Journal Article

PUBDB-2022-00501

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array

Spencer, S. (Corresponding author) ; Armstrong, T. ; Watson, J. J.DESY* ; Mangano, S. ; Renier, Y. ; Cotter, G.

2021
Elsevier Science Amsterdam [u.a.]

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Please use a persistent id in citations: doi:10.1016/j.astropartphys.2021.102579  doi:10.3204/PUBDB-2022-00501

Report No.: arXiv:2103.06054

Abstract: New deep learning techniques present promising new analysis methods for Imaging Atmospheric Cherenkov Telescopes (IACTs) such as the upcoming Cherenkov Telescope Array (CTA). In particular, the use of Convolutional Neural Networks (CNNs) could provide a direct event classification method that uses the entire information contained within the Cherenkov shower image, bypassing the need to Hillas parameterise the image and allowing fast processing of the data.Existing work in this field has utilised images of the integrated charge from IACT camera photomultipliers, however the majority of current and upcoming generation IACT cameras have the capacity to read out the entire photosensor waveform following a trigger. As the arrival times of Cherenkov photons from Extensive Air Showers (EAS) at the camera plane are dependent upon the altitude of their emission and the impact distance from the telescope, these waveforms contain information potentially useful for IACT event classification.In this test-of-concept simulation study, we investigate the potential for using these camera pixel waveforms with new deep learning techniques as a background rejection method, against both proton and electron induced EAS. We find that a means of utilising their information is to create a set of seven additional 2-dimensional pixel maps of waveform parameters, to be fed into the machine learning algorithm along with the integrated charge image. Whilst we ultimately find that the only classification power against electrons is based upon event direction, methods based upon timing information appear to out-perform similar charge based methods for gamma/hadron separation. We also review existing methods of event classifications using a combination of deep learning and timing information in other astroparticle physics experiments.

Keyword(s): showers: atmosphere ; radiation: Cherenkov ; Cherenkov Telescope Array ; background ; electron ; pixel ; Cherenkov counter ; photomultiplier ; neural network ; dimension: 2 ; trigger ; imaging ; photon ; hadron ; air ; CTA ; CHEC ; Deep learning ; IACT ; Background rejection

Note: Full paper accepted in Astroparticle Physics. 39 Pages with 11 Figures. Minimal code to reproduce results in the paper available at: https://github.com/STSpencer/wavelearn_release. Some early results previously presented at ICRC2019 (doi:10.22323/1.358.0798)

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Contributing Institute(s):

1. Cherenkov Telescope Array (Z_CTA)

Research Program(s):

1. 613 - Matter and Radiation from the Universe (POF4-613) (POF4-613)

Experiment(s):

1. Cherenkov Telescope Array

Appears in the scientific report 2021

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Database coverage:
; ; Clarivate Analytics Master Journal List ; Current Contents - Physical, Chemical and Earth Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF < 5 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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