Speaker
Lakshmi Murgod
(Central University of karnataka)
Description
Measurement of neutrino mixing parameters using a magnetized iron calorimeter (ICAL) is the primary goal of INO. Most of INO-ICAL related analysis, using prototype detector data and simulations, are currently based on conventional algorithms. In the recent years, AI-based analysis have shown impressive performance in many high-energy physics experiments. In this talk, we present an overview of machine learning algorithms that we have developed for a few analysis related to INO-ICAL and prototype detectors. The studies include directionality and charge identification, energy reconstruction of cosmic muons in mICAL, prediction of muon multiplicity, and search for new event topologies.
| Session | Neutrino Physics |
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Author
Lakshmi Murgod
(Central University of karnataka)
Co-author
Dr
Deepak Samuel
(Central University of karnataka)
