相关论文: Machine Learning Based Top Quark and W Jet Tagging…
We apply both cut-based and machine learning techniques using the same inputs to the challenge of hadronic jet substructure recognition, utilizing classical subjettiness variables within the Delphes parameterized detector simulation…
We apply gradient boosting machine learning techniques to the problem of hadronic jet substructure recognition using classical subjettiness variables available within a common parameterized detector simulation package DELPHES. Per-jet…
The ubiquity of top-rich final states in the context of beyond the Standard Model (BSM) searches has led to their status as extensively studied signatures at the LHC. Over the past decade, numerous endeavours have been undertaken in the…
The identification of hadronic final states plays a crucial role in the physics programme of the ATLAS Experiment at the CERN LHC. Sophisticated artificial intelligence (AI) algorithms are employed to classify jets according to their…
Top tagging is a recent approach to identifying boosted hadronic top quarks. It avoids reconstructing individual top decay products and instead uses a jet algorithm to reconstruct the entire top decay. Quite generally, geometrically large…
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…
Machine-learning assisted jet substructure tagging techniques have the potential to significantly improve searches for new particles and Standard Model measurements in hadronic final states. Techniques with simple analytic forms are…
We briefly review common tools and methods to identify boosted, hadronically decaying top quarks at the LHC experiments. This includes generic jet substructure variables, specific top identification algorithms, and recent developments in…
We assess the performance of different jet-clustering algorithms, in the presence of different resolution parameters and reconstruction procedures, in resolving fully hadronic final states emerging from the chain decay of the discovered…
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…
Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The…
In this contribution, we study the resonant pair production of heavy particles in hadronic final states using jet substructure techniques. We discuss a recently proposed resonance tagging strategy, which interpolates between the highly…
Boosted top quark tagging is one of the challenging, and at the same time exciting, tasks in high energy physics experiments, in particular in the exploration of new physics signals at the LHC. Several techniques have already been developed…
A method is introduced for distinguishing top jets (boosted, hadronically decaying top quarks) from light quark and gluon jets using jet substructure. The procedure involves parsing the jet cluster to resolve its subjets, and then imposing…
The application of machine learning (ML) in high energy physics (HEP), specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC) experiments, has experienced remarkable growth and innovation in the past decade. This review…
Jet clustering is traditionally an unsupervised learning task because there is no unique way to associate hadronic final states with the quark and gluon degrees of freedom that generated them. However, for uncolored particles like $W$, $Z$,…
In this article, we investigate the possibility of enhancing the di-jet resonance searches by tagging the final state radiation (FSR) jet, using an event-level deep neural network. It is found that solely relying on the 4-momenta of the…
We study the issue of separating hadronic jets that contain bottom quarks ($b$-jets) from jets featuring light partons only. We develop a novel approach to $b$-tagging that exploits the application of QCD-inspired jet substructure…
The ultimate motivation of our study is to look for signs of physics beyond the Standard Model (BSM). We investigate whether different jet clustering techniques might be more or less suited to the particular final states of interest. In…
Identification of hadronic jets originating from heavy-flavor quarks is extremely important to several physics analyses in High Energy Physics, such as studies of the properties of the top quark and the Higgs boson, and searches for new…