2019/09/03 by Fardin Syed, Riccardo Di Sipio, Syed, Fardin +5
Physics and Astronomy · #Computational Physics (physics.comp-ph) #Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High-Energy Particle Collisions Research #Particle physics theoretical and experimental studies #Quantum Chromodynamics and Particle Interactions #Statistics and Probability (physics.data-an) #hep-ex #physics.comp-ph #physics.data-an
paper · pdf · doi:10.48550/arxiv.1909.01144
13 pages, 8 figures, 1 table, the source code is available at https://github.com/IMFardz/AngryTops
arxiv created 2019/09/03 · openalex publication_date 2019/09/03 · arxiv updated 2019/09/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A probabilistic reconstruction using machine-learning of the decay kinematics of top-quark pairs produced in high-energy proton-proton collisions is presented. A deep neural network whose core consists of a Bidirectional Long Short-Term Memory (BLSTM) is trained to infer the four-momenta of the two top quarks produced in the hard scattering process. The MadGraph5+Pythia8 Monte Carlo event generator is used to create a sample of top-quark pairs decaying in the μ+jets channel, whose final-state objects are used to create the input to the deep neural network. Distortions due to limited resolution of the experimental apparatus are simulated with the Delphes3 fast detector simulator. The level of agreement between the Monte Carlo predictions and the BLSTM for kinematic distributions at parton level is comparable to that obtained using a benchmark method that finds the jet permutation that minimizes an objective function.