Application of the optimised next neighbour image cleaning method to the VERITAS array

TL;DR

This paper presents an optimized image cleaning method for the VERITAS array that adapts to varying noise conditions, improving low-energy gamma-ray detection and lowering the energy threshold.

Contribution

The paper introduces an adaptive next neighbour image cleaning technique that accounts for individual observation noise, enhancing gamma-ray reconstruction at lower energies.

Findings

Increased gamma-ray detection rate

Lowered energy threshold to 70 GeV

Improved reconstruction of low-energy events

Abstract

Imaging atmospheric Cherenkov telescopes, such as the VERITAS array, are subject to the Night Sky Background (NSB) and electronic noise, which contribute to the total signal of pixels in the telescope camera. The contribution of noise photons in event images is reduced with the application of image cleaning methods. Conventionally, high thresholds must be employed to ensure the removal of pixels containing noise signal. On that account, low-energy gamma-ray showers might be suppressed during the cleaning. We present here the application of an optimised next neighbour image cleaning for the VERITAS array. With this technique, differential noise rates are estimated for each individual observation and thus changes in the NSB and afterpulsing are consistently being accounted for. We show that this method increases the overall rate of reconstructed gamma-rays, lowers the energy threshold of the array and allows the reconstruction of low energy (E > 70 GeV) source events which were suppressed by the conventional cleaning method.