Detection of the Crab Nebula using a Random Forest Analysis of the first TAIGA IACT Data
M. Blank, M. Tluczykont, A. Porelli, R. Mirzoyan, R. Wischnewski, A., K. Awad, M. Brueckner

TL;DR
This paper demonstrates the detection of the Crab Nebula using a hybrid air shower technique with a random forest analysis on initial TAIGA IACT data, achieving significant gamma-ray signal identification.
Contribution
First application of a random forest classifier to TAIGA IACT data for Crab Nebula detection, combining Monte Carlo simulations and real data analysis.
Findings
Detected 163.5 excess events with 8.5 sigma significance.
Observed spectrum fits a power law above 6 TeV.
Achieved a flux normalization of (3.20±0.42)×10^{-10} TeV^{-1} cm^{-2} s^{-1} at 13 TeV.
Abstract
The Tunka Advanced Instrument for Gamma- and cosmic-ray Astronomy (TAIGA) is a multicomponent experiment for the measurement of TeV to PeV gamma- and cosmic rays. Our goal is to establish a novel hybrid direct air shower technique, sufficient to access the energy domain of the long-sought Pevatrons. The hybrid air Cherenkov light detection technique combines the strengths of the HiSCORE shower front sampling array, and two 4 m class, 9.6 deg field of view Imaging Air Cherenkov Telescopes (IACTs). The HiSCORE array provides good angular and shower core position resolution, while the IACTs provide the image shape and orientation for gamma-hadron separation. In future, an additional muon detector will be used for hadron tagging at 100 TeV energies. Here, only data from the first IACT of the TAIGA experiment are used. A random forest algorithm was trained using Monte…
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