenkov telescopes (MAGIC, HESS, CANGAROO, VERITAS).