分类的自组织网络：天体粒子物理学科学分析的发展应用
Self-Organising Networks for Classification: developing Applications to Science Analysis for Astroparticle Physics
摘要：
天体粒子实验中的物理分析需要有识别新现象的能力;为了确定什么是新现象，必须开发自动分类工具，能够将最终结果与来自不同探测器的数据进行比较。一个典型的例子是伽玛射线暴的探测、分类和与已知源的可能关联问题：对于这项任务，物理学家在未来几年将需要工具来关联来自光学数据库、卫星实验（EGRET、GLAST）和切伦科夫望远镜（MAGIC、HESS、CANGAROO、VERITAS）的数据。
英文摘要：
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).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
