Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks

TL;DR

This paper introduces SPA-Net, a neural network with symmetry-preserving attention, that accurately reconstructs top quark decay products in complex multi-jet events, outperforming existing methods.

Contribution

The paper presents SPA-Net, a novel symmetry-preserving attention network that effectively solves the permutation problem in multi-jet top quark reconstruction.

Findings

Achieves 93.0% accuracy on 6-jet events

Outperforms state-of-the-art methods in jet assignment

Successfully reconstructs decay products in complex events

Abstract

Top quarks, produced in large numbers at the Large Hadron Collider, have a complex detector signature and require special reconstruction techniques. The most common decay mode, the "all-jet" channel, results in a 6-jet final state which is particularly difficult to reconstruct in $pp$ collisions due to the large number of permutations possible. We present a novel approach to this class of problem, based on neural networks using a generalized attention mechanism, that we call Symmetry Preserving Attention Networks (SPA-Net). We train one such network to identify the decay products of each top quark unambiguously and without combinatorial explosion as an example of the power of this technique.This approach significantly outperforms existing state-of-the-art methods, correctly assigning all jets in $93.0%$ of $6$-jet, $87.8%$ of $7$-jet, and $82.6%$ of $\geq 8$-jet events respectively.