Research Report
ICRR Inter-University Research Program 2020
Research Subject: New Photogrammetry Calibration and Machine Learning (ML) Event
Reconstruction for Super-Kamiokande and Hyper-Kamiokande
Principal Investigator: Patrick de Perio
Participating Researchers: Nick Prouse

Summary of Research Result：We have continued to optimize the water Cherenkov
machine learning (WatChMaL) platform to improve performance, facilitate new modelsand architectures, and produce exploratory data analyses and benchmarking to theexisting traditional reconstruction algorithm (fiTQun). Comparable or better
performance was demonstrated in electron/muon classification, as well as non-negligible
power in separating electron/gamma in the IWCD. Application to Super-Kamiokande(SK) and Hyper-K Intermediate Water Cherenkov Detector (IWCD) neutrino Monte
Carlos (MCs) has begun, for eventual adoption to official analyses. A first version of theGenerative Adversarial Network (GAN) was trained to be able to mimic true simulated
events, towards the mitigation of data/MC mis-modeling. Several Graph Neural Nets(GNNs) and multi-layer perceptrons (MLPs) were implemented to tackle
neutron/electron/noise discrimination, to be tested on real SK data and compared toexisting MLP and NN techniques. A new spherical convolution NN has been tested on SKsupernova (SN) simulations to speed-up SN direction finding for the global SN detection
effort, with optimization and deployment in progress. Research and development on
panoptic segmentation algorithms has begun for improving multi-ring reconstruction andmulti-vertex (pile-up) reconstruction in Hyper-K’s IWCD.
Photogrammetry data was taken during the last SK tank open work in February 2020, anextremely rare opportunity granted by the request to monitor critical Water Teamupgrades for SK-Gd. For this campaign, an underwater remote operated vehicle (ROV) ordrone was purchased, as well as equipment for backup GoPro cameras, with last year’sICRR grant. The drone’s camera and backup cameras were calibrated underwater in apool for precise undistortion of the photos in analysis later. The PI and postdoc (NickProuse) were trained to safely pilot the drone in the pool to prepare for deployment inSK. After the successful drone monitoring of the Water Team upgrades, over 12000
photos of the inside of the SK detector were taken. A preliminary photogrammetry
analysis was able to reconstruct the PMT positions in a small section of the detector,
precisely reproducing expected features such as the distance and angle between PMTs,
as well as between the mounting bolts for each PMT. A simulation of thephotogrammetry system/analysis was also developed to be able to compare to the real
analysis and estimate systematic errors. The analysis is being improved to automate thefeature recognition procedure and be able to apply to the entire photo set to precisely
reconstruct the geometry of the whole SK detector. In parallel, work is ongoing to designand optimize the fixed camera systems for the IWCD, its prototype Water Cherenkov TestExperiment (WCTE), and the Hyper-K far detector, significantly benefiting from theabove experience and developments in SK.
About 40% of this year’s grant was spent on 1) shipping the above photogrammetry

equipment to Canada and 2) purchasing new cameras for a HK PMT support structure
mockup test in Kamioka. For 1), we are planning to continue calibrating the cameras,
especially as a function of pressure (which can distort the optics) to improve theprecision of the analysis and resulting measurements in SK. For 2), we are currently
synergizing with the PMT and water group to understand water flow and temperature
variations around the PMTs. Eventually, we will then use the mockup to test
photogrammetry performance with different cameras and lighting systems,
photographing various targets on the support structure to inform the design of IWCD,
WCTE, and Hyper-K. The remaining funds have been requested for carry-over due to thepandemic travel restrictions.
There have been no publications this fiscal year as both machine learning andphotogrammetry work have entered deeper stages of research and development that will
require increasing amounts of analysis, scrutiny, and approval from both the Hyper-K andSuper-K collaborations to publicize results.
No. A03