Publications (15)
Track Seeding and Labelling with Embedded-space Graph Neural Networks
Nicholas Choma, Daniel Murnane, Xiangyang Ju +16
To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promisi…
Reconstruction of Large Radius Tracks with the Exa.TrkX pipeline
Chun-Yi Wang, Xiangyang Ju, Shih-Chieh Hsu +22
Particle tracking is a challenging pattern recognition task at the Large Hadron Collider (LHC) and the High Luminosity-LHC. Conventional algorithms, such as those based on the Kalm…
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Xiangyang Ju, Steven Farrell, Paolo Calafiura +20
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and…
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…
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…
Cosmology and Dark Matter at the LHC
Richard Arnowitt, Adam Aurisano, Bhaskar Dutta +5
We examine the question of whether neutralinos produced at the LHC can be shown to be the particles making up the astronomically observed dark matter. If the WIMP alllowed region l…
Tau Neutrinos in the Next Decade: from GeV to EeV
Roshan Mammen Abraham, Jaime Alvarez-Muñiz, Carlos A. Argüelles +63
Tau neutrinos are the least studied particle in the Standard Model. This whitepaper discusses the current and expected upcoming status of tau neutrino physics with attention to the…
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…
Sterile neutrino search in the NOvA Far Detector
Sijith Edayath, Adam Aurisano, Alexandre Sousa +3
The majority of neutrino oscillation experiments have obtained evidence for neutrino oscillations that are compatible with the three-flavor model. Explaining anomalous results from…
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…
Machine Learning in High Energy Physics Community White Paper
Kim Albertsson, Piero Altoe, Dustin Anderson +125
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by a…
Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking
Xiangyang Ju, Daniel Murnane, Paolo Calafiura +21
The Exa.TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking. Exa.TrkX's tracking pipeline groups detecto…
NOvA Short-Baseline Tau Neutrino Appearance Search
Rijeesh Keloth, Adam Aurisano, Alexander Sousa +3
Standard three-flavor neutrino oscillations have well explained by a wide range of neutrino experiments. However, the anomalous results, such as electron-antineutrino excess seen b…
Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers
V Hewes, Adam Aurisano, Giuseppe Cerati +14
This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still…