First application of the optimised next neighbour cleaning to data from imaging atmospheric Cherenkov telescopes

Imaging atmospheric Cherenkov telescopes (IACTs) cameras are constantly subject to signals from the night sky background and electronic noise during event recordings. Image cleaning methods are typically employed to eliminate pixels that are mostly noise-contaminated and lack sufficient Cherenkov light. In this paper, we present the implementation of the optimised next neighbour (ONN) technique to the Very Energetic Radiation Imaging Telescope Array System (VERITAS) reconstruction pipeline. With this method, cleaning thresholds are derived in a two-parameter space, composed of the minimum total integrated charge of a cluster of pixels, q_̊m min, and by the maximum difference in the pulse arrival time within the cluster, δ_t. The cleaning contours in q_ ̊m min - δ_t are adjusted to set a high probability of noise suppression while optimising the retention of pixels containing Cherenkov light. In this paper, we investigate the impact of different levels of image noise contamination on the reconstruction efficiency. A maximum probability of noise cluster retention of f_ acc = 5 was found to provide the optimal balance between noise suppression, retention of Cherenkov signals, and preservation of image morphologies. In comparison to the VERITAS double-pass image cleaning method, effective areas are increased at the lower energy range (∼ 60 - 90 GeV) by a factor of up to ∼ 25. Energy thresholds are reduced by up to ∼ 22%. Testing the method on Crab Nebula data resulted in an increased number of reconstructed excess events at energies < 1 TeV by a factor of up to ∼ 82. Finally, an increase in the differential sensitivity of VERITAS by a factor of up to ∼ 8 was observed at ∼ 20 GeV. With the addition of BDT-based γ/hadron separation cuts, the method improves the detection significance of a 20 hour Crab Nebula dataset, while excess event rates are enhanced by ∼ 16%. Comparisons between Monte Carlo simulations and the data do not indicate any additional systematic effects. In summary, the ONN image cleaning provides a reduced energy threshold and a higher rate of event reconstruction. The technique is especially advantageous for the analysis of gamma-ray sources presenting transient and soft spectra. 10^ -4