Related papers: Machine Learning in Top Physics in the ATLAS and C…
After the discovery of the top quark more than 20 years ago, its properties have been studied in great detail both in production and in decay. Increasingly sophisticated experimental results from the Fermilab Tevatron and from Run 1 and Run…
The recent progresses in Machine Learning opened the door to actual applications of learning algorithms but also to new research directions both in the field of Machine Learning directly and, at the edges with other disciplines. The case…
An essential component of the long-term success of scientific research is communicating the methodology and significance of new results to the wider public. Utilising various social media platforms is a vital tool for this endeavour. Over…
The production of top quark pairs is one of the most relevant production modes at the LHC, and allow for precise measurement of the properties of this particle. Top quarks are also produced through rarer mechanisms, including the production…
Machine Learning is a powerful tool for astrophysicists, which has already had significant uptake in the community. But there remain some barriers to entry, relating to proper understanding, the difficulty of interpretability, and the lack…
Machine Learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial task, in particular when the details of the underlying dynamics…
The marriage of machine learning and quantum physics may give birth to a new research frontier that could transform both.
This paper describes the top quark physics measurements that can be performed with the first LHC data in the ATLAS and CMS experiments.
With the large data set delivered during the second run of the CERN LHC, inclusive and differential measurements of the top quark-antiquark production cross section at the ATLAS and CMS experiments often reach a precision that is comparable…
The ATLAS and CMS experiments are unique drivers of our fundamental understanding of nature at the energy frontier. In this contribution to the update of the European Strategy for Particle Physics, we update the physics reach of these…
Over the past years, machine learning has emerged as a powerful computational tool to tackle complex problems over a broad range of scientific disciplines. In particular, artificial neural networks have been successfully deployed to…
Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace as a tool to predict energy, forces and properties of…
Recently supervised machine learning has been ascending in providing new predictive approaches for chemical, biological and materials sciences applications. In this Perspective we focus on the interplay of machine learning algorithm with…
In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning…
Over the past decade inter-atomic potentials based on machine-learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure…
Twenty years past its discovery, the top quark continues attracting great interest as experiments keep unveiling its properties. An overview of the latest measurements in the domain of top quark production, performed by the ATLAS and CMS…
The Large Hadron Collider LHC is a top quark factory: due to its high design luminosity, LHC will produce about 200 millions of top quarks per year of operation. The large amount of data will allow to study with great precision the…
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…
The impact of Machine Learning (ML) algorithms in the age of big data and platform capitalism has not spared scientific research in academia. In this work, we will analyse the use of ML in fundamental physics and its relationship to other…
This review summarizes the highlights in the area of top quark physics obtained with the two general purpose detectors ATLAS and CMS during the first two years of operation of the Large Hadron Collider LHC. It covers the 2010 and 2011 data…