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SELF-ORGANISING NETWORKS FOR CLASSIFICATION: DEVELOPING APPLICATIONS TO SCIENCE ANALYSIS FOR ASTROPARTICLE PHYSICS

DE ANGELIS, Alessandro
•
P. BOINEE
•
M. FRAILIS
•
E. MILOTTI
2004
  • journal article

Periodico
PHYSICA. A
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). (C) 2003 Published by Elsevier B.V.
DOI
10.1016/j.physa.2004.02.023
WOS
WOS:000222215400008
Archivio
http://hdl.handle.net/11390/882776
info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-2942529265
Diritti
metadata only access
Web of Science© citazioni
0
Data di acquisizione
Mar 28, 2024
Visualizzazioni
1
Data di acquisizione
Apr 19, 2024
Vedi dettagli
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