Nuclear Theory·nucl-th

Monday·April 24, 2017

9 papers—6 primary·3 cross-listed

01
Standardized Cumulants of Flow Harmonic Fluctuations
Navid Abbasi🇮🇷 · Davood Allahbakhshi🇮🇷 · Ali Davody🇮🇷 · Seyed Farid Taghavi🇮🇷
The distribution of flow harmonics in heavy ion experiment can be characterized by standardized cumulants. We first model the ellipticity and power parameters of the elliptic-power distribution by employing MC-Glauber model. Then we use the elliptic-power distribution together with the hydrodynamic linear response approximation to study the two dimensional standardized cumulants of elliptic and triangular flow ( $v_{2}$ and $v_{3}$ ) distribution. For the second harmonic, it turns out that finding two dimensional cumulants in terms of $2 q$ -particle correlation functions $c_{2} {2 q}$ is limited to the skewness. We also show that $c_{3} {2}$ , $c_{3} {4}$ , and $c_{3} {6}$ , are related to the second, fourth, and sixth standardized cumulants of the $v_{3}$ distribution, respectively. The cumulant $c_{n} {2 q}$ can be also written in terms of $v_{n} {2 q}$ . Specifically, $- (v_{3} {4} / v_{3} {2})^{4}$ turns out to be the kurtosis of the $v_{3}$ event-by-event fluctuation distribution. We introduce a new parametrization for the distribution $p (v_{3})$ with $v_{3} {2}$ , kurtosis and sixth-order standardized cumulant being its free parameters. Compared to the Gaussian distribution, it indicates a more accurate fit with experimental results. Finally, we compare the kurtosis obtained from simulation with that of extracted from experimental data for the $v_{3}$ distribution.
nucl-thhep-phPRC(2018)·21 citations
02
Assessing theoretical uncertainties in fission barriers of superheavy nuclei
S. E. Agbemava · A. V. Afanasjev · D. Ray · P. Ring
Theoretical uncertainties in the predictions of inner fission barrier heights in superheavy elements have been investigated in a systematic way for a set of state-of-the-art covariant energy density functionals which represent major classes of the functionals used in covariant density functional theory. They differ in basic model assumptions and fitting protocols. Both systematic and statistical uncertainties have been quantified where the former turn out to be larger. Systematic uncertainties are substantial in superheavy elements and their behavior as a function of proton and neutron numbers contains a large random component. The benchmarking of the functionals to the experimental data on fission barriers in the actinides allows to reduce the systematic theoretical uncertainties for the inner fission barriers of unknown superheavy elements. However, even then they on average increase on moving away from the region where benchmarking has been performed. In addition, a comparison with the results of non-relativistic approaches is performed in order to define full systematic theoretical uncertainties over the state-of-the-art models. Even for the models benchmarked in the actinides, the difference in the inner fission barrier height of some superheavy elements reaches $5 - 6$ MeV. This uncertainty in the fission barrier heights will translate into huge (many tens of the orders of magnitude) uncertainties in the spontaneous fission half-lives.
nucl-thPRC(2017)·58 citations
03
Analytical and numerical assessment of accuracy of the approximated nuclear symmetry energy in the Hartree-Fock theory
Y. Tsukioka · H. Nakada
The nuclear symmetry energy is defined by the second derivative of the energy per nucleon with respect to the proton-neutron asymmetry, and is sometimes approximated by the energy difference between the neutron matter and the symmetric matter. Accuracy of this approximation is assessed analytically and numerically, within the Hartree-Fock theory using effective interactions. By decomposing the nuclear-matter energy, the relative error of each term is expressed analytically; it is constant or is a single-variable function determined by the function type. The full errors are evaluated for several effective interactions, by inserting values of the parameters. Although the errors stay within $10%$ up to twice of the normal density irrespective of the interactions, at higher densities accuracy of the approximation significantly depends on the interactions.
nucl-thPTEP(2017)·4 citations
04
Imprint of Nucleon Radius on the Slowly Rotating Neutron Star Properties
Suparti · A. Sulaksono · T. Mart
We have studied the effect of free space nucleon radius on the nuclear matter and slowly rotating neutron star properties within a relativistic mean field model with the inclusion of omega-rho mixing nonlinear term. We use the same density dependent radius as in our previous work. However, in the present work we utilize the parameter sets with more proper symmetric nuclear matter (SNM) predictions for a number of different nucleon radii. To this end, the isoscalar parameters in each parameter set and the cutoff parameter $β$ are adjusted by fitting the corresponding SNM properties at sub-saturation density to the prediction of IUFSU parameter set. We have obtained that the assumption of composite nucleons with free space radius $r \leq 0.83$ fm is still compatible with the currently acceptable nuclear matter (NM) properties beyond the saturation density. We have also found that the effect of free space nucleon radius could be imprinted in the radius, moment of inertia, and crust properties of the slowly rotating neutron star.
nucl-th0 citations
05
Refining mass formulas for astrophysical applications: a Bayesian neural network approach
Raditya Utama · Jorge Piekarewicz
Exotic nuclei, particularly those near the driplines, are at the core of one of the fundamental questions driving nuclear structure and astrophysics today: what are the limits of nuclear binding? Exotic nuclei play a critical role in both informing theoretical models as well as in our understanding of the origin of the heavy elements. Our purpose is to refine existing mass models through the training of an artificial neural network that will mitigate the large model discrepancies far away from stability. The basic paradigm of our two-pronged approach is an existing mass model that captures as much as possible of the underlying physics followed by the implementation of a Bayesian Neural Network (BNN) refinement to account for the missing physics. Bayesian inference is employed to determine the parameters of the neural network so that model predictions may be accompanied by theoretical uncertainties. Despite the undeniable quality of the mass models adopted in this work, we observe a significant improvement (of about 40%) after the BNN refinement is implemented. Indeed, in the specific case of the Duflo-Zuker mass formula, we find that the rms deviation relative to experiment is reduced from rms =0.503MeV to rms=0.286 MeV. These newly refined mass tables are used to map the neutron drip lines (or rather "drip bands") and to study a few critical r-process nuclei. The BNN approach is highly successful in refining the predictions of existing mass models. In particular, the large discrepancy displayed by the original "bare" models in regions where experimental data is unavailable is considerably quenched after the BNN refinement. This lends credence to our approach and has motivated us to publish refined mass tables that we trust will be helpful for future astrophysical applications.
nucl-thastro-ph.SRnucl-exPRC(2017)·93 citations
06
Hydrodynamic modeling of heavy-ion collisions
Li Yan🇨🇦
This contribution presents a theoretical overview of hydrodynamic modelling of heavy-ion collisions, with highlights on some recent developments. In particular, the formulation of anisotropic hydrodynamics, the role of hydrodynamic fluctuations, and the non-linear coupling of flow coefficients will be discussed.
nucl-thhep-phnucl-exNPA(2017)·4 citations

Affiliations

first authorsco-authorsvia INSPIRE

Standardized Cumulants of Flow Harmonic Fluctuations

Assessing theoretical uncertainties in fission barriers of superheavy nuclei

Analytical and numerical assessment of accuracy of the approximated nuclear symmetry energy in the Hartree-Fock theory

Imprint of Nucleon Radius on the Slowly Rotating Neutron Star Properties

Refining mass formulas for astrophysical applications: a Bayesian neural network approach

Hydrodynamic modeling of heavy-ion collisions

Affiliations