Neural networks for gamma-hadron separation in MAGIC

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.Comment: 6 pages, 4 figures, to be published in the Proceedings of the 6th International Symposium ''Frontiers of Fundamental and Computational Physics'' (FFP6), Udine (Italy), Sep. 26-29, 200

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)

The MAGIC Experiment and Its First Results

With its diameter of 17m, the MAGIC telescope is the largest Cherenkov detector for gamma ray astrophysics. It is sensitive to photons above an energy of 30 GeV. MAGIC started operations in October 2003 and is currently taking data. This report summarizes its main characteristics, its rst results and its potential for physics.Comment: 6 pages, 3 figures, to be published in the Proceedings of the 6th International Symposium ''Frontiers of Fundamental and Computational Physics'' (FFP6), Udine (Italy), Sep. 26-29, 200

Simulating the High Energy Gamma-ray sky seen by the GLAST Large Area Telescope

This paper presents the simulation of the GLAST high energy gamma-ray telescope. The simulation package, written in C++, is based on the Geant4 toolkit, and it is integrated into a general framework used to process events. A detailed simulation of the electronic signals inside Silicon detectors has been provided and it is used for the particle tracking, which is handled by a dedicated software. A unique repository for the geometrical description of the detector has been realized using the XML language and a C++ library to access this information has been designed and implemented. A new event display based on the HepRep protocol was implemented. The full simulation was used to simulate a full week of GLAST high energy gamma-ray observations. This paper outlines the contribution developed by the Italian GLAST software group.Comment: 6 pages, 4 figures, to be published in the Proceedings of the 6th International Symposium ''Frontiers of Fundamental and Computational Physics'' (FFP6), Udine (Italy), Sep. 26-29, 200

ANN-based energy reconstruction procedure for TACTIC gamma-ray telescope and its comparison with other conventional methods

The energy estimation procedures employed by different groups, for determining the energy of the primary $\gamma$-ray using a single atmospheric Cherenkov imaging telescope, include methods like polynomial fitting in SIZE and DISTANCE, general least square fitting and look-up table based interpolation. A novel energy reconstruction procedure, based on the utilization of Artificial Neural Network (ANN), has been developed for the TACTIC atmospheric Cherenkov imaging telescope. The procedure uses a 3:30:1 ANN configuration with resilient backpropagation algorithm to estimate the energy of a $\gamma$-ray like event on the basis of its image SIZE, DISTANCE and zenith angle. The new ANN-based energy reconstruction method, apart from yielding an energy resolution of $\sim$ 26%, which is comparable to that of other single imaging telescopes, has the added advantage that it considers zenith angle dependence as well. Details of the ANN-based energy estimation procedure along with its comparative performance with other conventional energy reconstruction methods are presented in the paper and the results indicate that amongst all the methods considered in this work, ANN method yields the best results. The performance of the ANN-based energy reconstruction has also been validated by determining the energy spectrum of the Crab Nebula in the energy range 1-16 TeV, as measured by the TACTIC telescope.Comment: 23pages, 9 figures Accepted for publication in NIM

GLAST Large Area Telescope simulation tools

This paper presents the simulation of the GLAST high energy gamma-ray telescope. The simulation package, written in C++, is based on the Geant4 toolkit, and it is integrated into a general framework used to process events. A detailed simulation of the electronic signals inside silicon detectors has been provided and it is used for the particle tracking, which is handled by a dedicated software. A unique repository for the geometrical description of the detector has been realized using the XML language and a C++ library to access this information has been designed and implemented. A new event display based on the HepRep protocol is being implemented. The GLAST satellite parameters derived from the simulation are used in a fast simulator to obtain a "snapshot" of the gamma-ray sky. This paper outlines the contribution developed by the Italian GLAST software group