Related papers: Machine learning in top quark physics at ATLAS and…
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…
Precise reconstruction of top quark properties is a challenging task at the Large Hadron Collider due to combinatorial backgrounds and missing information. We introduce a physics-informed neural network architecture called the Covariant…
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…
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,…
The High-Luminosity LHC is expected to provide $3 ab^{-1}$ of integrated luminosity. As a result billions of events containing top quarks will be detected at the CMS and ATLAS experiments, allowing for precise measurements of the top quark…
Results from searches for new particles with enhanced couplings to third-generation quarks are presented. They are based on proton-proton collision data at a center-of-mass energy of 13 TeV recorded by the CMS experiment. The signatures…
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently…
Recent measurements of top-quark properties (excluding the top quark mass) at ATLAS and CMS are discussed. This includes latest updates of measurements of $t\bar{t}$ production asymmetries, spin correlations and $W$ helicity, as well as the…
Over the last years, machine learning tools have been successfully applied to a wealth of problems in high-energy physics. A typical example is the classification of physics objects. Supervised machine learning methods allow for significant…
Over the past years, machine learning has emerged as a powerful computational tool to tackle complex problems over a broad range of scientific disciplines. In particular, artificial neural networks have been successfully deployed to…
Machine learning algorithms are powerful tools for data driven tasks such as image classification and feature detection, however their vulnerability to adversarial examples - input samples manipulated to fool the algorithm - remains a…
Scientific progress is tightly coupled to the emergence of new research tools. Today, machine learning (ML)-especially deep learning (DL)-has become a transformative instrument for quantum science and technology. Owing to the intrinsic…
Machine learning (ML) has emerged into formidable force for identifying hidden but pertinent patterns within a given data set with the objective of subsequent generation of automated predictive behavior. In the recent years, it is safe to…
The top quark was discovered at the Tevatron in 1995. For the last decade the study of its properties has been a major theme in the worldwide experimental high energy physics program. The advent of the LHC opens up a new era in top quark…
Automated industries lead to high quality production, lower manufacturing cost and better utilization of human resources. Robotic manipulator arms have major role in the automation process. However, for complex manipulation tasks, hard…
These proceedings summarize the latest measurements on top production, top properties and searches using the ATLAS detector at the LHC. The measurements are performed on $pp$ collision data with a center of mass energy $\sqrt{s} = 7, 8$ and…
Recent searches for dark matter (DM) produced in association with top quarks from the ATLAS and CMS experiments using data collected between 2015 and 2018 are presented. These comprise searches from both experiments for DM in association…
These are lecture notes presented at the online 2020 Hadron Collider Physics Summer School hosted by Fermilab. These are an extension of lectures presented at the 2017 and 2018 CTEQ summer schools in arXiv:1709.06195 and still introduces…
A precise measurement of the top-Higgs coupling is essential in particle physics, as it offers a powerful probe of potential new physics beyond the Standard Model (BSM), particularly scenarios involving ${\cal CP}$ violation, which is a key…
Over the past five years, modern machine learning has been quietly revolutionizing particle physics. Old methodology is being outdated and entirely new ways of thinking about data are becoming commonplace. This article will review some…