Related papers: Machine learning in top quark physics at ATLAS and…
We explore various aspects of top quark phenomenology at the Large Hadron Collider and proposed future machines. After summarising the role of the top quark in the Standard Model (and some of its well-known extensions), we discuss the…
Quantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, materials science, statistical mechanics, quantum information,…
The production of single top quarks allows to study the interplay of top quark physics and the electroweak sector of the standard model. Deviations from predictions can be a hint for physics beyond the standard model. The t-channel is the…
This report contains a brief summary of the latest single top quark production cross-section measurements performed by the ATLAS and CMS collaborations on pp collisions collected during Run 2 of the LHC. Various results for t-channel,…
The latest results from ATLAS and CMS on single top quark production and rare production channels of top quarks at the LHC are presented.
In recent years, the dramatic progress in machine learning has begun to impact many areas of science and technology significantly. In the present perspective article, we explore how quantum technologies are benefiting from this revolution.…
The latest results of the ATLAS and CMS experiments on top quark property measurements and interpretations in the EFT framework are presented. All measurements are based on the full LHC Run 2 dataset of approximately $138\,\textrm{fb}^{-1}$…
Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge…
Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the…
Top quark pair spin correlation measurements performed by the ATLAS experiment using $pp$ collisions at the CERN Large Hadron Collider are summarized. Moreover, the measurement of a specific observable $D$ related to top quark pair spin…
Rational design of compounds with specific properties requires conceptual understanding and fast evaluation of molecular properties throughout chemical compound space (CCS) -- the huge set of all potentially stable molecules. Recent…
Machine learning in quantum computing and communication provides intensive opportunities for revolutionizing the field of Physics, Mathematics, and Computer Science. There exists an aperture of understanding behind this interdisciplinary…
Deep learning, a branch of machine learning, have been recently applied to high energy experimental and phenomenological studies. In this note we give a brief review on those applications using supervised deep learning. We first describe…
As ultracold atom experiments become highly controlled and scalable quantum simulators, they require sophisticated control over high-dimensional parameter spaces and generate increasingly complex measurement data that need to be analyzed…
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy…
Numerical lattice quantum chromodynamics studies of the strong interaction are important in many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods…
We present the recent results of top-quark physics using up to 6 fb$^{-1}$ of $p\bar{p}$ collisions analyzed by the CDF collaboration. The large number of top quark events analyzed, of the order of several thousands, allows stringent checks…
Machine learning entails a broad range of techniques that have been widely used in Science and Engineering since decades. High-energy physics has also profited from the power of these tools for advanced analysis of colliders data. It is…
Machine learning (ML) is transforming all areas of science. The complex and time-consuming calculations in molecular simulations are particularly suitable for a machine learning revolution and have already been profoundly impacted by the…
Selected topics of the top-quark mass measurements in well-defined schemes are presented. The measurements have been performed using data recorded with the ATLAS and CMS detectors at the LHC at proton-proton centre-of-mass energies of 7 and…