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

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arxiv cs/0402014 v1 pith:3DPS3PLS submitted 2004-02-09 cs.NE astro-phcs.AI

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

classification cs.NE astro-phcs.AI
keywords classificationanalysisastroparticledataexperimentsphysicstoolsable
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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