Publications (12)
Neural Posterior Unfolding
Fernando Torales Acosta, Jay Chan, Krish Desai +4
Differential cross section measurements are the currency of scientific exchange in particle and nuclear physics. A key challenge for these analyses is the correction for detector d…
Application of Quantum Machine Learning using the Quantum Variational Classifier Method to High Energy Physics Analysis at the LHC on IBM Quantum Computer Simulator and Hardware with 10 qubits
Sau Lan Wu, Jay Chan, Wen Guan +12
One of the major objectives of the experimental programs at the LHC is the discovery of new physics. This requires the identification of rare signals in immense backgrounds. Using…
EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstruction
Paolo Calafiura, Jay Chan, Loic Delabrouille +1
Track reconstruction is a crucial task in particle experiments and is traditionally very computationally expensive due to its combinatorial nature. Recently, graph neural networks…
Application of Quantum Machine Learning using the Quantum Kernel Algorithm on High Energy Physics Analysis at the LHC
Sau Lan Wu, Shaojun Sun, Wen Guan +20
Quantum machine learning could possibly become a valuable alternative to classical machine learning for applications in High Energy Physics by offering computational speed-ups. In…
Physics and Computing Performance of the EggNet Tracking Pipeline
Jay Chan, Brandon Wang, Paolo Calafiura
Particle track reconstruction is traditionally computationally challenging due to the combinatorial nature of the tracking algorithms employed. Recent developments have focused on…
Breaking molecular nitrogen under mild conditions with an atomically clean lanthanide surface
Felicia Ullstad, Gabriel Bioletti, Jay Chan +5
A route to break molecular nitrogen (N2) under mild conditions is demonstrated by N2 gas cracking on, and incorporation into, lanthanide films. Successful growth of lanthanide nitr…
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…
Investigation of Higgs Boson Decaying to Di-muon, Dark Matter Produced in Association with a Higgs Boson Decaying to -quarks and Unbinned Profiled Unfolding
Jay Chan
The discovery of the Standard Model (SM) Higgs boson by ATLAS and CMS at the LHC in 2012 marked a major milestone in particle physics. However, many questions remain unanswered, wh…
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…
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…
Double Metric Learning for Building Directed Graphs with Chain Connections for the ATLAS ITk Detector
Jay Chan
Graph construction is an essential step in the Graph Neural Network (GNN) based tracking pipelines. The goal of the graph construction is to construct a graph that contains only th…
Unbinned Profiled Unfolding
Jay Chan, Benjamin Nachman
Unfolding is an important procedure in particle physics experiments which corrects for detector effects and provides differential cross section measurements that can be used for a…