Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
This addresses the challenge of combinatorial explosion in particle physics event reconstruction, providing a novel solution for high-energy physics researchers.
The paper tackles the problem of reconstructing top quark decays in all-jet channels at the Large Hadron Collider, which involves a large number of permutations, by using Symmetry Preserving Attention Networks (SPA-Net). It achieves correct jet assignments in 93.0% of 6-jet, 87.8% of 7-jet, and 82.6% of ≥8-jet events, significantly outperforming existing methods.
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.