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Nuclear Theory·nucl-th

Thursday·August 11, 2016

6 papers—4 primary·2 cross-listed

All6Primary4Cross-lists2
  1. 01

    Hydrodynamic Predictions for Mixed Harmonic Correlations in 200 GeV Au+Au Collisions

    Fernando G. Gardim🇧🇷 · Frederique Grassi🇧🇷 · Matthew Luzum🇧🇷 · Jacquelyn Noronha-Hostler🇺🇸

    Recent measurements at the LHC involve the correlation of different azimuthal flow harmonics . These new observables add constraints to theoretical models and probe aspects of the system that are independent of the traditional single-harmonic measurements such as 2- and multi-particle cumulants . Many of these new observables have not yet been measured at RHIC, leaving an opportunity to make predictions as a test of models across energies. We make predictions using NeXSPheRIO, a hydrodynamical model which has accurately reproduced a large set of single-harmonic correlations in a large range of transverse momenta and centralities at RHIC. Our predictions thus provide an important baseline for comparison to correlations of flow harmonics, which contain non-trivial information about the initial state as well as QGP transport properties. We also point out significant biases that can appear when using wide centrality bins and non-trivial event weighting, necessitating care in performing experimental analyses and in comparing theoretical calculations to these measurements.

    nucl-thhep-phPRC(2017)·52 citations
  2. 02

    Nuclear charge radii: Density functional theory meets Bayesian neural networks

    Raditya Utama · Wei-Chia Chen · Jorge Piekarewicz

    The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. We explore a novel approach that combines sophisticated models of nuclear structure with Bayesian neural networks (BNN) to generate predictions for the charge radii of thousands of nuclei throughout the nuclear chart. A class of relativistic energy density functionals is used to provide robust predictions for nuclear charge radii. In turn, these predictions are refined through Bayesian learning for a neural network that is trained using residuals between theoretical predictions and the experimental data. Although predictions obtained with density functional theory provide a fairly good description of experiment, our results show significant improvement (better than 40%) after BNN refinement. Moreover, these improved results for nuclear charge radii are supplemented with theoretical error bars. We have successfully demonstrated the ability of the BNN approach to significantly increase the accuracy of nuclear models in the predictions of nuclear charge radii. However, as many before us, we failed to uncover the underlying physics behind the intriguing behavior of charge radii along the calcium isotopic chain.

    nucl-thnucl-exJ.Phys.G(2016)·136 citations
  3. 03

    The radiative proton capture on 16O at astrophysical energies

    Sergey Dubovichenko · Albert Dzhazairov-Kakhramanov

    The possibility of description of the experimental data for the astrophysical S-factor of the proton radiative capture on 16O to the ground and first excited states of 17F was considered in the frame of the modified potential cluster model with forbidden states and classification of the states according to Young tableaux. It was shown that on the basis of the E1 transitions from the P states of p16O scattering to the bound states of 17F in the p16O channel, it generally succeed to explain the value of measured astrophysical S-factors and reaction rates at astrophysical energies.

    nucl-thIndian Journal of Physics 2018. V.92. P.9…·1 citation
  4. 04

    Event-shape-engineering study of charge separation in heavy-ion collisions

    Fufang Wen🇺🇸 · Jacob Bryon🇺🇸 · Liwen Wen🇺🇸 · Gang Wang🇺🇸

    Recent measurements of charge-dependent azimuthal correlations in high-energy heavy-ion collisions have indicated charge-separation signals perpendicular to the reaction plane, and have been related to the chiral magnetic effect (CME). However, the correlation signal is contaminated with the background caused by the collective motion (flow) of the collision system, and an effective approach is needed to remove the flow background from the correlation. We present a method study with simplified Monte Carlo simulations and a multi-phase transport model, and develop a scheme to reveal the true CME signal via the event-shape engineering with the flow vector of the particles of interest.

    nucl-thhep-phCPC(2018)·42 citations

Affiliations

first authorsco-authorsvia INSPIRE