Related papers: Quark-Gluon Tagging: Machine Learning vs Detector
In searches for new physics in the energy regime of the LHC, it is becoming increasingly important to distinguish single-jet objects that originate from the merging of the decay products of W bosons produced with high transverse momenta…
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…
We investigate the signature of a heavy charged Higgs boson of the Minimal Supersymmetric Standard Model in the lepton plus multi-jet channel at the Large Hadron Collider with four $b$-tags. The signal is the gluon-gluon fusion process…
We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at relatively low EIC energies, focusing on (i) identifying the…
Vector bosons become accessible experimental probes in heavy-ion collisions at the LHC. The capabilities of the LHC experiments to perform their measurement are outlined. The focus is given to their utility to study the possible formation…
Anomaly detection methods used in a recent search for new phenomena by CMS at the CERN LHC are presented. The methods use machine learning to detect anomalous jets produced in the decay of new massive particles. The effectiveness of these…
Three jets events in the e$^+$e$^-$ collisions at 91.2 GeV are investigated using both HERWIG and JETSET Monte Carlo generators. The angles between the three jets are used to identify the quark and gluon jets. The analysis at parton level…
Gluon jets are identified in e+e- hadronic annihilation events by tagging two quark jets in the same hemisphere of an event. The gluon jet is defined inclusively as all the particles in the opposite hemisphere. Gluon jets defined in this…
The increasing data rates and complexity of detectors at the Large Hadron Collider (LHC) necessitate fast and efficient machine learning models, particularly for rapid selection of what data to store, known as triggering. Building on recent…
We show how generalized energy correlation functions can be used as a powerful probe of jet substructure. These correlation functions are based on the energies and pair-wise angles of particles within a jet, with (N+1)-point correlators…
The identification of jets resulting from the fragmentation and hadronization of b quarks is an important part of high-pT collider physics. The methods used by the CDF and DO collaborations to perform this identification are described,…
In order to fully address the oscillation processes at a neutrino factory, a detector should be capable of identifying and measuring all three charged lepton flavors produced in charged current interactions {\it and} of measuring their…
Jet tagging has become an essential tool for new physics searches at the high-energy frontier. For jets that contain energetic charged leptons we introduce Feature Extended Supervised Tagging (FEST) which, in addition to jet substructure,…
We demonstrate that the classification of boosted, hadronically-decaying weak gauge bosons can be significantly improved over traditional cut-based and BDT-based methods using deep learning and the jet charge variable. We construct binary…
A method is proposed for distinguishing highly boosted hadronically decaying W's (W-jets) from QCD-jets using jet substructure. Previous methods, such as the filtering/mass-drop method, can give a factor of ~2 improvement in S/sqrt(B) for…
We use ATLAS and CMS searches in the mono-jet + missing energy and mono-photon + missing energy final state to set limits on the couplings of dark matter to quarks and gluons. Working in an effective field theory framework we compare…
At the LHC, a TeV-scale leptoquark (LQ) that decays dominantly to a top quark ($t$) and a light charged lepton ($\ell=e,\mu$) would form a resonance system of \emph{boosted-$t$ $+$ high-$p_{\rm T}$-$\ell$}. We consider all possible vector…
Machine learning techniques are used for treating jets as images to explore the performance of boosted top quark tagging. Tagging performances are studied in both hadronic and leptonic channels of top quark decay, employing a convolutional…
An overview of analyses using data at sqrt(s) = 7 TeV and 8 TeV of proton-proton collisions at the LHC is presented. These analyses use boosted techniques to search for new phenomena involving top quarks and to measure the production of top…
We present an improved search for single top quarks in two production modes, s-channel (tb) and t-channel (tqb). The search is performed in the electron+jets and muon+jets decay channels, with one or more b-tagged jets, on nearly 370…