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

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dc.contributor.authorAntoranz Canales, Pedro
dc.contributor.authorBarrio Uña, Juan Abel
dc.contributor.authorContreras González, José Luis
dc.contributor.authorFonseca González, María Victoria
dc.contributor.authorLópez Moya, Marcos
dc.contributor.authorMiranda Pantoja, José Miguel
dc.contributor.authorNieto Castaño, Daniel
dc.date.accessioned2023-06-20T10:37:56Z
dc.date.available2023-06-20T10:37:56Z
dc.date.issued2008-04-11
dc.description© Elsevier Science.We thank Jens Zimmermann for fruitful discussions about the RF method and for comparisons of the RF method with a Neural Net approach.
dc.description.abstractThe 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. (C) 2008 Published by Elsevier B.V.
dc.description.departmentDepto. de Estructura de la Materia, Física Térmica y Electrónica
dc.description.facultyFac. de Ciencias Físicas
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/23164
dc.identifier.doi10.1016/j.nima.2007.11.068
dc.identifier.issn0168-9002
dc.identifier.officialurlhttp://dx.doi.org/10.1016/j.nima.2007.11.068
dc.identifier.relatedurlhttp://www.sciencedirect.com
dc.identifier.relatedurlhttp://arxiv.org/abs/0709.3719
dc.identifier.urihttps://hdl.handle.net/20.500.14352/50837
dc.issue.number3
dc.journal.titleNuclear instruments & methods in physics research. Section A, Accelerators spectrometers detectors and associated equipment
dc.language.isoeng
dc.page.final432
dc.page.initial424
dc.publisherElsevier Science BV
dc.rights.accessRightsopen access
dc.subject.cdu537
dc.subject.cdu539.1
dc.subject.keywordGamma-Ray
dc.subject.keywordSeparation
dc.subject.keywordRadiation.
dc.subject.ucmElectrónica (Física)
dc.subject.ucmElectricidad
dc.subject.ucmFísica nuclear
dc.subject.unesco2202.03 Electricidad
dc.subject.unesco2207 Física Atómica y Nuclear
dc.titleImplementation of the Random Forest method for the Imaging Atmospheric Cherenkov Telescope MAGIC
dc.typejournal article
dc.volume.number588
dcterms.references[1] A.M. Hillas, in: Proceedings of the 19th International Cosmic Ray Conference, ICRC 1985, La Jolla, vol. 3, 1985, p. 445. [2] A.M. Hillas, Space Sci. Rev. 75 (1996) 17. [3] D.J. Fegan, J. Phys. G, Nucl. Part. Phys 23 (1997) 1013. [4] F. Aharonian, et al., Astropart. Phys. 6 (1997) 343. [5] H. Krawczynski, et al., Astropart. Phys. 25 (2006) 380. [6] R.K. Bock, A. Chilingarian, M. Gaug, et al., Nucl. Instr. and Meth. A 516 (2004) 511. [7] T. Hengstebeck, Ph.D. Thesis, Mathematisch-Naturwissenschaftliche Fakultät I, Humboldt-Universität zu Berlin, März 2007, Available at URL: hhttp://edoc.hu-berlin.de/docviews/abstract.php?id=28015i. [8] E. Lorenz, New Astron. Rev. 48 (2004) 339. [9] L. Breimann, J.H. Friedmann, R.A. Olshen, C.J. Stone, Classification and Regression Trees, Wadsworth, Belmont, CA, 1983. [10] J. Albert, et al., Astrophys. J. 664 (2007) L87. [11] J. Albert, et al., Astrophys. J. 665 (2007) L51. [12] J. Albert, et al., Astrophys. J. 669 (2007) 1143. [13] J. Albert, et al., Astrophys. J. 674 (2008) 1037. [14] L. Breiman, FORTRAN program random forests, Version 3.1, and L. Breiman, Manual on setting up, using, and understanding random forests V3. 1, both available at: hhttp://oz.berkeley.edu/users/breimani. [15] R. Brun, F. Rademakers hhttp://root.cern.ch/i. [16] J. Zimmermann, Ph.D. Thesis, Fakultät für Physik, Ludwig-Maximilians-Universität München, Juni 2005, Available at URL:hhttp://edoc.mpg.de/274832i.
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