13 citations · 69 across the 11 of their papers we have counts for
6 papers · 1 filter
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…