Reconstruction of Unstable Heavy Particles Using Deep Symmetry-Preserving Attention Networks
This work addresses particle physics data analysis challenges at the Large Hadron Collider, offering incremental enhancements to existing methods for improved reconstruction accuracy.
The paper tackles the problem of reconstructing unstable heavy particles like top quarks by extending a symmetry-preserving attention network to handle multiple input types and global features, achieving significant improvements in tasks such as ttH searches, top quark mass measurements, and heavy Z' searches.
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.