Thu·Aug 27, 2020Thursday·August 27, 2020

1. 01*

   Can a protophobic vector boson explain the ATOMKI anomaly?

   Xilin Zhang🇺🇸 · Gerald A. Miller🇺🇸

   In 2016, the ATOMKI collaboration announced [PRL {\bf 116}, 042501 (2016)] observing an unexpected enhancement of the $e^{+}-e^{-}$ pair production signal in one of the ${}^8$Be nuclear transitions induced by an incident proton beam on a ${}^7$Li target. Many beyond-standard-model physics explanations have subsequently been proposed. One popular theory is that the anomaly is caused by the creation of a protophobic vector boson ($X$) with a mass around 17 MeV [e.g., PRL\ {\bf 117}, 071803 (2016)] in the nuclear transition. We study this hypothesis by deriving an isospin relation between photon and $X$ couplings to nucleons. This allows us to find simple relations between protophobic $X$-production cross sections and those for measured photon production. The net result is that $X$ production is dominated by direct transitions induced by $E{1}^X$ and $L{1}^X$ (transverse and longitudinal electric dipoles) and $C{1}^X$ (charge dipole) without going through any nuclear resonance (i.e. Bremsstrahlung radiation) with a smooth energy dependence that occurs for all proton beam energies above threshold. This contradicts the experimental observations and invalidates the protophobic vector boson explanation.

   ↳ hep-phhep-exnucl-exnucl-thPLB(2021)·47 citations
2. 02*

   Curvature-slope correlation of nuclear symmetry energy and its imprints on the crust-core transition, radius and tidal deformability of canonical neutron stars

   Bao-An Li🇺🇸 · Macon Magno

   Background: The nuclear symmetry energy $E_{sym}(\rho )$ encodes information about the energy necessary to make nuclear systems more neutron-rich. While its slope parameter L at the saturation density ${\rho }_0$ of nuclear matter has been relatively well constrained by recent astrophysical observations and terrestrial nuclear experiments, its curvature $K_{\mathrm{sym}}$ characterizing the $E_{sym}(\rho )$ around $2{\rho }_0$ remains largely unconstrained. Over 520 calculations for $E_{sym}(\rho )$ using various nuclear theories and interactions in the literature have predicted several significantly different $K_{\mathrm{sym}}-L$ correlations. Purpose: If a unique $K_{\mathrm{sym}}-L$ correlation of $E_{sym}(\rho )$ can be firmly established, it will enable us to progressively better constrain the high-density behavior of $E_{sym}(\rho )$ using the available constraints on its slope parameter L. We investigate if and by how much the different $K_{\mathrm{sym}}-L$ correlations may affect neutron star observables. Method: A meta-model of nuclear Equation of States (EOSs) with three representative $K_{\mathrm{sym}}-L$ correlation functions is used to generate multiple EOSs for neutron stars. We then examine effects of the $K_{\mathrm{sym}}-L$ correlation on the crust-core transition density and pressure as well as the radius and tidal deformation of canonical neutron stars. Results:The $K_{\mathrm{sym}}-L$ correlation affects significantly both the crust-core transition density and pressure. It also has strong imprints on the radius and tidal deformability of canonical neutron stars especially at small L values. The available data from LIGO/VIRGO and NICER set some useful limits for the slope L but can not distinguish the three representative $K_{\mathrm{sym}}-L$ correlations considered.

   ↳ nucl-thastro-ph.HEastro-ph.SRnucl-exPRC(2020)·62 citations
3. 03*

   Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies

   Fupeng Li🇨🇳 · Yongjia Wang🇨🇳 · Hongliang Lü🇨🇳 · Pengcheng Li🇨🇳 · Qingfeng Li🇨🇳 · Fanxin Liu🇨🇳

   The impact parameter is one of the crucial physical quantities of heavy-ion collisions (HICs), and can affect obviously many observables at the final state, such as the multifragmentation and the collective flow. Usually, it cannot be measured directly in experiments but might be inferred from observables at the final state. Artificial intelligence has had great success in learning complex representations of data, which enables novel modeling and data processing approaches in physical sciences. In this article, we employ two of commonly used algorithms in the field of artificial intelligence, the Convolutional Neural Networks (CNN) and Light Gradient Boosting Machine (LightGBM), to improve the accuracy of determining impact parameter by analyzing the proton spectra in transverse momentum and rapidity on the event-by-event basis. Au+Au collisions with the impact parameter of 0$\le$$b$$\le$10 fm at intermediate energies ($E_{\mathrm{lab}}$=$0.2$-$1.0$ GeV$/$nucleon) are simulated with the ultrarelativistic quantum molecular dynamics (UrQMD) model to generate the proton spectra data. It is found that the average difference between the true impact parameter and the estimated one can be smaller than 0.1 fm. The LightGBM algorithm shows an improved performance with respect to the CNN on the task in this work. By using the LightGBM's visualization algorithm, one can obtain the important feature map of the distribution of transverse momentum and rapidity, which may be helpful in inferring the impact parameter or centrality in heavy-ion experiments.

   ↳ nucl-thnucl-exJ.Phys.G(2020)·44 citations

* Reconstructed cohort: no mailing for this day survives in the archive. Papers are grouped by their submission times and arXiv's announcement cut-off, assuming announcement without delay; positions follow identifier order. Validated at ~91% exact-day agreement against the archived era.