  Cherenkov gamma telescope observes high energy gamma rays, taking advantage
of the radiation emitted by charged particles produced inside the
electromagnetic showers initiated by the gammas, and developing in the
atmosphere. The detector records and allows for the reconstruction of the
shower parameters. The reconstruction of the parameter values was achieved
using a Monte Carlo simulation algorithm called CORSIKA. The present study
developed multiple machine-learning-based classification models and evaluated
their performance. Different data transformation and feature extraction
techniques were applied to the dataset to assess the impact on two separate
performance metrics. The results of the proposed application reveal that the
different data transformations did not significantly impact (p = 0.3165) the
performance of the models. A pairwise comparison indicates that the performance
from each transformed data was not significantly different from the performance
of the raw data. Additionally, the SVM algorithm produced the highest
performance score on the standardized dataset. In conclusion, this study
suggests that high-energy gamma particles can be predicted with sufficient
accuracy using SVM on a standardized dataset than the other algorithms with the
various data transformations.
