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

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

2004-02-09

arxiv.org/abs/cs/0402014v1

Computer Science

Neural and Evolutionary Computing

Scientific paper

10.1016/j.physa.2004.02.023

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).

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