Würzburg 2018 – scientific programme
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T: Fachverband Teilchenphysik
T 21: Experimentelle Methoden der Astroteilchenphysik I
T 21.4: Talk
Monday, March 19, 2018, 16:50–17:05, Z6 - SR 2.013
Substantial improvement in the MAGIC energy reconstruction through machine learning algorithms — •Kazuma Ishio1, David Paneque1, Abelardo Moralejo2, and Julian Sitarek3 for the MAGIC collaboration — 1Max-Planck-Institut für Physik — 2Institut de Fisica d'Altes Energies (IFAE), The Barcelona Institute of Science and Technology, Bellaterra (Barcelona), Spain — 3Division of Astrophysics, University of Lodz, Lodz, Poland
The MAGIC telescopes perform gamma-ray astronomy at energies above 50 GeV and extending to about 50 TeV. The energy of the detected gamma ray is estimated with a set of parameters extracted from the shower image on the cameras, and using Look-Up-Tables (LUTs) derived from Monte Carlo simulations. In this talk, I will show that a strategy using random forest (RF) can substantially improve (with respect to LUT) both the energy bias (30% improvement below 100 GeV) and the energy resolution (about 50% improvement above TeV energies). I will show that the choice of the image parameters and the procedure of nesting the RF process across the entire energy range play a crucial role in this improvement.