PyMAP: Python-Based Data Analysis Package with a New Image Cleaning Method to Enhance the Sensitivity of MACE Telescope

Observations of Very High Energy (VHE) gamma ray sources using the ground-based Imaging Atmospheric Cherenkov Telescopes (IACTs) play a pivotal role in understanding the non-thermal energetic phenomena and acceleration processes under extreme astrophysical conditions. However, detection of the VHE gamma ray signal from the astrophysical sources is very challenging, as these telescopes detect the photons indirectly by measuring the flash of Cherenkov light from the Extensive Air Showers (EAS) initiated by the cosmic gamma rays in the Earth’s atmosphere. This requires fast detection systems, along with advanced data acquisition and analysis techniques to measure the development of extensive air showers and the subsequent segregation of gamma ray events from the huge cosmic ray background, followed by the physics analysis of the signal. Here, we report the development of a python-based package for analyzing the data from the Major Atmospheric Cherenkov Experiment (MACE), which is operational at Hanle in India. The Python-based MACE data Analysis Package (PyMAP) analyzes data by using advanced methods and machine learning algorithms. Data recorded by the MACE telescope are passed through different utilities developed in the PyMAP to extract the gamma ray signal from a given source direction. We also propose a new image cleaning method called DIOS (Denoising Image of Shower) and compare its performance with the standard image cleaning method. The working performance of DIOS indicates an advantage over the standard method with an improvement of ≈25% in the sensitivity of MACE.