2,141 research outputs found
Deep-learned Top Tagging with a Lorentz Layer
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
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
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
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
- …