Deep learning with photosensor timing information as a background rejection method for the Cherenkov Telescope Array

TL;DR

This study explores using deep learning on Cherenkov telescope waveform data, including timing information, to improve background rejection in gamma-ray astronomy, showing timing-based methods outperform charge-only approaches.

Contribution

The paper introduces a novel approach combining waveform timing parameters with CNNs for event classification in IACTs, demonstrating improved gamma/hadron separation.

Findings

Timing information enhances classification performance over charge-only methods.

Event direction remains the primary classifier for electrons.

Timing-based methods outperform charge-based methods in gamma/hadron separation.

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.