The Cherenkov Telescope Array (CTA) will be the next major observatory for Very High Energy gamma-ray astronomy. Its optical throughput calibration relies on muon Cherenkov rings. This work is aimed at developing a fast and efficient muon tagger at the camera level for the CTA telescopes. A novel technique to tag muons using the capabilities of silicon photomultiplier Compact High-Energy Camera CHEC-S, one of the design options for the camera of the small size telescopes, has been developed, studying and comparing different algorithms such as circle fitting with the Taubin method, machine learning using a neural network and simple pixel counting. Their performance in terms of efficiency and computation speed was investigated using simulations with varying levels of night sky background light. The application of the best performing method to the large size telescope camera has also been studied, to improve the speed of the muon preselection.

A fast muon tagger method for imaging atmospheric cherenkov telescopes / Di Venere, L.; Giavitto, G.; Giordano, F.; Lopez-Coto, R.; Pillera, R.; for the CTA, Consortium. - In: JOURNAL OF PHYSICS. CONFERENCE SERIES. - ISSN 1742-6588. - STAMPA. - 1548:1(2020). (Intervento presentato al convegno 10th Young Researcher Meeting tenutosi a Roma, Italy nel June 18-21, 2019).

A fast muon tagger method for imaging atmospheric cherenkov telescopes

Pillera R.
Writing – Original Draft Preparation
;
2020-01-01

Abstract

The Cherenkov Telescope Array (CTA) will be the next major observatory for Very High Energy gamma-ray astronomy. Its optical throughput calibration relies on muon Cherenkov rings. This work is aimed at developing a fast and efficient muon tagger at the camera level for the CTA telescopes. A novel technique to tag muons using the capabilities of silicon photomultiplier Compact High-Energy Camera CHEC-S, one of the design options for the camera of the small size telescopes, has been developed, studying and comparing different algorithms such as circle fitting with the Taubin method, machine learning using a neural network and simple pixel counting. Their performance in terms of efficiency and computation speed was investigated using simulations with varying levels of night sky background light. The application of the best performing method to the large size telescope camera has also been studied, to improve the speed of the muon preselection.
2020
10th Young Researcher Meeting
A fast muon tagger method for imaging atmospheric cherenkov telescopes / Di Venere, L.; Giavitto, G.; Giordano, F.; Lopez-Coto, R.; Pillera, R.; for the CTA, Consortium. - In: JOURNAL OF PHYSICS. CONFERENCE SERIES. - ISSN 1742-6588. - STAMPA. - 1548:1(2020). (Intervento presentato al convegno 10th Young Researcher Meeting tenutosi a Roma, Italy nel June 18-21, 2019).
File in questo prodotto:
File Dimensione Formato  
Venere_2020_J._Phys. _Conf._Ser._1548_012036.pdf

accesso aperto

Tipologia: Versione editoriale
Licenza: Creative commons
Dimensione 1.61 MB
Formato Adobe PDF
1.61 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/211235
Citazioni
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact