Related papers: How to Unfold Top Decays
Deep unrolling is an emerging deep learning-based image reconstruction methodology that bridges the gap between model-based and purely deep learning-based image reconstruction methods. Although deep unrolling methods achieve…
We develop a generative model-based approach to Bayesian inverse problems, such as image reconstruction from noisy and incomplete images. Our framework addresses two common challenges of Bayesian reconstructions: 1) It makes use of complex,…
Most measurements in particle and nuclear physics use matrix-based unfolding algorithms to correct for detector effects. In nearly all cases, the observable is defined analogously at the particle and detector level. We point out that while…
An efficient method for assessing the quality of quantum state tomography is developed. Special attention is paid to the tomography of multipartite systems in terms of unbiased measurements. Although the overall reconstruction errors of…
We investigate the effects of top quark compositeness on various physical parameters, and obtain lower limits on the compositeness scale from electroweak precision data. We consider corrections to top quark decay rates and other physical…
One of the important goals at the future $e^+e^-$ colliders is to measure the top-quark mass and width in a scan of the pair production threshold. However, the shape of the pair-production cross section at the threshold depends also on…
I give an overview of recent results on top quark properties and interactions, obtained using data collected with the CMS experiment during the years 2010--2011 at sqrt(s)= 7 TeV. Measurements are presented for the inclusive top pair…
Measurements of the top-quark mass employing alternative techniques are presented, performed by the D0 and CDF collaborations at the Tevatron as well as the ATLAS and CMS experiments at the LHC. The alternative methods presented include…
Generative modeling, which learns joint probability distribution from data and generates samples according to it, is an important task in machine learning and artificial intelligence. Inspired by probabilistic interpretation of quantum…
Recent works have studied implicit biases in deep learning, especially the behavior of last-layer features and classifier weights. However, they usually need to simplify the intermediate dynamics under gradient flow or gradient descent due…
The unfolding problem formulation for correcting experimental data distortions due to finite resolution and limited detector acceptance is discussed. A novel validation of the problem solution is proposed. Attention is drawn to fact that…
We introduce a new class of infrared safe jet observables, which we refer to as template overlaps, designed to filter targeted highly boosted particle decays from QCD jets and other background. Template overlaps are functional measures that…
3D generative modeling is accelerating as the technology allowing the capture of geometric data is developing. However, the acquired data is often inconsistent, resulting in unregistered meshes or point clouds. Many generative learning…
We present alphaPDE, a new multivariate analysis technique for parameter estimation. The method is based on a direct construction of joint probability densities of known variables and the parameters to be estimated. We show how posterior…
We analyze the energy spectra of $single$ b-jets and B-hadrons resulting from the production and decay of top quarks within the SM at the LHC at the NLO QCD. For both hadrons and jets, we calculate the correlation of the peak of the…
We present a procedure for the determination of the mass of the top quark at the LHC based on leptonic observables in dilepton $t\bar{t}$ events. Our approach utilises the shapes of kinematic distributions through their few lowest Mellin…
Unparticles are realized by deconstruction in higher extra dimensions. It is shown that in this framework when the scale invariance is broken, the corresponding spectral function of the unparticle is shifted by an amount of the breaking…
In the context of the Standard Model (SM) of particle physics, the relationship between the top-quark mass and width ($\Gamma_t$) has been precisely calculated. However, the uncertainty from current direct measurements of the width is…
The increasing realism of generated images has raised significant concerns about their potential misuse, necessitating robust detection methods. Current approaches mainly rely on training binary classifiers, which depend heavily on the…
When solving long-horizon tasks, it is intriguing to decompose the high-level task into subtasks. Decomposing experiences into reusable subtasks can improve data efficiency, accelerate policy generalization, and in general provide promising…