244 citations · 352 across the 11 of their papers we have counts for
12 papers
The NOvA Test Beam Experiment
NOvA Collaboration, S. Abubakar, M. A. Acero +208
NOvA is a long-baseline neutrino oscillation experiment designed to study the neutrino mixing parameters, mass ordering, and CP violation in the lepton sector. A key component of t…
Neutron detector response modeling in NOvA
NOvA Collaboration, S. Abubakar, M. A. Acero +194
Neutrons can present a significant challenge for neutrino experiments in which energy reconstruction is critical. With the ability to escape detection completely and with a weak co…
Charge readout electronics for the DUNE horizontal drift far detector: design and performance in ProtoDUNE-HD
DUNE Collaboration, S. Abbaslu, F. Abd Alrahman +1364
DUNE (Deep Underground Neutrino Experiment) is a long-baseline neutrino oscillation experiment currently under construction, whose far detectors will be the largest liquid argon ti…
Submanifold Sparse Convolutional Networks for Automated 3D Segmentation of Kidneys and Kidney Tumours in Computed Tomography
Saúl Alonso-Monsalve, Leigh H. Whitehead, Adam Aurisano +1
Accurate delineation of kidney tumours in Computed Tomography (CT) is essential for downstream quantitative analysis and precision oncology, but manual segmentation is a specialise…
Graph Neural Network for Neutrino Physics Event Reconstruction
V Hewes, Adam Aurisano, Giuseppe Cerati +6
Liquid Argon Time Projection Chamber (LArTPC) detector technology offers a wealth of high-resolution information on particle interactions, and leveraging that information to its fu…
Automated Segmentation of Computed Tomography Images with Submanifold Sparse Convolutional Networks
Saúl Alonso-Monsalve, Leigh H. Whitehead, Adam Aurisano +1
Quantitative cancer image analysis relies on the accurate delineation of tumours, a very specialised and time-consuming task. For this reason, methods for automated segmentation of…
End-to-end analysis using image classification
Adam Aurisano, Leigh H. Whitehead
End-to-end analyses of data from high-energy physics experiments using machine and deep learning techniques have emerged in recent years. These analyses use deep learning algorithm…
Accelerating the Inference of the Exa.TrkX Pipeline
Alina Lazar, Xiangyang Ju, Daniel Murnane +21
Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.Tr…
Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume II: DUNE Physics
B. Abi, R. Acciarri, Mario A. Acero +958
The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventuall…
Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume III: DUNE Far Detector Technical Coordination
B. Abi, R. Acciarri, Mario A. Acero +958
The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventuall…
Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume I: Introduction to DUNE
B. Abi, R. Acciarri, Mario A. Acero +958
The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventuall…
A Convolutional Neural Network Neutrino Event Classifier
A. Aurisano, A. Radovic, D. Rocco +7
Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an applicati…