Related papers: Machine Learning in Top Physics in the ATLAS and C…
The popularity of Machine Learning (ML) has been increasing in the last decades in almost every area, being the commercial and scientific fields the most notorious ones. Concerning particle physics, ML has been proved as a useful resource…
Machine learning has become an essential tool in jet physics. Due to their complex, high-dimensional nature, jets can be explored holistically by neural networks in ways that are not possible manually. However, innovations in all areas of…
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…
Machine learning has had an enormous impact in many scientific disciplines. Also in the field of low-temperature plasma modeling and simulation it has attracted significant interest within the past years. Whereas its application should be…
Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate…
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…
Machine learning (ML) techniques have been applied with tremendous success in many areas of physics. In this work, we use ML to place bounds on the coupling between photons and axion-like particles (ALPs). This coupling causes ALPs and…
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range…
A number of scientific competitions have been organised in the last few years with the objective of discovering innovative techniques to perform typical High Energy Physics tasks, like event reconstruction, classification and new physics…
The study of particle correlations is an important instrument to understand the nature of relativistic heavy ion collisions. Using a wealth of new data available from the recent heavy ion runs of Large Hadron Collider at CERN it becomes…
Quantum information technologies, and intelligent learning systems, are both emergent technologies that will likely have a transforming impact on our society. The respective underlying fields of research -- quantum information (QI) versus…
Modern machine learning is driving a paradigm shift in particle physics phenomenology at the Large Hadron Collider. This short review examines the transformative role of machine learning across the entire theoretical prediction pipeline,…
Several recent new measurements of the top-quark mass that use alternative observables and reconstruction techniques are presented, performed by the ATLAS and CMS collaborations at the CERN LHC. Alternative observables can help provide…
Machine learning has found its way into almost every area of science and engineering, and we are only at the beginning of its exploration across fields. Being a popular, versatile and powerful framework, machine learning has proven most…
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…
The production of top quarks through single or rare production modes has become important due to the large amount of data collected by both ATLAS and CMS at the LHC. Many searches are now studying these processes either as a targeted signal…
Many models beyond the standard model predict the existence of vector-like quarks or other types of heavy resonances. Using proton-proton collision data at center-of-mass energies of 8 and 13 TeV, the ATLAS and CMS Collaborations have…
The latest results of high mass searches for new physics in a variety of final states from the CMS and ATLAS collaborations are presented. These searches are based on $\sqrt{s}=13$ TeV proton-proton collisions data at the LHC collected in…
Machine learning (ML) algorithms, particularly attention-based transformer models, have become indispensable for analyzing the vast data generated by particle physics experiments like ATLAS and CMS at the CERN LHC. Particle Transformer…
Machine learning has rose to become an important research tool in the past decade, its application has been expanded to almost if not all disciplines known to mankind. Particularly, the use of machine learning in astrophysics research had a…