Related papers: Machine Learning in Top Physics in the ATLAS and C…
The observation of the top quark will be an important milestone in ATLAS. This talk reviews methods that ATLAS plans to use to observe the top quark pair production process and measure its cross section.
This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum computers to reshape the landscape of machine learning. For…
There is a growing consensus that solutions to complex science and engineering problems require novel methodologies that are able to integrate traditional physics-based modeling approaches with state-of-the-art machine learning (ML)…
In this article, we review the application of modern machine-learning (ML) techniques to boost the search for processes involving the top quarks at the LHC. We revisit the formalism of Convolutional Neural Networks (CNNs), Graph Neural…
It has been shown that entanglement and Bell nonlocality, which are key concepts in Quantum Mechanics, can be probed in high-energy colliders via processes of fundamental particle scattering. In fact, the ATLAS and CMS collaborations have…
An exponential growth in computing power, which has brought more sophisticated and higher resolution simulations of the climate system, and an exponential increase in observations since the first weather satellite was put in orbit, are…
This note summarizes the various physics studies done for the LHC. It concentrates on the processes involving the production of high mass states. Results are drawn from simulations performed by the CMS and ATLAS collaborations. The ability…
The application of machine learning (ML) in high energy physics (HEP), specifically in heavy-flavor jet tagging at Large Hadron Collider (LHC) experiments, has experienced remarkable growth and innovation in the past decade. This review…
Recent results in soft QCD at LHC by the ATLAS, CMS and TOTEM Collaborations are presented. Special focus is reserved to studies in diffractive and forward physics, and to the underlying event, with comparison with previous results and…
Machine learning (ML) is a subfield of artificial intelligence. The term applies broadly to a collection of computational algorithms and techniques that train systems from raw data rather than a priori models. ML techniques are now…
This contribution describes the experience with the application of different Machine Learning (ML) techniques to a physics analysis case. The use case chosen is the classification of top-antitop events coming from BSM or from SM using data…
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical…
Machine learning (ML) has become a key tool in astronomy, driving advancements in the analysis and interpretation of complex datasets from observations. This article reviews the application of ML techniques in the identification and…
Machine learning algorithms have recently emerged as a tool to generate force fields which display accuracies approaching the ones of the ab-initio calculations they are trained on, but are much faster to compute. The enhanced computational…
The most recent studies in the top quark sector are reviewed with the focus on the rare production mechanisms and suppressed decays. The experimental results obtained with the ATLAS and CMS detectors in proton-proton collisions at the…
Searches for physics beyond the standard model are considered one of the corner stones of the physics program at the two multipurpose experiments, ATLAS and CMS, at the LHC. Both experiments have conducted numerous searches for new…
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.…
Cross section measurements of top quark production in association with a vector boson in proton-proton collisions at $\sqrt{s}=13\,\mathrm{TeV}$ at the CERN LHC provide an important probe of the electroweak top quark couplings. In this…
(Report of the Top Physics working group at the 1996 DPF/DPB Snowmass workshop, to appear in the proceedings). We discuss the study of the top quark at future experiments and machines. Top's large mass makes it a unique probe of physics at…
Advances in machine learning have impacted myriad areas of materials science, ranging from the discovery of novel materials to the improvement of molecular simulations, with likely many more important developments to come. Given the rapid…