Prospects for the Use of Photosensor Timing Information with Machine Learning Techniques in Background Rejection.

Abstract

Recent developments in machine learning (ML) techniques present a promising new analysis method for high-speed imaging in astroparticle physics experiments, for example with imaging atmospheric Cherenkov telescopes (IACTs). In particular, the use of timing information with new machine learning techniques provides a novel method for event classification. Previous work in this field has utilised images of the integrated charge from IACT camera photomultipliers, but the majority of current and upcoming 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, these waveforms contain information useful for IACT event classification. In this work, we investigate the potential for using these waveforms with ML techniques, and find that a highly effective means of utilising their information is to create a set of seven additional two dimensional histograms of waveform parameters to be fed into the machine learning algorithm along with the integrated charge image. This appears to be superior to using only these new ML techniques with the waveform integrated charge alone. We also examine these timing-based ML techniques in the context of other experiments.

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

“…In section 5 we present our results and we conclude in section 6. A preliminary version of this work was presented in [28].…”

Deep learning techniques for Imaging Air Cherenkov Telescopes

“…A related issue is that a single event can show up in multiple telescopes and information from all the telescopes has to be collated and treated as a single input to the machine [34]. Additionally, with more sophisticated telescopes, one may attempt to use not only static telescopic images for each event, but also the time series wave forms for gamma-hadron separation [47,48].…”