Developing a Machine Learning Algorithm-Based Classification Models for the Detection of High-Energy Gamma Particles
This work addresses the problem of detecting high-energy gamma particles for astrophysics research, but it is incremental as it applies existing methods to a specific dataset.
The study tackled the detection of high-energy gamma particles by developing multiple machine-learning classification models and found that data transformations did not significantly impact performance, with SVM achieving the highest accuracy on a standardized dataset.
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.