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

DisCo Fever: Robust Networks Through Distance Correlation

While deep learning has proven to be extremely successful at supervised classification tasks at the LHC and beyond, for practical applications, raw classification accuracy is often not the only consideration. One crucial issue is the stability of network predictions, either versus changes of individual features of the input data, or against systematic perturbations. We present a new method based on a novel application of "distance correlation" (DisCo), a measure quantifying non-linear correlations, that achieves equal performance to state-of-the-art adversarial decorrelation networks but is much simpler and more stable to train. To demonstrate the effectiveness of our method, we carefully recast a recent ATLAS study of decorrelation methods as applied to boosted, hadronic W-tagging. We also show the feasibility of DisCo regularization for more powerful convolutional neural networks, as well as for the problem of hadronic top tagging.Comment: 9 pages, v2: essentially the journal version (refs added, typos fixed, minor improvements

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

Residual ANODE

We present R-ANODE, a new method for data-driven, model-agnostic resonant anomaly detection that raises the bar for both performance and interpretability. The key to R-ANODE is to enhance the inductive bias of the anomaly detection task by fitting a normalizing flow directly to the small and unknown signal component, while holding fixed a background model (also a normalizing flow) learned from sidebands. In doing so, R-ANODE is able to outperform all classifier-based, weakly-supervised approaches, as well as the previous ANODE method which fit a density estimator to all of the data in the signal region instead of just the signal. We show that the method works equally well whether the unknown signal fraction is learned or fixed, and is even robust to signal fraction misspecification. Finally, with the learned signal model we can sample and gain qualitative insights into the underlying anomaly, which greatly enhances the interpretability of resonant anomaly detection and offers the possibility of simultaneously discovering and characterizing the new physics that could be hiding in the data.Comment: 9 pages, 6 figure

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