Research Report
ICRR Inter-University Research Program 2021
Research Subject: New Photogrammetry Calibration and Machine Learning (ML)Event Reconstruction for Super-Kamiokande and Hyper-Kamiokande
Principal Investigator:

Patrick de Perio (TRIUMF)

Participating Researchers:

Nick Prouse (TRIUMF)
Xiaoyue Li (TRIUMF)
Rhea Gaur (TRIUMF/UBC)
Barry Pointon (BCIT)
Alejandro Yankelevich (UCI)
Joanna Gao (KCL)

Summary of Research Result：
There has been significant progress in the water Cherenkov machine learning(WatChMaL) consortium in implementing new models and architectures, validation,and benchmarking to existing traditional reconstruction algorithms, and application
to physics analyses:
1. The ResNet classification model was applied to the Hyper-Kamiokande (HK)Intermediate Water Cherenkov Detector (IWCD) beam neutrino Monte Carlo(MC) and showed superior electron/muon discrimination and hints ofelectron/gamma separation, which continues to be investigated and improved.
2. The data pre-processing pipeline for the HK far detector has been developed andPointNet is now being tested with electron/muon/π0/gamma particle guns in thehybrid (50-cm PMT & multi-PMT) detector geometry.
3. The data pre-processing pipeline for Super-Kamiokande (SK) have been written
for the low energy (LowE) and high energy (HE) analyses.
4. A first application of the ResNet (and a new boosted decision tree) to the LowE
solar neutrino analysis shows potential for significantly higher backgroundrejection compared to the traditional selection criteria.
5. The spherical convolution neural network (HEALPix fitter) shows significant
improvement in supernova (SN) direction reconstruction time (~seconds) for theSK SNWatch program.
6. The Graph Neural Net (GNN) project has completed an initial result showing
neutron/electron discrimination in IWCD and a publication is near submission.

7. A first implementation of panoptic segmentation algorithms was completed,showing the ability to separate rings in neutral pion events in IWCD, towards
improving multi-ring and multi-vertex (pile-up) reconstruction.
This will be the final report on ML, as the next years’ program will focus onphotogrammetry only, which used all the funding in previous years.
Precious photogrammetry data was taken during the last SK tank open work in
February 2020. This was a rare opportunity granted after a synergistic request to use
an underwater remote operated vehicle to monitor critical water system upgrades forSK-Gd. An initial version of PMT feature detection using traditional image processingand ML semantic segmentation have been developed. These features were thenlabeled with a custom semi-automatic algorithm specific to the SK super-module
structure to ensure the correct PMT is identified in each photo. Geometry
reconstruction code was then applied to a set of photos from the ROV traversing
horizontally around the detector barrel. This resulted in the first underwaterreconstruction of PMT positions around a small ring (5-6 rows) of SK, showing nodeviation from the design geometry within current measurement uncertainties,
which contribute to a spread in deviation from design of 1.3 (4.2) cm in the tangential(radial) direction. Work is ongoing to automate this process for the entire detector,reduce the uncertainties, and apply the results to the SK detector simulation.
In parallel, the design, procurement, and production of the fixed and deployed camera
systems for HK, IWCD, and its prototype Water Cherenkov Test Experiment (WCTE)
at CERN, is progressing on schedule. A prototype of the underwater housing for theconsumer-grade camera has been produced and is being testing now with new opticalcalibration hardware and procedures. An underwater red lamp was purchased withthis grant to test its feasibility with online PMTs. Integration designs for the WCTE
and its calibration deployment system are ongoing. The experience, analysis andsimulation software developed from the SK photogrammetry campaign have been
extremely beneficial to this work.
There have been no publications this fiscal year as both machine learning andphotogrammetry work are still in the research and development stage that require
increasing amounts of analysis, scrutiny, and approval from both the Hyper-K andSuper-K collaborations to publicize results. However, ~8 conference and workshop
presentations were given on the photogrammetry process and status.
No. A03