The GRAPES-3 experiment located in Ooty consists of an array of 400 plastic scintillator detectors spread over an area of 25,000 m2 and a large area (560 m2 ) muon telescope. Everyday, the array records about 3 million showers induced by the interaction of primary cosmic rays in the atmosphere. One of the primary objectives of the experiment is to measure the energy spectrum and composition of the cosmic rays in the TeV-PeV energy range. Some of the detected showers have cores outside the array. This fraction increases with energy due to the higher lateral spread of shower particles at higher energies. Identifying these events is thus crucial for accurate measurement of the cosmic ray energy spectrum. This talk will describe simple cut based as well as machine learning based strategies for identifying and excluding such events and their impact on the cosmic ray energy spectrum as measured by Bayesian unfolding technique.
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