Implementation of the Random Forest method for the Imaging Atmospheric Cherenkov Telescope MAGIC

  • J. Albert
  • , E. Aliu
  • , H. Anderhub
  • , P. Antoranz
  • , A. Armada
  • , M. Asensio
  • , C. Baixeras
  • , J. A. Barrio
  • , H. Bartko
  • , D. Bastieri
  • , J. Becker
  • , W. Bednarek
  • , K. Berger
  • , C. Bigongiari
  • , A. Biland
  • , R. K. Bock
  • , P. Bordas
  • , V. Bosch-Ramon
  • , T. Bretz
  • , I. Britvitch
  • M. Camara, E. Carmona, A. Chilingarian, S. Ciprini, J. A. Coarasa, S. Commichau, J. L. Contreras, J. Cortina, M. T. Costado, V. Curtef, V. Danielyan, F. Dazzi, A. De Angelis, C. Delgado, R. de los Reyes, B. De Lotto, E. Domingo-Santamaría, D. Dorner, M. Doro, M. Errando, M. Fagiolini, D. Ferenc, E. Fernández, R. Firpo, J. Flix, M. V. Fonseca, L. Font, M. Fuchs, N. Galante, R. J. García-López, M. Garczarczyk, M. Gaug, M. Giller, F. Goebel, D. Hakobyan, M. Hayashida, T. Hengstebeck, A. Herrero, D. Höhne, J. Hose, S. Huber, C. C. Hsu, P. Jacon, T. Jogler, R. Kosyra, D. Kranich, R. Kritzer, A. Laille, E. Lindfors, S. Lombardi, F. Longo, J. López, M. López, E. Lorenz, P. Majumdar, G. Maneva, K. Mannheim, M. Mariotti, M. Martínez, D. Mazin, C. Merck, M. Meucci, M. Meyer, J. M. Miranda, R. Mirzoyan, S. Mizobuchi, A. Moralejo, D. Nieto, K. Nilsson, J. Ninkovic, E. Oña-Wilhelmi, N. Otte, I. Oya, M. Panniello, R. Paoletti, J. M. Paredes, M. Pasanen, D. Pascoli, F. Pauss, R. Pegna, M. Persic, L. Peruzzo, A. Piccioli, N. Puchades, E. Prandini, A. Raymers, W. Rhode, M. Ribó, J. Rico, M. Rissi, A. Robert, S. Rügamer, A. Saggion, T. Y. Saito, A. Sánchez, P. Sartori, V. Scalzotto, V. Scapin, R. Schmitt, T. Schweizer, M. Shayduk, K. Shinozaki, S. N. Shore, N. Sidro, A. Sillanpää, D. Sobczynska, F. Spanier, A. Stamerra, L. S. Stark, L. Takalo, P. Temnikov, D. Tescaro, M. Teshima, D. F. Torres, N. Turini, H. Vankov, A. Venturini, V. Vitale, R. M. Wagner, T. Wibig, W. Wittek, F. Zandanel, R. Zanin, J. Zapatero

Research output: Contribution to journalArticlepeer-review

Abstract

The paper describes an application of the tree classification method Random Forest (RF), as used in the analysis of data from the ground-based gamma telescope MAGIC. In such telescopes, cosmic gamma-rays are observed and have to be discriminated against a dominating background of hadronic cosmic-ray particles. We describe the application of RF for this gamma/hadron separation. The RF method often shows superior performance in comparison with traditional semi-empirical techniques. Critical issues of the method and its implementation are discussed. An application of the RF method for estimation of a continuous parameter from related variables, rather than discrete classes, is also discussed.

Original languageEnglish
Pages (from-to)424-432
Number of pages9
JournalNuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment
Volume588
Issue number3
DOIs
StatePublished - Apr 11 2008

Keywords

  • Classification
  • Decision tree
  • Discrimination

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