Bidirectional Long Short-Term Memory (BLSTM) neural networks for reconstruction of top-quark pair decay kinematics

TL;DR

This paper introduces a deep neural network based on Bidirectional LSTM to reconstruct top-quark pair decay kinematics from simulated collider data, achieving accuracy comparable to traditional methods.

Contribution

The study presents a novel application of BLSTM neural networks for probabilistic reconstruction of top-quark decay kinematics in high-energy physics.

Findings

BLSTM achieves comparable accuracy to traditional permutation-based methods.

The neural network effectively reconstructs parton-level kinematic distributions.

Simulation results demonstrate robustness against detector resolution effects.

Abstract

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 $\mu$+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.