Related papers: Machine Learning in Top Physics in the ATLAS and C…
The most recent results on top-quark physics reported by the CMS experiment at the Large Hadron Collider (LHC) are presented in this talk. The results are based on a data sample of about 36/pb of data collected during 2010 at a pp…
Top quarks can be produced abundantly at hadron colliders like the Tevatron at Fermilab and the Large Hadron Collider at CERN, and a variety of measurements of top-quark properties have been gathered in the recent years from four…
We present recent measurements of the top quark mass by the ATLAS and CMS experiments in the $\mathrm{t}\overline{\mathrm{t}}$ lepton+jets, all-hadronic, and dilepton channels. In addition, we present a measurement using a topology enriched…
Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding, datasets have become enormous, and the memory and CPU requirements for classical simulation of generic quantum systems often scale…
Searches for supersymmetric partner particles of the top and bottom quarks at the Large Hadron Collider are reviewed. The focus is on the status of searches for a relatively light partner of the top quark performed by the CMS and ATLAS…
First principles based exploration of chemical space deepens our understanding of chemistry, and might help with the design of new materials or experiments. Due to the computational cost of quantum chemistry methods and the immens number of…
Machine-learning models in high-energy physics are often trained on simulated data, where fully simulated samples are computationally expensive while fast simulation provides large statistics at reduced realism. In this work, we…
The application of machine learning (ML) methods to the analysis of astrophysical datasets is on the rise, particularly as the computing power and complex algorithms become more powerful and accessible. As the field of ML enjoys a…
We discuss the emerging advances and opportunities at the intersection of machine learning (ML) and climate physics, highlighting the use of ML techniques, including supervised, unsupervised, and equation discovery, to accelerate climate…
Measurements of the top quark by the ATLAS and CMS experiments go beyond testing the Standard Model (SM) with high precision. Axion-like particles (ALPs), a potential SM extension involving new pseudoscalar particles, exhibit strong…
Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily…
With data collected during the first half of 2011 pp run of the Large Hadron Collider at \surds = 7 TeV, a substantial data sample of high p_T triggers, 1.08/fb, has been collected by the ATLAS detector. Measurements of the production of…
Automated analyses of the outcome of a simulation have been an important part of atomistic modeling since the early days, addressing the need of linking the behavior of individual atoms and the collective properties that are usually the…
Quantum Machine Learning(QML) is developed by combining quantum mechanics principles with classical machine learning techniques in a hybrid framework that can give faster, exponential, more efficient power of quantum computing with the data…
Astronomy is experiencing a rapid growth in data size and complexity. This change fosters the development of data-driven science as a useful companion to the common model-driven data analysis paradigm, where astronomers develop automatic…
Recent advances in machine-learning interatomic potentials have enabled the efficient modeling of complex atomistic systems with an accuracy that is comparable to that of conventional quantum mechanics based methods. At the same time, the…
With the continuous breakthroughs in core technology, the dawn of large-scale integration of robotic systems into daily human life is on the horizon. Multi-robot systems (MRS) built on this foundation are undergoing drastic evolution. The…
The development of quantum technologies relies on creating and manipulating quantum systems of increasing complexity, with key applications in computation, simulation, and sensing. This poses severe challenges in efficient control,…
In this chapter, we discuss recent advances and new opportunities through methods of machine learning for the field of classical density functional theory, dealing with the equilibrium properties of thermal nano- and micro-particle systems…
The ATLAS and CMS Collaborations at the CERN Large Hadron Collider (LHC) have devoted considerable effort to the study of SUSY signatures and measurements. This talk provides an overview of what can be learned at the LHC if TeV-scale SUSY…