Publications
Below is the list of publications lead by our group members, followed by a link to the full lists of publications in experiments our group participate.
Physics
- Search for heavy neutral leptons decaying into muon-pion pairs in the MicroBooNE detector=
- P. Abratenko and others (MicroBooNE collaboration), Phys. Rev. D 101, 052001 (2020)
- Prospects for detecting boosted fark matter in DUNE through hadronic interactions
- J. Berger, Y. Cui, M. Graham, L. Necib, G. Petrillo, D. Stocks, Y.-T. Tsai and Y. Zhao, arXiv 1912.05558, (2019)
Detector R&D
- Design and performance of a 35-ton liquid argon time projection chamber as a prototype for future very large detectors
- D.L. Adams and others (DUNE 35-ton collaboration), JINST 15, P03035 (2020)
- A New Concept for Kilotonne Scale Liquid Argon Time Projection Chambers
- M. Auger and others, arXiv 1908.10956 (2019)
- Photon detector system timing performance in the DUNE 35-ton prototype liquid argon time projection chamber
- D.L. Adams and others (DUNE 35-ton collaboration), JINST 13, P06022 (2018)
Machine Learning
- Scalable, Proposal-free Instance Segmentation Network for 3D Pixel Clustering and Particle Trajectory Reconstruction in Liquid Argon Time Projection Chambers
- D.H. Koh and others (DeepLearnPhysics collaboration), arXiv 2007.03083 (2020)
- Clustering of Electromagnetic Showers and Particle Interactions with Graph Neural Networks in Liquid Argon Time Projection Chambers Data
- F. Drielsma and others (DeepLearnPhysics collaboration), arXiv 2007.01335 (2020)
- Point Proposal Network for Reconstructing 3D Particle Endpoints with Sub-Pixel Precision in Liquid Argon Time Projection Chambers
- L. Domine and others (DeepLearnPhysics collaboration), arXiv 2006.14745 (2020)
- PILArNet: Public Dataset for Particle Imaging Liquid Argon Detectors in High Energy Physics
- C. Adams, K. Terao, and T. Wongjirad (DeepLearnPhysics collaboration), arXiv 2006.01993 (2020)
- Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data
- L. Domine and K. Terao (DeepLearnPhysics collaboration), Phys. Rev. D 102 012005 (2020)
- Deep neural network for pixel-level electromagnetic particle identification in the MicroBooNE liquid argon time projection chamber
- C. Adams et al., Phys. Rev. D, 99, 092001 (2019)
- Machine learning at the energy and intensity frontiers of particle physics
- A. Radovic, M. Williams, D. Rousseau, M. Kagan, D. Bonacorsi, A. Himmel, A. Aurisano, K. Terao, and T. Wongjirad, Nature 560 41–48 (2018)
- Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber
- R. Acciarri and others (MicroBooNE collaboration), JINST 12, P03011 (2017)
