Related papers: New jet tagging techniques at CMS
Recent literature on deep neural networks for tagging of highly energetic jets resulting from top quark decays has focused on image based techniques or multivariate approaches using high-level jet substructure variables. Here, a sequential…
We introduce a novel jet substructure method which exploits the variation of observables with respect to a sampling of phase-space boundaries quantified by the variability. We apply this technique to identify boosted W boson and top quark…
From particle identification to the discovery of the Higgs boson, deep learning algorithms have become an increasingly important tool for data analysis at the Large Hadron Collider (LHC). We present an innovative end-to-end deep learning…
In this paper, we present a new proposal on how to measure quark/gluon jet properties at the LHC. The measurement strategy takes advantage of the fact that the LHC has collected data at different energies. Measurements at two or more…
A variety of models of physics beyond the standard model predict new particles that decay to leptons, jets, or both together. These models include axigluons, colorons, diquarks, excited quarks, heavy long-lived charged particles,…
We review the history of jets in high energy physics, and describe in more detail the developments of the past ten years, discussing new algorithms for jet finding and their main characteristics, and summarising the status of perturbative…
CMS will use dijets to search for physics beyond the standard model during early LHC running. The inclusive jet cross section as a function of jet transverse momentum, with 10 inverse picobarns of integrated luminosity, is sensitive to…
The ability to identify jets containing B hadrons is important for the high-pT physics program of a general-purpose experiment such as ATLAS. b-tagging is in particular useful for selecting very pure top quark samples, for studying standard…
To enhance the scientific discovery power of high-energy collider experiments, we propose and realize the concept of jet origin identification that categorizes jets into 5 quark species $(b,c,s,u,d)$, 5 anti-quarks…
Jet flavour tagging enables the identification of jets originating from heavy-flavour quarks in proton-proton collisions at the Large Hadron Collider, playing a critical role in its physics programmes. This paper presents GN2, a…
We study quark and gluon jets separately using public collider data from the CMS experiment. Our analysis is based on 2.3/fb of proton-proton collisions at 7 TeV, collected at the Large Hadron Collider in 2011. We define two non-overlapping…
These proceedings highlight a selection of recent results by the ATLAS, CMS and LHCb collaborations. The majority of the featured analyses make use of the large set of $\sqrt{s}=13$ TeV proton-proton collision data collected during the…
Identifying the origin of high-energy hadronic jets ('jet tagging') has been a critical benchmark problem for machine learning in particle physics. Jets are ubiquitous at colliders and are complex objects that serve as prototypical examples…
Jets are one of the most prominent physics signatures of high energy proton proton (p-p) collisions at the Large Hadron Collider (LHC). They are key physics objects for precision measurements and searches for new phenomena. This review…
Tagging jets of strongly interacting particles initiated by energetic strange quarks is one of the few largely unexplored Standard Model object classification problems remaining in high energy collider physics. In this paper we investigate…
High mass resonances decaying into ttbar pairs appear in many extensions of the Standard Model. The top quarks from these decays have high transverse momenta and their decay products are highly collimated due to the boost into the lab…
Machine Learning algorithms have played an important role in hadronic jet classification problems. The large variety of models applied to Large Hadron Collider data has demonstrated that there is still room for improvement. In this context…
A novel deep neural network classifier, a ``Particle transformer'' (PaRT), is introduced for the identification of highly Lorentz-boosted resonances reconstructed as single, multipronged jets in measurements and searches performed by the…
The internal structure of jets produced in pp collisions at the LHC is measured using the ATLAS detector in an inclusive jet sample corresponding to 35pb-1 of pp collisions at sqrt(s) = 7 TeV. Classical jet shape and energy flow…
We use public data from the CMS experiment to study the 2-prong substructure of jets. The CMS Open Data is based on 31.8/pb of 7 TeV proton-proton collisions recorded at the Large Hadron Collider in 2010, yielding a sample of 768,687 events…