The article presents an extension of the SPA-NET architecture to handle events with multiple types of reconstructed objects, such as leptons and missing transverse momentum, in addition to hadronic jets. The extended SPA-NET model provides several capabilities beyond just parton-jet assignment, including regression of missing kinematic quantities, classification of signal vs. background events, and estimation of the probability of each particle being reconstructable in the event.
The performance of SPA-NET is evaluated and compared to two baseline permutation-based methods, KLFitter and a Permutation Deep Neural Network (PDNN), in the context of semi-leptonic top quark pair production and top quark pair production in association with a Higgs boson. SPA-NET is shown to significantly outperform the baseline methods in terms of reconstruction efficiency, especially in events with high jet multiplicity.
The improved reconstruction capabilities of SPA-NET are then leveraged to demonstrate significant gains in three representative LHC analyses: a search for ttH production, a measurement of the top quark mass, and a search for a heavy Z' boson decaying to top quark pairs. Ablation studies are also presented to provide insight into what the network has learned.
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by Michael Jame... at arxiv.org 05-02-2024
https://arxiv.org/pdf/2309.01886.pdfDeeper Inquiries