1.4k citations · 1.6k across the 14 of their papers we have counts for
14 papers
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…
Application of Transfer Learning to Neutrino Interaction Classification
Andrew Chappell, Leigh H. Whitehead
Training deep neural networks using simulations typically requires very large numbers of simulated events. This can be a large computational burden and a limitation in the performa…
Neutrino Characterisation using Convolutional Neural Networks in CHIPS water Cherenkov detectors
Josh Tingey, Simeon Bash, John Cesar +8
This work presents a novel approach to water Cherenkov neutrino detector event reconstruction and classification. Three forms of a Convolutional Neural Network have been trained to…
Detector and Beamline Simulation for Next-Generation High Energy Physics Experiments
Sunanda Banerjee, D. N. Brown, David N. Brown +31
The success of high energy physics programs relies heavily on accurate detector simulations and beam interaction modeling. The increasingly complex detector geometries and beam dyn…
Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors
Saúl Alonso-Monsalve, Dana Douqa, César Jesús-Valls +5
Deep learning tools are being used extensively in high energy physics and are becoming central in the reconstruction of neutrino interactions in particle detectors. In this work, w…