相关论文: Ultra fast, event-by-event heavy-ion simulations f…

The simple method for simulation of ``thermal'' hadron spectra in ultrarelativistic heavy ion collisions including longitudinal, transverse and elliptic flow is developed. The model is realized as fast Monte-Carlo event generator.

We present a realistic treatment of the hydrodynamic evolution of ultrarelativistic heavy ion collisions, based on the following features: initial conditions obtained from a flux tube approach, compatible with the string model and the color…

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 propose a framework for studying predictability of extreme events in complex systems. Major conceptual elements -- direct cascading or fragmentation, spatial dynamics, and external driving -- are combined in a classical age-dependent…

We have developed a next-generation hybrid event-by-event three-fluid hydrodynamic model, suitable for simulations of heavy-ion collisions in the energy range from few up to tens of GeV per colliding NN pair. At such energies the…

We present results of simulations of light-nuclei production in relativistic heavy-ion collisions within updated Three-fluid Hydrodynamics-based Event Simulator Extended by UrQMD (Ultra-relativistic Quantum Molecular Dynamics) final State…

With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generative machine learning models enable fast event generation, yet…

Convolutional Neural Nets, which is a powerful method of Deep Learning, is applied to classify equation of state of heavy-ion collision event generated within the UrQMD model. Event-by-event transverse momentum and azimuthal angle…

Turbulent flows have historically presented formidable challenges to predictive computational modeling. Traditional numerical simulations often require vast computational resources, making them infeasible for numerous engineering…

Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating particle interactions with detectors is both time consuming and computationally expensive. With the proton-proton…

The computational intensity of detector simulation and event reconstruction poses a significant difficulty for data analysis in collider experiments. This challenge inspires the continued development of machine learning techniques to serve…

Dynamic contingency screening is a challenging task in dynamic security assessment, when traditional numerical approaches are computationally intensive and often not able to repeatedly solve full AC power flow for all possible contingencies…

We develop the first event generator, the electron-Heavy-Ion-Jet-INteraction-Generator (eHIJING), for the jet tomography study of electron-ion collisions. In this generator, energetic jet partons produced from the initial hard scattering…

We introduce an event-by-event perturbative-QCD + saturation + hydro ("EKRT") framework for ultrarelativistic heavy-ion collisions, where we compute the produced fluctuating QCD-matter energy densities from next-to-leading order…

We present a new technique to calculate the cross-section for diffractive vector meson production and DVCS in electron-ion collisions based on the dipole model. The measurement of these processes can provide valuable information on…

Transverse momentum spectra of identified particles produced in heavy-ion collisions at the Large Hadron Collider are described with relativistic fluid dynamics. We perform a systematic comparison of experimental data for pions, kaons and…

Inferences of the nuclear symmetry energy from heavy-ion collisions are currently based on the comparison of measured observables and transport model simulations. Only the expectation values of observables over all considered events are…

We demonstrate that a new type of analysis in heavy-ion collisions, based on an event-by-event analysis of the transverse momentum distribution, allows us to obtain information on secondary interactions and collective behaviour that is not…

Neural Temporal Point Processes (TPPs) have emerged as the primary framework for predicting sequences of events that occur at irregular time intervals, but their sequential nature can hamper performance for long-horizon forecasts. To…

In High Energy Physics, detailed and time-consuming simulations are used for particle interactions with detectors. To bypass these simulations with a generative model, the generation of large point clouds in a short time is required, while…