Neutrino Physics and Machine Learning 2026

Session

Ecosystem: Public Datasets and Open Data Challenges

18 Jun 2026, 14:00

The Interdisciplinary Science and Engineering Building (UC Irvine)

44. MicroBooNE Open Datasets

David Caratelli (UC Santa Barbara)

18/06/2026, 14:00

Datasets

Public Datasets and Challenges

Public datasets from high energy physics experiments can help spur the development of new analysis methods and techniques. This is particularly true in the Liquid Argon Time Projection Chamber (LArTPC) community, where many detectors sharing the same technology have been operating concurrently at Fermilab and CERN, enhancing the benefit of shared, broadly-available datasets for...

37. .The Ghost Hunter Open Data Challeng for ML-driven PMT Waveform Analysis and Event Reconstruction

Benda Xu (Tsinghua University)

18/06/2026, 14:25

Datasets

Public Datasets and Challenges

Next-generation large-scale liquid scintillator detectors (JUNO, KamLAND2 , SNO+ and JNE) rely on tens of thousands of Photomultiplier Tubes (PMTs) to capture single optical photons. Pushing the energy resolution to the physical limit requires extracting sub-nanosecond timing and precise charge from highly piled-up and noisy PMT waveforms. However, the lack of standardized, publicly accessible...

42. Surrogate Model Design for Neutrino Public Datasets

Linyan Wan (Fermilab)

18/06/2026, 14:50

Datasets

Public Datasets and Challenges

Neutrino public datasets are increasingly important for advancing neutrino physics by broadening participation, enabling independent cross-checks, and supporting method development beyond large experimental collaborations. However, their scientific utility is limited when users lack access to detector simulation or the detailed detector knowledge needed to interpret reconstructed quantities....

34. Introducing WAND: a Water-cherenkov Annotated Neutrino Dataset

César Jesús Valls (CERN)

18/06/2026, 15:30

Datasets

Public Datasets and Challenges

Machine learning is increasingly shaping analysis strategies in high-energy physics, a field characterised by large data volumes and complex detector responses. Progress in this direction depends on accessible, well-documented datasets that enable method development and provide common benchmarks for comparison.
We present WAND, a public dataset of simulated events in a water Cherenkov...

61. DORAEMON: Building Neutrino AI R&D Ecosystem Through Open Data Challegne and Common Benchmarks

Kazuhiro Terao (SLAC)

18/06/2026, 15:55