Related papers: Identification of b-jets using QCD-inspired observ…
This contribution reports preliminary jet results in ppbar collisions at sqrt(s)=1.96 TeV from the CDF and D0 experiments. The jet inclusive cross section, measured using both the Midpoint and the K_T jet clustering algorithm, is compared…
Jet substructure observables serve as essential tools for probing the quark-gluon plasma produced in relativistic heavy-ion collisions. Their interpretation, however, is often complicated by edge effects, which arise when correlated…
Deep learning techniques have shown the capability to identify the degree of energy loss of high-energy jets traversing hot QCD medium on a jet-by-jet basis. The average amount of quenching of quark and gluon jets in hot QCD medium actually…
Recent jet and jet substructure measurements at the LHC, and of machine-learning-based tagging techniques are presented using proton-proton collision data collected by the ATLAS and CMS experiments at CERN's Large Hadron Collider. These…
Searching for new physics in large data sets needs a balance between two competing effects---signal identification vs background distortion. In this work, we perform a systematic study of both single variable and multivariate jet tagging…
We study the sensitivity of a down type quark $b^{\prime}$ via process $pp\rightarrow b^{\prime}\bar{b^{\prime}} \rightarrow tW^-\bar{t}W^+$ using jet substructure methods at the LHC with the collision energy $\sqrt{s}=14$ TeV. We consider…
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…
Jet substructure observables have been used by experiments at the Large Hadron Collider (LHC) as instruments to search for new physics as well as to study perturbative and nonperturbative processes in quantum chromodynamics (QCD). Some…
Jet substructure observables provide powerful tools to search for new physics and test theoretical descriptions of perturbative and non-perturbative processes in QCD. In heavy-ion collisions, jet substructure observables are used to…
A method of top tagging is introduced. Using the anti-kt algorithm to define jets, events with nj = 2 fat jets of cone size R = 1.5 are decomposed into R = 0.6 sub-jets and retained if nj (R= 0.6) >= 4 . One pair of sub-jets reconstructs…
The article is devoted to the searches for new particles predicted by physics beyond the Standard Model through the b-tagging algorithm. The dependence of b-tagging efficiency on the jet identification, impact parameter identification,…
The study of the internal structure of hadronic jets has become in recent years a very active area of research in particle physics. Jet substructure techniques are increasingly used in experimental analyses by the LHC collaborations, both…
Deep learning methods have been increasingly adopted to study jets in particle physics. Since symmetry-preserving behavior has been shown to be an important factor for improving the performance of deep learning in many applications, Lorentz…
Two machine learning techniques for jet measurements at the LHCb experiment are presented: a regression-based method for jet-energy calibration and a deep neural network algorithm for jet flavour tagging, distinguishing between $b$-quark,…
Once the existence of the Higgs boson is established at the CERN Large Hadron Collider (LHC), the focus will be shifted toward understanding its couplings to other particles. A crucial aspect is the measurement of the bottom Yukawa…
I explore many aspects of jet substructure at the Large Hadron Collider, ranging from theoretical techniques for jet calculations, to phenomenological tools for better searches with jets, to software for implementing and comparing such…
We introduce collinear drop jet substructure observables, which are unaffected by contributions from collinear radiation, and systematically probe soft radiation within jets. These observables can be designed to be either sensitive or…
We discuss next-to-next-to-leading order QCD results for Higgs, W-boson and Z-boson production in association with a jet in hadronic collisions, obtained using the recently developed N-jettiness subtraction method.
Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of…
We summarize measurements of b and c jet production at the LHC, which are an important signature and background for decays of massive particles such as H-to-b-bbar. These include measurements of the inclusive and dijet production of heavy…