Related papers: Machine learning-based jet and event classificatio…
Energy-energy correlators (EECs) provide a sensitive probe of both perturbative and nonperturbative dynamics in relativistic heavy-ion collisions. Jet-medium interactions enhance particle multiplicity within the jet cone, which must be…
A fundamental characteristic of hadron colliders is the abundant production of jets, which then are studied to learn about hard QCD, the proton structure, or nonperturbative effects. In the following the latest results and developments from…
Tensor Networks, a numerical tool originally designed for simulating quantum many-body systems, have recently been applied to solve Machine Learning problems. Exploiting a tree tensor network, we apply a quantum-inspired machine learning…
The correct identification of charged hadrons plays a crucial role in flavor-physics measurements. The final detector configurations at the proposed Future Circular Collider are yet to be determined and this study aims to contribute to this…
Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The…
We present a systematic investigation of flavor-dependent jet quenching using energy-energy correlators (EEC) in $\sqrt{\rm s}=5.02$ TeV Pb+Pb collisions. Employing the improved SHELL model, which incorporates collisional and radiative…
In the past decade the observation of cross section modification for leading hadrons, heavy flavor and two particle correlations in heavy ion collisions has provided important insights into the dynamics of parton propagation in dense…
This talk reviews some key developments that have taken place in hadron-collider jet finding over the past couple of years, including: technical advances such as the complete formulation of an infrared safe seedless cone algorithm and fast…
Exciting results from the Relativistic Heavy Ion Collider (RHIC) have been presented at this Workshop. However, fundamental questions remain to be addressed in the future regarding whether the system is deconfined, chiral symmetry is…
I give a brief overview of the science cases of the Electron-Ion Collider (EIC) with a particular emphasis on the connections to the physics of ultrarelativistic heavy-ion collisions.
We investigate the flavor dependence of jet quenching, by performing a systematic analysis of medium modifications on the inclusive jet, $\gamma$+jet, and $b$-jet in Pb+Pb collisions at the LHC. Our results from MadGraph+PYTHIA exhibit…
We propose the lepton-jet correlation in deep inelastic scattering as a unique tool for nucleon/nucleus tomography at the electron-ion collider. The azimuthal angular correlation between the final state lepton and jet depends on the…
Machine learning-based jet classifiers are able to achieve impressive tagging performance in a variety of applications in high-energy and nuclear physics. However, it remains unclear in many cases which aspects of jets give rise to this…
The paper explores the feasibility of using machine learning techniques, in particular neural networks, for classification of the experimental data from the joint $^\text{nat}$C(n,p) and $^\text{nat}$C(n,d) reaction cross section…
This overview describes several science cases at the Electron-Ion-Collider (EIC) experiment which are traditional to general particle physics. It has an emphasis on connections between future measurements at the EIC and the physics topics…
Jet flavour classification is of paramount importance for a broad range of applications in modern-day high-energy-physics experiments, particularly at the LHC. In this paper we propose a novel architecture for this task that exploits modern…
Measurements of jet substructure in heavy-ion collisions may provide key insight to the nature of jet quenching in the quark-gluon plasma. Jet grooming techniques from high-energy physics have been applied to heavy-ion collisions in order…
Events with vector bosons produced in association with jets have been extensively studied at hadron colliders and provide high-accuracy tests of the Standard Model. A good understanding of these processes is of paramount importance for…
We review recent progress in the theoretical description of hard probes in heavy-ion collisions within the framework of Soft Collinear Effective Theory (SCET). Firstly, we consider the inclusive production of heavy flavor mesons and jets…
The identification and classification of collimated particle sprays, or jets, are essential for interpreting data from high-energy collider experiments. While deep learning has improved jet classification, it often lacks interpretability.…