13 citations · 69 across the 11 of their papers we have counts for
12 papers
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…
Fitting a Deep Generative Hadronization Model
Jay Chan, Xiangyang Ju, Adam Kania +3
Hadronization is a critical step in the simulation of high-energy particle and nuclear physics experiments. As there is no first principles understanding of this process, physicall…
Parton Labeling without Matching: Unveiling Emergent Labelling Capabilities in Regression Models
Shikai Qiu, Shuo Han, Xiangyang Ju +2
Parton labeling methods are widely used when reconstructing collider events with top quarks or other massive particles. State-of-the-art techniques are based on machine learning an…
Hierarchical Graph Neural Networks for Particle Track Reconstruction
Ryan Liu, Paolo Calafiura, Steven Farrell +3
We introduce a novel variant of GNN for particle tracking called Hierarchical Graph Neural Network (HGNN). The architecture creates a set of higher-level representations which corr…
Generative Machine Learning for Detector Response Modeling with a Conditional Normalizing Flow
Allison Xu, Shuo Han, Xiangyang Ju +1
In this paper, we explore the potential of generative machine learning models as an alternative to the computationally expensive Monte Carlo (MC) simulations commonly used by the L…
Heterogeneous Graph Neural Network for Identifying Hadronically Decayed Tau Leptons at the High Luminosity LHC
Andris Huang, Xiangyang Ju, Jacob Lyons +3
We present a new algorithm that identifies reconstructed jets originating from hadronic decays of tau leptons against those from quarks or gluons. No tau lepton reconstruction algo…
Benchmarking GPU and TPU Performance with Graph Neural Networks
xiangyang Ju, Yunsong Wang, Daniel Murnane +3
Many artificial intelligence (AI) devices have been developed to accelerate the training and inference of neural networks models. The most common ones are the Graphics Processing U…
Track Reconstruction using Geometric Deep Learning in the Straw Tube Tracker (STT) at the PANDA Experiment
Adeel Akram, Xiangyang Ju
The PANDA (anti-Proton ANnihilation at DArmstadt) experiment at the Facility for Anti-proton and Ion Research is going to study strong interactions at the scale at which quarks are…
Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations
Vincent Dumont, Xiangyang Ju, Juliane Mueller
The Generative Adversarial Network (GAN) is a powerful and flexible tool that can generate high-fidelity synthesized data by learning. It has seen many applications in simulating e…
Portability: A Necessary Approach for Future Scientific Software
Meghna Bhattacharya, Paolo Calafiura, Taylor Childers +16
Today's world of scientific software for High Energy Physics (HEP) is powered by x86 code, while the future will be much more reliant on accelerators like GPUs and FPGAs. The porta…
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…
Beyond 4D Tracking: Using Cluster Shapes for Track Seeding
Patrick J. Fox, Shangqing Huang, Joshua Isaacson +2
Tracking is one of the most time consuming aspects of event reconstruction at the Large Hadron Collider (LHC) and its high-luminosity upgrade (HL-LHC). Innovative detector technolo…