Related papers: How to Unfold Top Decays
The underlying structure of matter can be deeply probed via precision measurements of the mass of the \emph{top quark}, the most massive observed fundamental particle. Top quarks can be produced and studied only in collisions at high energy…
We show that maximally twisted mass fermions can be employed to regularize on the lattice the fully unquenched QCD+QED theory with vanishing $\theta$-term. We discuss how the critical mass of the up and down quarks can be conveniently…
Deep generative models are effective methods of modeling data. However, it is not easy for a single generative model to faithfully capture the distributions of complex data such as images. In this paper, we propose an approach for boosting…
Experimental results on top-quark physics obtained at the CMS experiment are reported based on the data recorded at centre-of-mass energy up to 13 TeV. Inclusive and differential cross sections for both top-quark pair and single top-quark…
A likelihood-based unfolding method based on Bayes' theorem is presented, with a particular emphasis on the application to differential cross-section measurements in high-energy particle interactions.
In top quark production, the polarization of top quarks, decided by the chiral structure of couplings, is likely to be modified in the presence of any new physics contribution to the production. Hence the same is a good discriminator for…
In this paper we study the identification of boosted hadronically decaying top quarks using jet substructure in the center-of-mass frame of the jet. We demonstrate that the method can greatly reduce the QCD jet background while maintaining…
The top quark is expected to decay as a free particle with definite momentum and polarization. Here we consider the possibilities to obtain information about its polarization through the energy and angular distributions of the b-quarks from…
We consider top-quarks produced at large energy in e+e- collisions, and address the question of what top-mass can be measured from reconstruction. The production process is characterized by well separated scales: the center-of-mass energy,…
We use a tensor unfolding technique to prove a new identifiability result for discrete bipartite graphical models, which have a bipartite graph between an observed and a latent layer. This model family includes popular models such as…
Particle filtering is used to compute good nonlinear estimates of complex systems. It samples trajectories from a chosen distribution and computes the estimate as a weighted average. Easy-to-sample distributions often lead to degenerate…
We present an up-to-date overview of the problem of top quark mass determination. We assess the need for precision in the top mass extraction in the LHC era together with the main theoretical and experimental issues arising in precision top…
The role of the top quark in completing the Standard Model quark sector is reviewed, along with a discussion of production, decay, and theoretical restrictions on the top quark properties. Particular attention is paid to the top quark as a…
A determination of the top-quark mass is presented using 20.2 $\text{fb}^{-1}$ of 8 TeV proton-proton collision data produced by the Large Hadron Collider and collected by the ATLAS experiment. The normalised differential cross section of…
The prediction of protein stability changes following single-point mutations plays a pivotal role in computational biology, particularly in areas like drug discovery, enzyme reengineering, and genetic disease analysis. Although…
A learned generative model often produces biased statistics relative to the underlying data distribution. A standard technique to correct this bias is importance sampling, where samples from the model are weighted by the likelihood ratio…
This paper extends and builds upon the results of an earlier paper, in which we described how to use the tools of geometrical engineering to deform geometrically-engineered grand unified models into ones with lower symmetry. This top-down…
In recent years, algorithm unrolling has emerged as a powerful technique for designing interpretable neural networks based on iterative algorithms. Imaging inverse problems have particularly benefited from unrolling-based deep network…
We present a systematic comparison of different approaches for the modeling of $t\bar{t}\gamma$ final states with leptonic decays at the LHC. On the one hand, we consider a complete calculation at NLO QCD accuracy which includes all…
Unsupervised learning aims at the discovery of hidden structure that drives the observations in the real world. It is essential for success in modern machine learning. Latent variable models are versatile in unsupervised learning and have…