Related papers: Harnessing data-driven methods for precise model i…
The majority of Multi-Object Tracking (MOT) algorithms based on the tracking-by-detection scheme do not use higher order dependencies among objects or tracklets, which makes them less effective in handling complex scenarios. In this work,…
The choice of optimal event variables is crucial for achieving the maximal sensitivity of experimental analyses. Over time, physicists have derived suitable kinematic variables for many typical event topologies in collider physics. Here we…
In the pursuit of developing high-temperature alloys with improved properties for meeting the performance requirements of next-generation energy and aerospace demands, integrated computational materials engineering (ICME) has played a…
The model to simulate rescattering and partonic energy loss in ultrarelativistic heavy ion collisions is presented. The full heavy ion event is obtained as a superposition of a soft hydro-type state and hard multi-jets. This model is…
Results on the transverse spherocity dependence of light-flavor particle production ($\pi$, K, p, $\phi$, ${\rm K^{*0}}$, ${\rm K}^{0}_{\rm{S}}$, $\Lambda$, $\Xi$) at midrapidity in high-multiplicity pp collisions at $\sqrt{s} = 13$ TeV…
Simulating the dynamics of ions near polarizable nanoparticles (NPs) using coarse-grained models is extremely challenging due to the need to solve the Poisson equation at every simulation timestep. Recently, a molecular dynamics (MD) method…
At the Relativistic Heavy Ion Collider, jets have been a useful tool to probe the properties of the hot, dense matter created. At the Large Hadron Collider, collisions of Pb+Pb at $\sqrt{s_{NN}}$ = 5.5 TeV will provide a large cross section…
Supervised learning with a deep convolutional neural network is used to identify the QCD equation of state (EoS) employed in relativistic hydrodynamic simulations of heavy-ion collisions from the simulated final-state particle spectra…
Understanding the inner working of the quark-gluon plasma requires complete and precise jet substructure studies in heavy ion collisions. In this proceeding we discuss the use of quark and gluon jets as independent probes, and how their…
Recent developments in machine learning (ML) techniques present a promising new analysis method for high-speed imaging in astroparticle physics experiments, for example with imaging atmospheric Cherenkov telescopes (IACTs). In particular,…
After the start of the LHC, a plethora of novel observables for jet tomography in heavy-ion collisions has appeared. Many of these studies have yielded counter-intuitive null results of apparently unmodified jets, which have sparked…
The path-length dependence of jet quenching can help to constrain different jet quenching mechanisms in heavy-ion collisions. However, measuring an explicit value for this dependence has proven challenging. Traditional approaches, which…
Recent jet and jet substructure measurements at the LHC, and of machine-learning-based tagging techniques are presented using proton-proton collision data collected by the ATLAS and CMS experiments at CERN's Large Hadron Collider. These…
A model-independent search for deviations from the Standard Model prediction has been performed in e+p and e-p collisions at HERA using H1 data. All experimentally measurable event topologies involving isolated electrons, photons, muons,…
RHIC data opens new ways of characterizing the medium created in a heavy ion collision by measuring particles with high transverse momentum. In addition to the observation of the predicted jet quenching, the new data on particle…
A transport model for ultra-relativistic nucleus-nucleus collisions based on the mean free path approach is proposed. The method is manifestly Lorentz invariant. We discuss some calculations for pp and AA collisions and compare to a…
This study examines the feasibility of applying large language models (LLMs) for forecasting the impact of traffic incidents on the traffic flow. The use of LLMs for this task has several advantages over existing machine learning-based…
In this paper, we propose a method for numerical modeling of the nuclear matter properties within the framework of relativistic heavy-ion collisions using a holographic equation of state. Machine learning methods were applied to address the…
Analyses in high energy physics aim to put the Standard Model---the commonly accepted theory---to test. For convincing conclusions, analysis methods are needed which offer an unambiguous comparison between data and theory while allowing…
Multi-particle correlation techniques are frequently used to study jet shapes and yields in hadronic and nuclear collisions. To date, a standard assumption applied in such analyses is that the observed correlations arise from either jets…