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Register a new userLearning Langevin dynamics with QCD phase transition
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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 process, 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.
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The machine-learning techniques have shown their capability for studying phase transitions in condensed matter physics. Here, we employ the machine-learning techniques to study the nuclear liquid-gas phase transition. We adopt an unsupervised learning and classify the liquid and gas phases of nuclei directly from the final state raw experimental data of heavy-ion reactions. Based on a confusion scheme which combines the supervised and unsupervised learning, we obtain the limiting temperature of the nuclear liquid-gas phase transition. Its value $9.24pm0.04~rm MeV$ is consistent with that obtained by the traditional caloric curve method. Our study explores the paradigm of combining the machine-learning techniques with heavy-ion experimental data, and it is also instructive for studying the phase transition of other uncontrollable systems, like QCD matter.
We describe the Bayesian Analysis of Nuclear Dynamics (BAND) framework, a cyberinfrastructure that we are developing which will unify the treatment of nuclear models, experimental data, and associated uncertainties. We overview the statistical principles and nuclear-physics contexts underlying the BAND toolset, with an emphasis on Bayesian methodologys ability to leverage insight from multiple models. In order to facilitate understanding of these tools we provide a simple and accessible example of the BAND frameworks application. Four case studies are presented to highlight how elements of the framework will enable progress on complex, far-ranging problems in nuclear physics. By collecting notation and terminology, providing illustrative examples, and giving an overview of the associated techniques, this paper aims to open paths through which the nuclear physics and statistics communities can contribute to and build upon the BAND framework.
We make ab initio predictions for the A = 6 nuclear level scheme based on two- and three-nucleon interactions up to next-to-next-to-leading order in chiral effective field theory ($chi$EFT). We utilize eigenvector continuation and Bayesian methods to quantify uncertainties stemming from the many-body method, the $chi$EFT truncation, and the low-energy constants of the nuclear interaction. The construction and validation of emulators is made possible via the development of JupiterNCSM -- a new M-scheme no-core shell model code that uses on-the-fly Hamiltonian matrix construction for efficient, single-node computations up to $N_mathrm{max} = 10$ for ${}^{6}mathrm{Li}$. We find a slight underbinding of ${}^{6}mathrm{He}$ and ${}^{6}mathrm{Li}$, although consistent with experimental data given our theoretical error bars. As a result of incorporating a correlated $chi$EFT-truncation errors we find more precise predictions (smaller error bars) for separation energies: $S_d({}^{6}mathrm{Li}) = 0.89 pm 0.44$ MeV, $S_{2n}({}^{6}mathrm{He}) = 0.20 pm 0.60$ MeV, and for the beta decay Q-value: $Q_{beta^-}({}^{6}mathrm{He}) = 3.71 pm 0.65$ MeV. We conclude that our error bars can potentially be reduced further by extending the model space used by JupiterNCSM.
In high multiplicity nucleus-nucleus collisions baryon-antibaryon annihilation and regeneration occur during the final hadronic expansion phase, thus distorting the initial equilibrium multiplicity ratios. We quantify the modifications employing the hybrid UrQMD transport model and apply them to the grand canonical partition functions of the Statistical Hadronization Model(SHM). We analyze minimum bias and central Pb+Pb collision data at SPS and LHC energy. We explain the Pion to Proton ratio puzzle. We also reproduce the deuteron to proton ratio at LHC energy by the SHM, and by UrQMD after attaching a phase space coalescence process. We discuss the resulting (T,$mu_{B}$) diagram.
An abnormal production of events with almost equal-sized fragments was theoretically proposed as a signature of spinodal instabilities responsible for nuclear multifragmentation in the Fermi energy domain. On the other hand finite size effects are predicted to strongly reduce this abnormal production. High statistics quasifusion hot nuclei produced in central collisions between Xe and Sn isotopes at 32 and 45 AMeV incident energies have been used to definitively establish, through the experimental measurement of charge correlations, the presence of spinodal instabilities. N/Z influence was also studied.
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