Self-Organising Networks for Classification: developing Applications to Science Analysis for Astroparticle Physics
<|reference_start|>Self-Organising Networks for Classification: developing Applications to Science Analysis for Astroparticle Physics: 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).<|reference_end|>
@article{de angelis2004self-organising,
    title={Self-Organising Networks for Classification: developing Applications to
  Science Analysis for Astroparticle Physics},
    author={A. De Angelis, P. Boinee, M. Frailis, E. Milotti},
    journal={arXiv preprint arXiv:cs/0402014},
    year={2004},
    doi={10.1016/j.physa.2004.02.023},
    archivePrefix={arXiv},
    eprint={cs/0402014},
    primaryClass={cs.NE astro-ph cs.AI}
}