Related papers: Jet tagging made easy
A framework is presented to extract and understand decision-making information from a deep neural network (DNN) classifier of jet substructure tagging techniques. The general method studied is to provide expert variables that augment inputs…
The identification and characterization of jets are crucial tasks for effectively probing fundamental particle interactions. The ATLAS and CMS experiments have developed cutting-edge techniques to improve jet identification and calibration,…
Jet flavor tagging plays an important role in precise Standard Model measurement enabling the extraction of mass dependence in jet-quark interaction and quark-gluon plasma (QGP) interactions. They also enable inferring the nature of…
We propose a new approach of jet-based event reconstruction that aims to optimally exploit correlations between the products of a hadronic multi-pronged decay across all Lorentz boost regimes. The new approach utilizes clustered…
The identification of jets containing $b$ hadrons is important for the physics programme of the ATLAS experiment at the Large Hadron Collider. Several algorithms to identify jets containing $b$ hadrons are described, ranging from those…
Deep neural networks trained for jet tagging are typically specific to a narrow range of transverse momenta or jet masses. Given the large phase space that the LHC is able to probe, the potential benefit of classifiers that are effective…
We present the development and validation of a new multivariate $b$ jet identification algorithm ("$b$ tagger") used at the CDF experiment at the Fermilab Tevatron. At collider experiments, $b$ taggers allow one to distinguish particle jets…
Studying heavy-flavor jets in pp collision is important since they can test pQCD calculations and be used as a reference for heavy-ion collisions. Jets in this analysis are reconstructed from charged particles using the…
Jet flavor tagging, the identification of jets originating from $c$-quarks, $b$-quarks, and other quarks (light quarks and gluons), is a crucial task in high-energy heavy-ion physics, as it enables the investigation of flavor-dependent…
We apply techniques from Bayesian generative statistical modeling to uncover hidden features in jet substructure observables that discriminate between different a priori unknown underlying short distance physical processes in multi-jet…
There has been substantial progress in applying machine learning techniques to classification problems in collider and jet physics. But as these techniques grow in sophistication, they are becoming more sensitive to subtle features of jets…
The process by which jet algorithms construct jets and subjets is inherently ambiguous and equally well motivated algorithms often return very different answers. The Qjets procedure was introduced by the authors to account for this…
Heavy flavour jet tagging is widely used in the determination of cross sections including the production of heavy flavoured quarks. This requires the knowledge of heavy and light flavour jet tagging efficiencies and their uncertainties. A…
Deep Learning approaches are becoming the go-to methods for data analysis in High Energy Physics (HEP). Nonetheless, most physics-inspired modern architectures are computationally inefficient and lack interpretability. This is especially…
Jet point cloud images are high dimensional data structures that needs to be transformed to a separable feature space for machine learning algorithms to distinguish them with simple decision boundaries. In this article, the authors focus on…
Classifying hadronic jets using their constituents' kinematic information is a critical task in modern high-energy collider physics. Often, classifiers are designed by targeting the best performance using metrics such as accuracy, AUC, or…
Semivisible jets are a characteristic signature of many confining dark sectors and consist of jets of visible hadrons intermixed with invisible stable particles. Since their initial proposal, considerable progress has been made in…
Jet classification is an important ingredient in measurements and searches for new physics at particle coliders, and secondary vertex reconstruction is a key intermediate step in building powerful jet classifiers. We use a neural network to…
A tagging algorithm to identify jets that are significantly displaced from the proton-proton (pp) collision region in the CMS detector at the LHC is presented. Displaced jets can arise from the decays of long-lived particles (LLPs), which…
A measurement of jet substructure variables is presented using data collected in 2016 by the ATLAS experiment at the LHC with proton-proton collisions at $\sqrt{s}=13$ TeV. Large-radius jets groomed with the trimming and soft-drop…