Related papers: Machine learning in top quark physics at ATLAS and…
An overview of recent results on top quark properties and interactions is given, obtained using data collected with the CMS and ATLAS experiments during the years 2011 and 2012 at 7 TeV and 8 TeV centre-of-mass energies. Measurements of top…
Machine learning (ML) plays an increasingly important role in both online and offline event reconstruction and identification at CMS experiment. A variety of ML techniques are used to improve the identification of physics objects. Dedicated…
Latest results on inclusive top-quark pair production cross-sections are presented using collision data collected by ATLAS and CMS experiments at the LHC. Inclusive and differential measurements of top-quark pair production cross-sections…
This article gives an overview and a perspective of recent theoretical proposals and their experimental implementations in the field of quantum machine learning. Without an aim to being exhaustive, the article reviews specific high-impact…
Theoretical predictions for standard model (SM) processes involving top quarks, such as top quark-antiquark pair or single top production, depend on fundamental SM parameters like the strong coupling constant or the top quark mass. By…
Quantum computing applications are an emerging field in high-energy physics. Its ambitious fusion with artificial intelligence is expected to deliver significant efficiency gains over existing methods and/or enable computation from a…
The experimental systematic uncertainties associated to the reconstruction and calibration of the objects appearing in top quark final states at the LHC and Tevatron are discussed. The strategies followed in the ATLAS and CMS experiments…
This experimental review gives an overview of top-quark measurements performed by the two general purpose-detectors ATLAS and CMS during the first few years of running of the Large Hadron Collider. In the years 2010 - 2012 each experiment…
The identification and characterization of jets are crucial tasks for effectively probing fundamental particle interactions. The ATLAS and CMS experiments have developed cutting-edge techniques to improve jet identification and calibration,…
Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well…
Recent top quark properties measurements made with the CMS detector at the LHC are presented. The measurements summarized include spin correlation of top quark pairs, asymmetries, top quark mass, and the underlying event in top quark pair…
Recent measurements dedicated to improving the understanding of modelling top quark pair (${\text{t}\overline{\text{t}}}$) production at the LHC are summarised. These measurements, performed with proton-proton collision data collected by…
These proceedings highlight a selection of recent results by the ATLAS, CMS and LHCb collaborations. The majority of the featured analyses make use of the large set of $\sqrt{s}=13$ TeV proton-proton collision data collected during the…
Machine learning encompasses a set of tools and algorithms which are now becoming popular in almost all scientific and technological fields. This is true for molecular dynamics as well, where machine learning offers promises of extracting…
Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT…
This report describes the studies performed for the Snowmass "Top algorithms and detectors" High Energy Frontier Study Group.
Condensed Matter Physics (CMP) seeks to understand the microscopic interactions of matter at the quantum and atomistic levels, and describes how these interactions result in both mesoscopic and macroscopic properties. CMP overlaps with many…
Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be…
The top quark is the heaviest elementary particle known to date and therefore an important topic to study in the context of the standard model at the LHC. In this contribution the latest measurements of top quark production cross sections…
Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle physicists conduct searches and measurements as a versatile…