Neural networks for gamma-hadron separation in MAGIC

Details Neural networks for gamma-hadron separation in MAGIC Neural networks for gamma-hadron separation in MAGIC

2005-03-24

arxiv.org/abs/astro-ph/0503539v1

Astronomy and Astrophysics

Astrophysics

6 pages, 4 figures, to be published in the Proceedings of the 6th International Symposium ''Frontiers of Fundamental and Compu

Scientific paper

Neural networks have proved to be versatile and robust for particle separation in many experiments related to particle astrophysics. We apply these techniques to separate gamma rays from hadrons for the MAGIC Cerenkov Telescope. Two types of neural network architectures have been used for the classi cation task: one is the MultiLayer Perceptron (MLP) based on supervised learning, and the other is the Self-Organising Tree Algorithm (SOTA), which is based on unsupervised learning. We propose a new architecture by combining these two neural networks types to yield better and faster classi cation results for our classi cation problem.

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