相关论文: Enhancing the top signal at Tevatron using Neural …
Neural networks (NNs) provide a powerful and flexible tool for selecting a signal from a larger background. The D0 collaboration has used them extensively in studying t-tbar decays. NNs were essential to the measurement of the t-tbar…
The application of Neural Networks in High Energy Physics to the separation of signal from background events is studied. A variety of problems usually encountered in this sort of analyses, from variable selection to systematic errors, are…
We present a method for resolving the combinatorial issues in the \ttbar lepton+jets events occurring at the Tevatron collider. By incorporating multiple information into an artificial neural network, we introduce a novel event…
I review the latest results on properties of the top quark from the Tevatron and the LHC, including results measured in $t\bar{t}$ and single-top events on the mass, width, couplings, and spin correlations.
We present an optimized and physically motivated method for separating top quark signal events from background events at the Tevatron. For the top quark signal $t\bar t \to e/\mu + 4$ jets, we show how to reject all but $25\%$ of the…
The first evidence for the top quark at the Tevatron may indicate a cross section higher than the QCD expectation. We consider the possibility that isosinglet heavy quarks may be contributing to the signal and discuss ways of testing this…
The reconstruction of top-quark pair-production ($t\bar{t}$) events is a prerequisite for many top-quark measurements. We use a deep neural network, trained with Monte-Carlo simulated events, to reconstruct $t\bar{t}$ decays in the…
An optimal choice of proper kinematical variables is one of the main steps in using neural networks (NN) in high energy physics. Our method of the variable selection is based on the analysis of a structure of Feynman diagrams (singularities…
We study the possibility to employ neural networks to simulate jet clustering procedures in high energy hadron-hadron collisions. We concentrate our analysis on the Fermilab Tevatron energy and on the $k_\bot$ algorithm. We consider both…
The large data samples of thousands of top events collected at the Tevatron experiments CDF and D0 allow for a variety of measurements to analyze the properties of the top quark. Guided by the question "Is the top quark observed at the…
Neural networks are used extensively in classification problems in particle physics research. Since the training of neural networks can be viewed as a problem of inference, Bayesian learning of neural networks can provide more optimal and…
The use of neural networks for signal vs.~background discrimination in high-energy physics experiment has been investigated and has compared favorably with the efficiency of traditional kinematic cuts. Recent work in top quark…
Further kinematical variables are suggested, in which to compare the putative leptonic $W$ plus 4-jet top quark signal with the QCD background. We show that the lepton rapidity asymmetry, the $p_T$-ranking of the tagged $b$-jet, the…
Multi-channel satellite imagery, from stacked spectral bands or spatiotemporal data, have meaningful representations for various atmospheric properties. Combining these features in an effective manner to create a performant and trustworthy…
We explore the possibility that the right-handed top quark is composite. We examine the consequences that compositeness would have on $t \bar{t}$ production at the Tevatron, and derive a weak constraint on the scale of compositeness of…
Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it…
Measurements of the top quark mass and the t-tbar and single top production cross sections, obtained by CDF and D0 Collaboration at the Tevatron, are presented. The methodology of the CDF and D0 analyses and their underlying assumptions are…
Properties of the top-quark are presented, with emphasis on the most recent ATLAS measurements of the mass and $t\bar{t}$ spin correlations, obtained with proton-proton collision data collected at the Large Hadron Collider. Normalised…
Topological neural networks (TNNs) are information processing architectures that model representations from data lying over topological spaces (e.g., simplicial or cell complexes) and allow for decentralized implementation through localized…
Using a simple analytic expression for $q \bar{q}, g g \rightarrow t \bar{t} \rightarrow b W^+ \bar{b} W^- \rightarrow b \bar{l} \nu_l \bar{b} l' \bar{\nu_{l'}}$ with the interference effects due to the polarizations of the $t$ and…