no code implementations • 1 Feb 2021 • Francois Drielsma, Kazuhiro Terao, Laura Dominé, Dae Heun Koh
Recent inroads in Computer Vision (CV) and Machine Learning (ML) have motivated a new approach to the analysis of particle imaging detector data.
no code implementations • 6 Jul 2020 • Dae Heun Koh, Pierre Côte de Soux, Laura Dominé, François Drielsma, Ran Itay, Qing Lin, Kazuhiro Terao, Ka Vang Tsang, Tracy Usher
This work contributes to the development of an end-to-end optimizable full data reconstruction chain for LArTPCs, in particular pixel-based 3D imaging detectors including the near detector of the Deep Underground Neutrino Experiment.
no code implementations • 2 Jul 2020 • Francois Drielsma, Qing Lin, Pierre Côte de Soux, Laura Dominé, Ran Itay, Dae Heun Koh, Bradley J. Nelson, Kazuhiro Terao, Ka Vang Tsang, Tracy L. Usher
The optimized algorithm is then applied to the related task of clustering particle instances into interactions and yields a mean ARI of 99. 2 % for an interaction density of $\sim\mathcal{O}(1)\, m^{-3}$.
no code implementations • 26 Jun 2020 • Laura Dominé, Pierre Côte de Soux, François Drielsma, Dae Heun Koh, Ran Itay, Qing Lin, Kazuhiro Terao, Ka Vang Tsang, Tracy L. Usher
Using as a benchmark the PILArNet public LArTPC data sample in which the voxel resolution is 3mm/voxel, our algorithm successfully predicted 96. 8% and 97. 8% of 3D points within a distance of 3 and 10~voxels from the provided true point locations respectively.