Speaker
Description
Precise measurements of the nuclear composition and energy spectrum of primary cosmic rays at the knee and beyond are crucial for understanding their astrophysical origin, acceleration mechanisms, and interactions with the interstellar medium. The GRAPES-3 experiment, located in Ooty, India, consists of a densely packed array of scintillator detectors and large area tracking muon detector designed to measure cosmic rays in the energy range from a few TeV to over 10 PeV, providing significant overlap with direct detection experiments. Recently, GRAPES-3 reported a spectral hardening in the cosmic-ray proton spectrum at approximately 165 TeV using the muon multiplicity distribution, a composition-sensitive parameter derived from extensive air shower observations. Building upon this, we have employed machine learning techniques, including Boosted Decision Trees using the XGBoost classifier and Deep Neural Networks, to classify primary cosmic-ray nuclei into proton, helium, and heavier elements. This approach utilizes key shower parameters such as the shower age, muon multiplicity distribution, and other low-level features. These classification results will further be used to reconstruct the energy spectra of individual mass groups. The latest findings and their implications for cosmic-ray composition studies will be presented at the conference.
| Collaboration(s) | GRAPES-3 |
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