Neutrino Physics and Machine Learning (NPML)

Session

Day 5 Morning

24 Jul 2020, 10:00

Conveners

Day 5 Morning

• Kazuhiro Terao (SLAC)
• Taritree Wongjirad (Tufts University)

22. Machine Learning based reconstruction for Hyper Kamiokande

Lukas Berns (Tokyo Institute of Technology)

24/07/2020, 10:00

A collaboration/project summary talk

We present initial developments of ML based event reconstruction for water Cherenkov detectors in the context of Hyper Kamiokande. Using ML, we aim to exploit additional spatial and directional information from higher granularity PMTs developed for HyperK to improve on existing reconstruction performance and to enable new measurements that are very challenging in conventional...

39. Inverse Beta Decay Reconstruction in Super-Kamiokande with CNNs

Alexander Goldsack (University of Oxford/Kavli IPMU)

24/07/2020, 10:40

Individual talk

Inverse beta decay is the primary interaction mode for low energy electron anti-neutrinos, producing two signals in a water Cherenkov detector like Super-Kamiokande: a low energy positron and, ~200 µs later, a neutron capture on hydrogen producing a 2.2 MeV photon. These result in only ~10 of SK’s 11,000+ photomultiplier tubes being hit by light, making them difficult to differentiate from...

20. ML Methods Investigation for Hyper-K Neutron Capture Classification

Matthew Stubbs (University of Winnipeg)

24/07/2020, 11:05

Individual talk

Hyper-Kamiokande is the proposed next generation Water Cherenkov neutrino detector in Kamioka, Japan. Based on the design of Super-Kamiokande, Hyper-K will have an order of magnitude larger fiducial mass, enabling the survey of topics in neutrino physics on a broader scale. The intermediate Water Cherenkov detector (IWCD) near the J-PARC beam in Tokai aims at reducing systemic uncertainty in...

21. A Generative Neural Network for Water Cherenkov Reconstruction

Cristovao Vilela (Stony Brook University)

24/07/2020, 11:30

Individual talk

Deep neural networks are an area of very active research in neutrino event reconstruction. On the other hand, state-of-the-art reconstruction methods for water Cherenkov detectors use more traditional maximum-likelihood approaches. Here we present initial studies for a convolutional neural network that generates probability density functions for the data (hit charge and time) observed at each...