اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدProbing criticality with deep learning in relativistic heavy-ion collisions
188
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Systems with different interactions could develop the same critical behaviour due to the underlying symmetry and universality. Using this principle of universality, we can embed critical correlations modeled on the 3D Ising model into the simulated data of heavy-ion collisions, hiding weak signals of a few inter-particle correlations within a large particle cloud. Employing a point cloud network with dynamical edge convolution, we are able to identify events with critical fluctuations through supervised learning, and pick out a large fraction of signal particles used for decision-making in each single event.
قيم البحث
اقرأ أيضاً
The momentum correlation functions of baryon pairs, which reflects the baryon-baryon interaction at low energies, are investigated for multi-strangeness pairs ($OmegaOmega$ and $NOmega$) produced in relativistic heavy-ion collisions. We calculate the
correlation functions based on an expanding source model constrained by single-particle distributions. The interaction potentials are taken from those obtained from recent lattice QCD calculations at nearly physical quark masses. Experimental measurements of these correlation functions for different system sizes will help to disentangle the strong interaction between baryons and to unravel the possible existence of strange dibaryons.
We study the formation of large hyper-fragments in relativistic heavy-ion collisions within two transport models, DCM and UrQMD. Our goal is to explore a new mechanism for the formation of strange nuclear systems via capture of hyperons by relatively
cold spectator matter produced in semi-peripheral collisions. We investigate basic characteristics of the produced hyper-spectators and evaluate the production probabilities of multi-strange systems. Advantages of the proposed mechanisms over an alternative coalescence mechanism are analysed. We also discuss how such systems can be detected taking into account the background of free hyperons. This investigation is important for the development of new experimental methods for producing hyper-nuclei in peripheral relativistic nucleus-nucleus collisions, which are now underway at GSI and are planned for the future FAIR and NICA facilities.
Heavy flavor probes are sensitive to the properties of the quark gluon plasma (QGP) produced in relativistic heavy-ion collisions. A huge amount of effort has been devoted to studying different aspects of the heavy-ion collisions using heavy flavor p
articles. In this work, we study the dynamics of heavy quark transport in the QGP medium using the rapidity dependence of heavy flavor observables. We calculate the nuclear modification of $text{B}$ and $text{D}$ meson spectra as well as spectra of leptons from heavy flavor decays in the rapidity range $[-4.0,4.0]$. We use an implementation of the improved Langevin equation with gluon radiation on top of a (3+1)-dimensional relativistic viscous hydrodynamical background for several collision setups. We find that the rapidity dependence of the heavy quark modification is determined by the interplay between the smaller size of the medium, which affects the path length of the heavy quarks, and the softer heavy quark initial production spectrum. We compare our results with available experimental data and present predictions for open heavy flavor meson $R_text{AA}$ at finite rapidity.
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 distributions of pr
otons are used to train a classifier. An overall accuracy of classification of 98% is reached for Au+Au events at $sqrt{s_{NN}} = 11$ GeV. Performance of classifiers, trained on events at different colliding energies, is investigated. Obtained results indicate extensive possibilities of application of Deep Learning methods to other problems in physics of heavy-ion collisions.
We investigate the behavior of low energy photons radiated by the deceleration processes of two colliding nuclei in relativistic heavy ion collisions using the Wigner function approach for electromagnetic radiation fields. The angular distribution re
veals the information of the initial geometric configurations. Such a property is reflected in the anisotropic parameter $v_{2}$, showing an increasing $v_{2}$ as energy decreases, which is a behavior qualitatively different from $v_{2}$ from hadrons produced in the collisions.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
