Presentation description

Energy reconstruction within cosmic-ray studies is crucial. Determining the energy of a cosmic-ray air shower is essential to understanding origins of cosmic rays and their interactions. Accurate analysis of air showers is limited by the performance of reconstruction algorithms. In this contribution, we benchmark the performance of regression models for energy reconstruction using Monte-Carlo simulations, structured with features including arrival time of the signals, signal charges detected, and arrival direction of the air shower. We test four different regression models: linear, ridge, LASSO, and polynomial. The measured reconstruction accuracy is quantified by measuring the correlation coefficients and mean squared error of true and reconstructed energies. We will demonstrate that polynomial regression shows optimal performance, having a correlation coefficient of 0.981 and mean squared error of 0.013.