DUNE UK Production Project
Lead Research Organisation:
University of Cambridge
Department Name: Physics
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Planned Impact
Please refer to lead submission of Oxford.
Organisations
Publications
Tingey J
(2023)
Neutrino characterisation using convolutional neural networks in CHIPS water Cherenkov detectors
in Journal of Instrumentation
Abed Abud A
(2023)
Highly-parallelized simulation of a pixelated LArTPC on a GPU
in Journal of Instrumentation
Abud A
(2023)
Identification and reconstruction of low-energy electrons in the ProtoDUNE-SP detector
in Physical Review D
Abed Abud A
(2023)
Impact of cross-section uncertainties on supernova neutrino spectral parameter fitting in the Deep Underground Neutrino Experiment
in Physical Review D
Abud A
(2023)
Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora
in The European Physical Journal C
Abed Abud A
(2022)
Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network
in The European Physical Journal C
Aurisano A
(2022)
Artificial Intelligence for High Energy Physics
Abud AA
(2022)
Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC.
in The European physical journal. C, Particles and fields
Chappell A
(2022)
Application of transfer learning to neutrino interaction classification
in The European Physical Journal C