A novel energy reconstruction method for the MAGIC stereoscopic observation
Abstract
We report the successful development of a novel methodology of energy reconstruction for very high energy gamma rays detected with Imaging Atmospheric Cherenkov Telescopes (IACTs). This methodology, based on the machine learning algorithm Random Forest, and named RF-Erec, has been adjusted for being used with data from the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) stereo telescope system, which is a worldwide leading instrument for gamma-ray astronomy in the energy range from about 20GeV to beyond 100TeV. The RF-Erec has been evaluated using different realistic scenarios with Monte Carlo simulated data and real observations from the Crab Nebula (the standard candle for the VHE gamma-ray community). This new methodology has been validated by the MAGIC software board, and it is implemented and ready-to-use in the MAGIC Analysis and Reconstruction Software (MARS). This new methodology, validated by the MAGIC software board, has been implemented and is ready for use in the MAGIC Analysis and Reconstruction Software (MARS). We demonstrate that, in comparison to the previous energy reconstruction methodology for MAGIC data, which relied on Look-Up-Tables (LUTs- Erec) and has been utilized in over 100 scientific publications over the last decade, RF-Erec significantly enhances the energy reconstruction of gamma rays. This improvement extends the capabilities of the MAGIC telescopes. Specifically, when quantifying the energy resolution with the width of a Gaussian fitted to the error distribution (resolution-
- Publication:
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Astroparticle Physics
- Pub Date:
- June 2024
- DOI:
- arXiv:
- arXiv:2212.03592
- Bibcode:
- 2024APh...15802937I
- Keywords:
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- Very high energy gamma ray;
- Cherenkov telescopes;
- Energy reconstruction;
- Random forest;
- Astrophysics - Instrumentation and Methods for Astrophysics;
- Astrophysics - High Energy Astrophysical Phenomena
- E-Print:
- Accepted version