pith. sign in

arxiv: 1907.08061 · v1 · pith:EKHTTC5Pnew · submitted 2019-07-18 · 🌌 astro-ph.HE · astro-ph.IM

Monte Carlo study of a single SST-1M prototype for the Cherenkov Telescope Array

Jakub Jurysek , Imen Al Samarai , Cyril Alispach , Matteo Balbo , Anastasia Maria Barbano , Vasyl Beshley , Adrian Biland , Jiri Blazek
show 51 more authors
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)
This is my paper
classification 🌌 astro-ph.HE astro-ph.IM
keywords prototypetelescopearraycarlocherenkovgammakrakowmonte
0
0 comments X p. Extension
pith:EKHTTC5P Add to your LaTeX paper What is a Pith Number? 
\usepackage{pith}
\pithnumber{EKHTTC5P}

Prints a linked pith:EKHTTC5P badge after your title and writes the identifier into PDF metadata. Compiles on arXiv with no extra files. Learn more

read the original 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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.