Neutrino Physics and Machine Learning (NPML 2025)

Session

Experiments - SK/HK

27 Oct 2025, 09:30

Koshiba Hall (University of Tokyo)

53. Overview of reconstruction methods in Super and Hyper-Kamiokande

Benjamin Quilain

27/10/2025, 09:30

Long talk (25min. + 10min. Q/A)

The detectors of the Kamiokande saga relies on a well-established detection method using Cherenkov effect in ultra-pure water. Consequently, the reconstruction algorithms, especially of the Super-Kamiokande detector, have been developed since several decades, allowing to reach unparallel results in neutrino sector, from the constraints on the solar neutrino upturn to the atmospheric neutrino...

15. Graph Neural Networks for Hyper K Reconstruction

Erwan Le Blévec

27/10/2025, 10:05

Short talk (15min. + 5 min. Q/A)

"The Hyper-Kamiokande Detector represents the next generation of neutrino observatories, following in the lineage of the Kamiokande and Super-Kamiokande experiments. With significantly enhanced sensitivity, Hyper-Kamiokande will support a diverse and ambitious physics program, including searches for proton decay, studies of solar neutrinos under non-standard scenarios, and the potential first...

26. Improving Event Reconstruction in Hyper-Kamiokande with ResNet

Andrew Atta

27/10/2025, 10:45

Short talk (15min. + 5 min. Q/A)

A key goal of neutrino physics is to measure CP violation in neutrino oscillations, which may explain why the universe is dominated by matter over antimatter. The upcoming Hyper-Kamiokande experiment will be central to this effort. With a much larger detector volume and more photomultipliers than Super-K, Hyper-K will collect data at kilohertz rates. Traditional reconstruction tools are...

25. Vision Transformers for event reconstruction in water Cherenkov detector

Shuoyu Chen

27/10/2025, 11:05

Short talk (15min. + 5 min. Q/A)

Hyper-Kamiokande (Hyper-K) is a next-generation water Cherenkov neutrino experiment currently under construction, designed to address key questions in particle physics, including leptonic CP violation and proton decay. Convolutional neural networks (CNNs) have previously been applied to water Cherenkov detectors by treating PMTs as pixels, with charge and timing information serving as input...

54. The Water Cherenkov Test Experiment as a Demonstrator of Machine Learning Techniques for Neutrino Experiments

Nick Prouse

27/10/2025, 13:30

Short talk (15min. + 5 min. Q/A)

The Water Cherenkov Test Experiment (WCTE) is a 30-ton water Cherenkov detector that received 100-1200 MeV electrons, muons, charged pions and protons from the CERN East Area T9 beam, as well as observing secondary neutrons captured on Gadolinium and tagged photons through operation in a dedicated setup. With a suite of beamline detectors to characterise and tag the particles entering the...

23. WCTE Event Reconstruction with Graph Neural Networks

Mathieu Ferey

27/10/2025, 13:50

Short talk (15min. + 5 min. Q/A)

The Water Cherenkov Test Experiment is a powerful detector for studying water Cherenkov detectors, demonstrating new detection technologies testing reconstruction algorithms with controlled data. In particular, the relatively small scale of the detector allows for a fast and lightweight exploration of machine learning methods. We will present here the performances of Graph Neural Networks for...

17. Simulation, calibration and reconstruction in water Cherenkov detectors with machine learning techniques

Ka Ming Tsui

27/10/2025, 14:10

Short talk (15min. + 5 min. Q/A)

Traditional Monte Carlo (MC) simulations rely on accurate modeling of both physics processes and detector responses. However, optimization with calibration data is often challenging,or even impractical, due to stochastic effects and the high dimensionality of correlated parameters.

Within the CIDeR-ML Collaboration, we are developing differentiable surrogate models that enable automated...