Related papers: Deep-learned Top Tagging with a Lorentz Layer
There is an increasing interest in the application of deep learning architectures to tabular data. One of the state-of-the-art solutions is TabTransformer which incorporates an attention mechanism to better track relationships between…
Observables sensitive to top quark polarization are important for characterizing or even discovering new physics. The most powerful spin analyzer in top decay is the down-type fermion from the W, which in the case of leptonic decay allows…
Neural network-based algorithms provide a promising approach to jet classification problems, such as boosted top jet tagging. To date, NN-based top taggers demonstrated excellent performance in Monte Carlo studies. In this paper, we…
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…
Heavy resonances are an integral part of many extensions of the Standard Model. The discovery of such heavy resonances are a primary goal at the LHC and future hadron colliders. When a particle with TeV-scale mass decays into…
This note presents an overview of current and potential future applications of machine-learning-based techniques in the study of the top quark. The research community has developed a diverse set of ideas and tools, including algorithms for…
Weakly supervised methods have emerged as a powerful tool for model-agnostic anomaly detection at the Large Hadron Collider (LHC). While these methods have shown remarkable performance on specific signatures such as di-jet resonances, their…
The large ttbar production cross-section at the LHC suggests the use of top quark decays to calibrate several critical parts of the detectors, such as the trigger system, the jet energy scale and b-tagging.
We describe a novel application of the end-to-end deep learning technique to the task of discriminating top quark-initiated jets from those originating from the hadronization of a light quark or a gluon. The end-to-end deep learning…
A novel deep learning method for improving the belief propagation algorithm is proposed. The method generalizes the standard belief propagation algorithm by assigning weights to the edges of the Tanner graph. These edges are then trained…
Deep learning has shown promising results in many machine learning applications. The hierarchical feature representation built by deep networks enable compact and precise encoding of the data. A kernel analysis of the trained deep networks…
Precise reconstruction of top quark properties is a challenging task at the Large Hadron Collider due to combinatorial backgrounds and missing information. We introduce a physics-informed neural network architecture called the Covariant…
Pixel detectors currently in use by high energy physics experiments such as ATLAS, CMS, LHCb, etc., are critical systems for tagging $B$ hadrons within particle jets. However, the performance of standard tagging algorithms begins to fall in…
Top partner searches are one of the key aspects of new physics analyses at the LHC. We correct an earlier statement that supersymmetric top searches based on decays to semileptonic tops are not promising. Reconstructing the direction of the…
The distribution of particles inside hadronic jets produced in the decay of boosted $W$ and $Z$ bosons can be used to discriminate such jets from the continuum background. Given that a jet has been identified as likely resulting from the…
A novel technique based on machine learning is introduced to reconstruct the decays of highly Lorentz-boosted particles. Using an end-to-end deep learning strategy, the technique bypasses existing rule-based particle reconstruction methods…
New heavy resonances with sizeable couplings to top quarks can be probed through searches for beyond-the-Standard-Model effects in four-top production at the LHC. In this work, we present the first next-to-leading-order QCD predictions for…
We present a novel method for measuring $|V_{cb}|$ at the LHC using an advanced boosted-jet tagger to identify "$bc$ signatures". By associating boosted $W \rightarrow bc$ signals with $bc$-matched jets from top-quark decays, we enable an…
We study the heavy charged Higgs boson (from 800 GeV to 1500 GeV in this study) in production associated with a top quark at the LHC with the collision energy $\sqrt{s}=14$ TeV. Such a heavy charged Higgs boson can dominantly decay into a…
A significant challenge in the tagging of boosted objects via machine-learning technology is the prohibitive computational cost associated with training sophisticated models. Nevertheless, the universality of QCD suggests that a large…