اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدUniversal scaling of the sigma field and net-protons from Langevin dynamics of model A
50
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
In this paper, we investigate the Kibble-Zurek scaling of the sigma field and net-protons within the framework of Langevin dynamics of model A. After determining the characteristic scales $tau_{kz},l_{kz}$ and $theta_{kz}$ and properly rescaling the traditional cumulants, we construct universal functions for the sigma field and approximate universal functions for net-protons in the critical regime, which are insensitive to the relaxation time and the chosen evolving trajectory. Besides, the oscillating behavior for the higher order cumulants of net-protons near the critical point is also drastically suppressed, which converge into approximate universal curves with these constructed Kibble-Zurek functions.
قيم البحث
اقرأ أيضاً
A short review of simulation results of anti-proton-proton and anti-proton-nucleus interactions within the framework of Geant4 FTF (Fritiof) model is presented. The model uses the main assumptions of the Quark-Gluon-String Model or Dual Parton Model.
The model assumes production and fragmentation of quark-anti-quark and diquark-anti-diquark strings in the mentioned interactions. Key ingredients of the model are cross sections of string creation processes and an usage of the LUND string fragmentation algorithm. They allow one to satisfactory describe a large set of experimental data, especially, a strange particle production, Lambda hyperons and K mesons.
Macroscopic parameters as well as precise information on the random force characterizing the Langevin type description of the nuclear fusion process around the Coulomb barrier are extracted from the microscopic dynamics of individual nucleons by expl
oiting the numerical simulation of the improved quantum molecular dynamics. It turns out that the dissipation dynamics of the relative motion between two fusing nuclei is caused by a non-Gaussian distribution of the random force. We find that the friction coefficient as well as the time correlation function of the random force takes particularly large values in a region a little bit inside of the Coulomb barrier. A clear non-Markovian effect is observed in the time correlation function of the random force. It is further shown that an emergent dynamics of the fusion process can be described by the generalized Langevin equation with memory effects by appropriately incorporating the microscopic information of individual nucleons through the random force and its time correlation function.
Thermodynamic uncertainty relation (TUR) provides a stricter bound for entropy production (EP) than that of the thermodynamic second law. This stricter bound can be utilized to infer the EP and derive other trade-off relations. Though the validity of
the TUR has been verified in various stochastic systems, its application to general Langevin dynamics has not been successful in a unified way, especially for underdamped Langevin dynamics, where odd parity variables in time-reversal operation such as velocity get involved. Previous TURs for underdamped Langevin dynamics is neither experimentally accessible nor reduced to the original form of the overdamped Langevin dynamics in the zero-mass limit. Here, we find an operationally accessible TUR for underdamped Langevin dynamics with an arbitrary time-dependent protocol. We show that the original TUR is a consequence of our underdamped TUR in the zero-mass limit. This indicates that the TUR formulation presented here can be regarded as the universal form of the TUR for general Langevin dynamics. The validity of our result is examined and confirmed for three prototypical underdamped Langevin systems and their zero-mass limits; free diffusion dynamics, charged Brownian particle in a magnetic field, and molecular refrigerator.
In this proceeding, the deep Convolutional Neural Networks (CNNs) are deployed to recognize the order of QCD phase transition and predict the dynamical parameters in Langevin processes. To overcome the intrinsic randomness existed in a stochastic pro
cess, we treat the final spectra as image-type inputs which preserve sufficient spatiotemporal correlations. As a practical example, we demonstrate this paradigm for the scalar condensation in QCD matter near the critical point, in which the order parameter of chiral phase transition can be characterized in a $1+1$-dimensional Langevin equation for $sigma$ field. The well-trained CNNs accurately classify the first-order phase transition and crossover from $sigma$ field configurations with fluctuations, in which the noise does not impair the performance of the recognition. In reconstructing the dynamics, we demonstrate it is robust to extract the damping coefficients $eta$ from the intricate field configurations.
We study the long time behavior of an underdamped mean-field Langevin (MFL) equation, and provide a general convergence as well as an exponential convergence rate result under different conditions. The results on the MFL equation can be applied to st
udy the convergence of the Hamiltonian gradient descent algorithm for the overparametrized optimization. We then provide a numerical example of the algorithm to train a generative adversarial networks (GAN).
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
