Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021) 2022
DOI: 10.22323/1.395.0771
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Deep-learning-driven event reconstruction applied to simulated data from a single Large-Sized Telescope of CTA

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“…The TRN model are trained and tested with a modest cut, adapted from Ref. [14], where faint images (Hillas intensity less than 50 photoelectrons) and images close to the camera edge (leakage2 parameter more than 0.2) are discarded. For the stereoscopic reconstruction, faint and truncated images are kept, but a multiplicity cut of four or more triggered telescopes is applied.…”
Section: Data Selection (Quality Cuts)mentioning
confidence: 99%
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“…The TRN model are trained and tested with a modest cut, adapted from Ref. [14], where faint images (Hillas intensity less than 50 photoelectrons) and images close to the camera edge (leakage2 parameter more than 0.2) are discarded. For the stereoscopic reconstruction, faint and truncated images are kept, but a multiplicity cut of four or more triggered telescopes is applied.…”
Section: Data Selection (Quality Cuts)mentioning
confidence: 99%
“…Previous works have demonstrated the potential application of these algorithms for IACT event reconstruction [8][9][10][11][12][13]. DCN-based monoscopic telescope performance and the application of DCNs on observational data from the first Large-Sized Telescope (LST-1 prototype) of CTA North is discussed in these proceedings elsewhere [14,15]. As a natural continuation of this line of work, this contribution focuses on full-event reconstruction of MC-simulated stereoscopic events.…”
Section: Introductionmentioning
confidence: 99%
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