Related papers: Parameter Inference from Event Ensembles and the T…
An ensemble method is introduced that utilizes randomization and loss function gradients to compute a prediction. Multiple weakly-correlated estimators approximate the gradient at randomly sampled points on the error surface and are…
Monte Carlo event generators are an essential tool for data analysis in collider physics. To include subleading quantum corrections, these generators often need to produce negative weight events, which leads to statistical dilution of the…
High-multiplicity signatures at particle colliders can arise in Standard Model processes and beyond. With such signatures, difficulties often arise from the large dimensionality of the kinematic space. For final states containing a single…
A determination of the top-quark mass is presented using 20.2 $\text{fb}^{-1}$ of 8 TeV proton-proton collision data produced by the Large Hadron Collider and collected by the ATLAS experiment. The normalised differential cross section of…
We present the calculation of the next-to-leading order QCD corrections for top-quark pair production in association with an additional jet at hadron colliders, using the modified minimal subtraction scheme to renormalize the top-quark…
One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies…
Constraints on models which predict resonant top-quark pair production at the LHC are provided via a reinterpretation of the Standard Model (SM) particle level measurement of the top-anti-top invariant mass distribution, $m(t\bar{t})$. We…
Many inverse problems in nuclear fusion and high-energy astrophysics research, such as the optimization of tokamak reactor geometries or the inference of black hole parameters from interferometric images, necessitate high-dimensional…
State-space models have been used in many applications, including econometrics, engineering, medical research, etc. The maximum likelihood estimation (MLE) of the static parameter of general state-space models is not straightforward because…
Quantum classifiers are trainable quantum circuits used as machine learning models. The first part of the circuit implements a quantum feature map that encodes classical inputs into quantum states, embedding the data in a high-dimensional…
We study the Monte Carlo uncertainties due to modeling of hadronization and showering in the extraction of the top-quark mass from observables that use exclusive hadronic final states in top decays, such as $t \rightarrow…
A measurement of the top quark mass is reported in events containing a single top quark produced via the electroweak t channel. The analysis is performed using data from proton-proton collisions collected with the CMS detector at the LHC at…
Monte Carlo simulations of physics processes at particle colliders like the Large Hadron Collider at CERN take up a major fraction of the computational budget. For some simulations, a single data point takes seconds, minutes, or even hours…
A new method of event characterization based on Deep Learning is presented. The PointNet models can be used for fast, online event-by-event impact parameter determination at the CBM experiment. For this study, UrQMD and the CBM detector…
We present a method to establish experimentally the relation between the top-quark mass $m_t^{MC}$ as implemented in Monte-Carlo generators and the Lagrangian mass parameter $m_t$ in a theoretically well-defined renormalization scheme. We…
The problem of heterogeneous clients in federated learning has recently drawn a lot of attention. Spectral model sharding, i.e., partitioning the model parameters into low-rank matrices based on the singular value decomposition, has been…
Theoretical issues are discussed for the measurement of the top-mass using jets, including perturbative and non-perturbative effects that relate experimental observables to the Lagrangian mass, and appropriate choices for mass schemes. Full…
The dependence of the top-quark mass measurement in top-quark production on the parton distribution functions is explored through the differential distribution of the invariant mass of the top-antitop system in top-quark pair production at…
Quantifying unknown quantum entanglement experimentally is a difficult task, but also becomes more and more necessary because of the fast development of quantum engineering. Machine learning provides practical solutions to this fundamental…
Weak-lensing mass-mapping algorithms, which reconstruct the convergence field from galaxy shear measurements, are crucial for extracting higher-order statistics to constrain cosmological parameters. However, only limited research has…