Speaker
Description
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 detector with a geometry inspired by Super-Kamiokande. The dataset is intended for the development and evaluation of reconstruction and particle identification algorithms in Cherenkov detectors. In the presentation, we describe the dataset structure, physics configurations, labelling strategy, and simulation used in its production, as well as preliminary benchmarks and challenges.
| Contribution types | Standard talk (20min + 5min Q/A) |
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