Speaker
Description
Water Cherenkov neutrino detectors present unique opportunities and challenges for the application of machine learning techniques. The Super Kamiokande detector is a 50 kiloton detector set up for a multitude of neutrino and non-neutrino physics, including that of detecting neutrinos from the T2K experiment 295km away. The flavour of neutrino as well as its interaction vertex and direction are all crucial observables for $\delta _{CP}$ and other searches being performed by T2K. Reconstructing these observables from PMT charge and time is well-suited to machine learning techniques, given the large multiplicity of final states, multivariate inputs and intractable size of the detector. In this talk, I will present the set up of the first application of a neural network, namely ResNet, to reconstruct neutrino interactions in the Super Kamiokande detector at the energy ranges typical of T2K. I will highlight the improvements which ResNet is capable of with respect to traditional algorithms. Furthermore, I will highlight the challenges we have encountered in applying this network to real Super Kamiokande data, the techniques we used to study data-simulation differences and our solutions to the difficulties which were encountered in applying neural networks to the data. Finally, I will end with a glimpse to the future and where ML can really shine when it comes to neutrino searches in water Cherenkov detectors.
| Contribution types | Standard talk (20min + 5min Q/A) |
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