Related papers: Parameter Inference from Event Ensembles and the T…
The amount of data collected by the CMS experiment at the CERN LHC in the second data taking period provides the possibility to study absolute and differential cross sections of top quark interaction processes with high precision. Utilizing…
Recent work has provided the means to rigorously determine properties of super-hadronic matter from experimental data through the application of broad scale modeling of high-energy nuclear collisions within a Bayesian framework. These…
Scalable quantum technologies will present challenges for characterizing and tuning quantum devices. This is a time-consuming activity, and as the size of quantum systems increases, this task will become intractable without the aid of…
Precision determinations of the top-quark mass require theory predictions with a well-defined mass parameter in a given renormalization scheme. The top-quark's running mass in the MSbar scheme can be extracted with good precision from the…
Neural simulation-based inference is a powerful class of machine-learning-based methods for statistical inference that naturally handles high-dimensional parameter estimation without the need to bin data into low-dimensional summary…
The practice of collider physics typically involves the marginalization of multi-dimensional collider data to uni-dimensional observables relevant for some physics task. In any cases, such as classification or anomaly detection, the…
The physics impact of a precise determination of the top-quark mass, m_t, at the Linear Collider (LC) is discussed, and the results are compared with the prospective accuracy at the LHC. The importance of a precise knowledge of m_t for…
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…
A power constrained sensor network that consists of multiple sensor nodes and a fusion center (FC) is considered, where the goal is to estimate a random parameter of interest. In contrast to the distributed framework, the sensor nodes may…
We present a measurement of the top quark mass in the all-hadronic channel (\tt $\to$ \bb$q_{1}\bar{q_{2}}q_{3}\bar{q_{4}}$) using 943 pb$^{-1}$ of \ppbar collisions at $\sqrt {s} = 1.96$ TeV collected at the CDF II detector at Fermilab…
Event classifiers are the most fundamental observables to probe the event topology of hadronic and nuclear collisions at relativistic energies. Over the last five decades, significant progress has been made to establish suitable event…
The top quark is the heaviest elementary particle in the Standard Model (SM). For this reason, a precise determination of its mass $m_t$ is part of the LHC physics program. The most accurate determinations of $m_t$ rely on the kinematic…
Our predictions for particle physics processes are realized in a chain of complex simulators. They allow us to generate high-fidelity simulated data, but they are not well-suited for inference on the theory parameters with observed data. We…
The mass of the top quark is measured using top-antitop-quark pair events with high transverse momentum top quarks. The dataset, collected with the ATLAS detector in proton--proton collisions at $\sqrt{s}=13$ TeV delivered by the Large…
The mass of the top quark is measured using a sample of t t-bar candidate events with one electron or muon and at least four jets in the final state, collected by CMS in pp collisions at sqrt(s) = 7 TeV at the LHC. A total of 5174 candidate…
A likelihood-based reconstruction algorithm for arbitrary event topologies is introduced and, as an example, applied to the single-lepton decay mode of top-quark pair production. The algorithm comes with several options which further…
Numerical lattice quantum chromodynamics studies of the strong interaction are important in many aspects of particle and nuclear physics. Such studies require significant computing resources to undertake. A number of proposed methods…
The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as $top$ $tagging$, is a crucial component in many measurements of Standard Model processes and searches for beyond the…
Machine-learning-based methods can be developed for the reconstruction of clusters in segmented detectors for high energy physics experiments. Convolutional neural networks with autoencoder architecture trained on labeled data from a…
We demonstrate high prediction accuracy of three important properties that determine the initial geometry of the heavy-ion collision (HIC) experiments by using supervised Machine Learning (ML) methods. These properties are the impact…