Related papers: Machine Learning in Top Physics in the ATLAS and C…
Deep neural networks have rightfully won the place of one of the most accurate analysis tools in high energy physics. In this paper we will cover several methods of improving the performance of a deep neural network in a classification task…
At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and…
An array of large observational programs using ground-based and space-borne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver a sheer volume of data exceeding an exabyte. Processing the…
Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge…
Quantum machine learning is a rapidly growing field at the intersection of quantum technology and artificial intelligence. This review provides a two-fold overview of several key approaches that can offer advancements in both the…
The physics case for studying top-quark physics at the International Linear Collider is well established. This summary places in context the top-quark physics goals, examines the current state-of-the art in understanding of the top-quark…
The study of Standard Model (SM) physics is crucial at the LHC for several reasons. Before any discovery can be claimed a detailed understanding of the detectors should be reached and benchmark SM processes should be measured. A precise…
Machine learning in quantum computing and communication provides intensive opportunities for revolutionizing the field of Physics, Mathematics, and Computer Science. There exists an aperture of understanding behind this interdisciplinary…
Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. Given the fast pace of this research, we have created a living review with the goal of providing a…
The recent observation of entanglement between top and anti-top quarks at the LHC opens the way to interpreting collider data with quantum information tools. In this work we investigate the relevance of quantum observables in searches of…
Studies of the boosted sector in top-quark physics have known a fast-growing development with the arrival of high-energy data at LHC. This short review summarizes the current status of the boosted top-tagging techniques in ATLAS and CMS and…
The application of "machine learning" and "artificial intelligence" has become popular within the last decade. Both terms are frequently used in science and media, sometimes interchangeably, sometimes with different meanings. In this work,…
Machine Learning methods will play a fundamental role in our ability to optimize the science output from the next generation of large scale surveys. Given the peculiarities of astronomical data, it is crucial that algorithms are adapted to…
Deep learning, a branch of machine learning, have been recently applied to high energy experimental and phenomenological studies. In this note we give a brief review on those applications using supervised deep learning. We first describe…
Recent inclusive and differential cross section measurements of the associated production of top quark pairs with gauge bosons or heavy-flavor jets are reported. A search for physics beyond the standard model in the top quark sector is also…
Quantum Machine Learning (QML) is an exciting tool that has received significant recent attention due in part to advances in quantum computing hardware. While there is currently no formal guarantee that QML is superior to classical ML for…
Machine learning techniques have found their way into computational chemistry as indispensable tools to accelerate atomistic simulations and materials design. In addition, machine learning approaches hold the potential to boost the…
Quantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, materials science, statistical mechanics, quantum information,…
I review recent results from the ATLAS and CMS collaborations on the measurements of the top quark mass, its spin and decay properties.
In recent years, an interest in the study of quantum information has grown within the high-energy particle physics community. The possibility to establish the presence of entanglement at particle colliders, such as the Large Hadron Collider…