Reconstruction of Unstable Heavy Particles Using Deep Symmetry-Preserving Attention Networks

TL;DR

This paper extends symmetry-preserving attention networks (SPA-NET) to incorporate multiple object types and event features, significantly improving heavy particle reconstruction in collider experiments.

Contribution

The authors enhance SPA-NET architecture to handle diverse input data and provide additional outputs, improving particle reconstruction accuracy in complex collider events.

Findings

Improved performance in ttH search, top quark mass measurement, and Z' boson search.

Significant accuracy gains over previous methods.

Ablation studies reveal network's learned features.

Abstract

Reconstructing unstable heavy particles requires sophisticated techniques to sift through the large number of possible permutations for assignment of detector objects to the underlying partons. Anapproach based on a generalized attention mechanism, symmetry preserving attention networks (SPA-NET), has been previously applied to top quark pair decays at the Large Hadron Collider which produce only hadronic jets. Here we extend the SPA-NET architecture to consider multiple input object types, such as leptons, as well as global event features, such as the missing transverse momentum. Inaddition, we provide regression and classification outputs to supplement the parton assignment. We explore the performance of the extended capability of SPA-NET in the context of semi-leptonic decays of top quark pairs as well as top quark pairs produced in association with a Higgs boson. We find significant improvements in the power of three representative studies: a search for ttH, a measurement of the top quark mass, and a search for a heavy Z' decaying to top quark pairs. We present ablation studies to provide insight on what the network has learned in each case.