Related papers: Machine Learning in Top Physics in the ATLAS and C…
Machine learning is poised as a very powerful tool that can drastically improve our ability to carry out scientific research. However, many issues need to be addressed before this becomes a reality. This article focuses on one particular…
Recent years have witnessed the fast development of machine learning potentials (MLPs) and their widespread applications in chemistry, physics, and material science. By fitting discrete ab initio data faithfully to continuous and…
The many ways in which machine and deep learning are transforming the analysis and simulation of data in particle physics are reviewed. The main methods based on boosted decision trees and various types of neural networks are introduced,…
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 large ttbar production cross-section at the LHC suggests the use of top quark decays to calibrate several critical parts of the detectors, such as the trigger system, the jet energy scale and b-tagging.
The top-quark mass measurements carried out by the LHC experiments, ATLAS and CMS, are summarized. Results of different approaches to the top-quark mass reconstruction are presented. Masses from different measurements are in good agreement…
A set of measurements of top quark pair and single top quark production in association with standard model (SM) bosons performed by the CMS and ATLAS Collaborations is presented. The consistency of these results among themselves and with…
Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first principles based virtual sampling of this space, for example…
Machine learning is increasingly transforming various scientific fields, enabled by advancements in computational power and access to large data sets from experiments and simulations. As artificial intelligence (AI) continues to grow in…
There is great potential to apply machine learning in the area of numerical lattice quantum field theory, but full exploitation of that potential will require new strategies. In this white paper for the Snowmass community planning process,…
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…
The integration of machine learning (ML) in cyber physical systems (CPS) is a complex task due to the challenges that arise in terms of real-time decision making, safety, reliability, device heterogeneity, and data privacy. There are also…
Measurements of top quark properties performed at the Large Hadron Collider are reviewed, with a particular emphasis on top-pair charge asymmetries, spin correlations and polarization measurements performed by the ATLAS and CMS…
Machine learning is finding increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated,…
The precise reconstruction of properties of photons and electrons in modern high energy physics detectors, such as the CMS or Atlas experiments, plays a crucial role in numerous physics results. Conventional geometrical algorithms are used…
Quantum machine learning (QML) is rapidly transitioning from theoretical promise to practical relevance across data-intensive scientific domains. In this Review, we provide a structured overview of recent advances that bridge foundational…
The areas of machine learning and communication technology are converging. Today's communications systems generate a huge amount of traffic data, which can help to significantly enhance the design and management of networks and…
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…
An overview of recent searches with top quarks in the final state using up to 36 fb$^{-1}$ of pp collision data at $\sqrt{s}$ = 13 TeV collected with the ATLAS and CMS experiments at the LHC is presented. In particular, searches for heavy…
Machine learning is a rapidly growing field with the potential to revolutionize many areas of science, including physics. This review provides a brief overview of machine learning in physics, covering the main concepts of supervised,…