2014/02/17 by Ji Hyun Shim, H. S. Lee, Hyun Su Lee · 2 citations
Physics and Astronomy · #Algorithm #Artificial intelligence #Artificial neural network #Collider #Computer science #High-Energy Particle Collisions Research #Large Hadron Collider #Observable #Particle physics #Particle physics theoretical and experimental studies #Physics #Quantum Chromodynamics and Particle Interactions #Quantum mechanics #Tevatron #hep-ex #hep-ph
paper · pdf · doi:10.1103/physrevd.89.114023
published in Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields 89(11) (American Physical Society)
arxiv created 2014/02/17 · openalex publication_date 2014/06/25 · arxiv updated 2014/06/27 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
We present a method for resolving the combinatorial issues in the tt lepton+jets events occurring at the Tevatron collider. By incorporating multiple information into an artificial neural network, we introduce a novel event reconstruction method for such events. We find that this method significantly reduces the number of combinatorial ambiguities. Compared to the classical reconstruction method, our method provides significantly higher purity with the same efficiency. We illustrate the reconstructed observables for the realistic top-quark mass and the forward-backward asymmetry measurements. A Monte Carlo study shows that our method provides meaningful improvements in the top-quark measurements using the same amount of data as other methods.