6 papers
Uncertainty Quantification and Propagation for ACORN, a geometric deep learning tracking pipeline for HEP experiments
Lukas Péron, Paolo Calafiura, Xiangyang Ju +1
We have developed an Uncertainty Quantification process for multistep pipelines and applied it to the ACORN particle tracking pipeline. All our experiments are made using the Track…
TrackSorter: A Transformer-based sorting algorithm for track finding in High Energy Physics
Yash Melkani, Xiangyang Ju
Track finding in particle data is a challenging pattern recognition problem in High Energy Physics. It takes as inputs a point cloud of space points and labels them so that space p…
An Application of HEP Track Seeding to Astrophysical Data
Mine Gökçen, Maurice Garcia-Sciveres, Xiangyang Ju
We apply methods of particle track reconstruction in High Energy Physics (HEP) to the search for distinct stellar populations in the Milky Way, using the Gaia EDR3 data set. This w…
Integrating Particle Flavor into Deep Learning Models for Hadronization
Jay Chan, Xiangyang Ju, Adam Kania +3
Hadronization models used in event generators are physics-inspired functions with many tunable parameters. Since we do not understand hadronization from first principles, there hav…
Event Generator Tuning Incorporating Systematic Uncertainty
Jaffae Schroff, Xiangyang Ju
Event generators play an important role in all physics programs at the Large Hadron Collider and beyond. Dedicated efforts are required to tune the parameters of event generators t…
Simulation of Hadronic Interactions with Deep Generative Models
Tuan Minh Pham, Xiangyang Ju
Accurate simulation of detector responses to hadrons is paramount for all physics programs at the Large Hadron Collider (LHC). Central to this simulation is the modeling of hadroni…