Machine learning-based prediction of multi-muon events in the INO-ICAL prototype stack

Abstract

The upcoming India-based Neutrino Observatory (INO) will host a 50 kton magnetized Iron Calorimeter (ICAL) to study atmospheric neutrinos. As part of its proposal, small-scale prototype detectors have been built and are in operation. The primary focus in these prototypes has been on detector characterization studies. At the same time, few physics analyses were also carried out with the cosmic muon data collected. However, due to the small size of the detectors, such analyses always relied on the assumption that the tracks were of single muons only. Consequently, multi-muon events were discarded as noisy events, reducing the physics potential. In this work, we report the development of a machine learning model to predict multi-muon events, study its efficiency and report the muon multiplicity distribution observed using cosmic muon events from the prototype detector.