2,004 research outputs found

    Deep-learned Top Tagging with a Lorentz Layer

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    We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric. With its novel machine learning setup and architecture it allows us to identify boosted top quarks not only from calorimeter towers, but also including tracking information. We show how the performance of our tagger compares with QCD-inspired and image-recognition approaches and find that it significantly increases the performance for strongly boosted top quarks.Comment: v3: minor revisions following SciPost referee report

    Resonance Searches with an Updated Top Tagger

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    The performance of top taggers, for example in resonance searches, can be significantly enhanced through an increased set of variables, with a special focus on final-state radiation. We study the production and the decay of a heavy gauge boson in the upcoming LHC run. For constant signal efficiency, the multivariate analysis achieves an increased background rejection by up to a factor 30 compared to our previous tagger. Based on this study and the documentation in the Appendix we release a new HEPTopTagger2 for the upcoming LHC run. It now includes an optimal choice of the size of the fat jet, N-subjettiness, and different modes of Qjets.Comment: 26 page

    EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets

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    With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet so far these approaches are largely constrained to fixed data structures and rigid detector geometries. In this paper, we introduce EPiC-GAN - equivariant point cloud generative adversarial network - which can produce point clouds of variable multiplicity. This flexible framework is based on deep sets and is well suited for simulating sprays of particles called jets. The generator and discriminator utilize multiple EPiC layers with an interpretable global latent vector. Crucially, the EPiC layers do not rely on pairwise information sharing between particles, which leads to a significant speed-up over graph- and transformer-based approaches with more complex relation diagrams. We demonstrate that EPiC-GAN scales well to large particle multiplicities and achieves high generation fidelity on benchmark jet generation tasks.Comment: 18 pages, 8 figures, 3 tables, code available at: https://github.com/uhh-pd-ml/EPiC-GA

    Search for Resonances Decaying into Top Quark Pairs Using Fully Hadronic Decays in pp Collisions with ATLAS at sqrt(s) = 7 TeV

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    A search for new particles that decay into top-quark pairs producing two massive jets with high transverse momentum is presented. Data collected with the ATLAS detector at the Large Hadron Collider during the proton-proton collision run at sqrt(s) = 7 TeV in 2011 is analysed. The substructure-based HEPTopTagger technique is used to separate top-quark jets from those arising from light quarks or gluons. The performance of this method is evaluated using a statistically independent sample. Top-quark candidates are also required to have a bottom-quark decay associated with them. The backgrounds are estimated using data-driven techniques. No significant deviation between data and the sum of Standard Model background processes, such as ttbar production and multijet production, is observed in the di-top invariant mass spectrum. Therefore limits on the production cross section times branching fractions of certain models of Z' boson and a Kaluza-Klein gluon resonances are set. The production of Z' bosons with masses between 0.70 and 1.00 TeV as well as 1.28 and 1.32 TeV and Kaluza-Klein gluons with masses between 0.70 and 1.48 TeV is excluded at 95% C.L
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