Estimation of the excess noise factor for TACTIC photomultiplier tubes using Gaussian mixture model based machine learning algorithm

The Excess Noise Factor (ENF) of a Photomultiplier Tube (PMT) is basically the additional noise introduced because of the statistical nature of electron multiplication inside PMT. In the field of Very High Energy (VHE) gamma-ray astronomy, PMT is used as a Cherenkov photon detector. Hence, the noise of PMT plays an important role in estimating the energy of VHE gamma rays. Conventionally, estimation of ENF of PMT requires a very low-intensity, tunable light source experiment in a lab where no external light background interferes. In this study, we propose the application of a novel Machine Learning (ML) technique, the Gaussian Mixture Model (GMM) to estimate the ENF of the telescope camera for an in-situ observation. [Display omitted] •The statistical noise introduced by a PMT is known as the Excess Noise Factor (ENF).•GMM is used to decompose any distributions into a few Gaussians.•By decomposing the simulated and the actual charge spectrum, one can determine the ENF.•ENF measured by GMM agrees well with the value obtained by SPE experiment.