Mass Composition of Primary Cosmic Rays with GRAPES-3 Using Machine Learning Techniques

Precise measurements of the nuclear composition and energy spectrum of primary cosmic rays around the knee are essential to understand their origin, acceleration, and propagation. The GRAPES-3 experiment in Ooty, India, recently reported a spectral hardening in the proton spectrum at $\sim 166 \mathrm{TeV}$ using Gold’s unfolding method based on muon multiplicity distributions. To enhance composition sensitivity by incorporating additional observables, we implement a Deep Neural Network (DNN) using both muon multiplicity and shower age, along with other high-level reconstructed shower parameters. This work presents the strategy, performance, and reliability of the DNN-based approach for mass composition studies at GRAPES-3.