# Monte Carlo study of a single SST-1M prototype for the Cherenkov Telescope Array **Authors:** Jakub Jurysek, Imen Al Samarai, Cyril Alispach, Matteo Balbo,, Anastasia Maria Barbano, Vasyl Beshley, Adrian Biland, Jiri Blazek, Jacek, B{\l}ocki, Jerzy Borkowski, Tomek Bulik, Frank Raphael Cadoux, Ladislav, Chytka, Victor Coco, Nicolas De Angelis, Domenico Della Volpe, Yannick Favre,, Tomasz Gieras, Mira Grudzi\'nska, Petr Hamal, Mathieu Heller, Miroslav, Hrabovsky, Jerzy Kasperek, Katarzyna Koncewicz, Andrzej Kotarba, Etienne, Lyard, Emil Mach, Dusan Mandat, Stanislav Michal, Jerzy Michalowski, Rafal, Moderski, Teresa Montaruli, Andrii Nagai, Dominik Neise, Jacek Niemiec,, Theodore Rodrigue Njoh Ekoume, Michal Ostrowski, Miroslav Palatka, Pawel, Pasko, Miroslav Pech, Bart{\l}omiej Pilszyk, Henry Przybilski, Pawel Rajda,, Yves Renier, Pawe{\l} Rozwadowski, Petr Schovanek, K. Seweryn, Vitalii, Sliusar, Dorota Smakulska, Dorota Sobczy\'nska, {\L}ukasz Stawarz, Jacek, \'Swierblewski, Pawe{\l} \'Swierk, Petr Travnicek, Isaac Troyano Pujadas,, Roland Walter, Marek Wiecek, Aleksander Zagda\'nski, Krzystof Zietara (for, the CTA consortium) arXiv: 1907.08061 · 2019-07-19 ## TL;DR This paper validates a Monte Carlo model of the SST-1M prototype telescope for the Cherenkov Array, focusing on gamma/hadron separation and energy/direction reconstruction using machine learning. ## Contribution It presents the first validation of the Monte Carlo model of the SST-1M prototype and assesses its expected performance in real observing conditions. ## Key findings - Monte Carlo model accurately predicts telescope performance. - Machine learning improves gamma/hadron separation. - Expected energy and direction reconstruction performance is quantified. ## Abstract The SST-1M telescope was developed as a prototype of a Small-Size-Telescope for the Cherenkov Telescope Array observatory and it has been extensively tested in Krakow since 2017. In this contribution we present validation of the Monte Carlo model of the prototype and expected performance in Krakow conditions. We focus on gamma/hadron separation and mono reconstruction of energy and gamma photon arrival direction using Machine learning methods. ## Full text _Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/1907.08061/full.md ## Figures 9 figures with captions in the complete paper: https://tomesphere.com/paper/1907.08061/full.md ## References 13 references — full list in the complete paper: https://tomesphere.com/paper/1907.08061/full.md --- Source: https://tomesphere.com/paper/1907.08061