Fri·Oct 7, 2022Friday·October 7, 2022

1. 01*

   $K^{\ast 0}$ production in Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$ = 7.7, 11.5, 14.5, 19.6, 27 and 39 GeV from RHIC beam energy scan

   STAR Collaboration: M. S. Abdallah · B. E. Aboona · J. Adam · L. Adamczyk · J. R. Adams · J. K. Adkins · I. Aggarwal · M. M. Aggarwal · Z. Ahammed · D. M. Anderson · E. C. Aschenauer · J. Atchison and 362 other authors

   We report the measurement of $K^{\ast 0}$ meson at midrapidity ($|y|<$ 1.0) in Au+Au collisions at $\sqrt{s_{\mathrm{NN}}}$~=~7.7, 11.5, 14.5, 19.6, 27 and 39 GeV collected by the STAR experiment during the RHIC beam energy scan (BES) program. The transverse momentum spectra, yield, and average transverse momentum of $K^{\ast 0}$ are presented as functions of collision centrality and beam energy. The $K^{\ast 0}/K$ yield ratios are presented for different collision centrality intervals and beam energies. The $K^{\ast 0}/K$ ratio in heavy-ion collisions are observed to be smaller than that in small system collisions (e+e and p+p). The $K^{\ast 0}/K$ ratio follows a similar centrality dependence to that observed in previous RHIC and LHC measurements. The data favor the scenario of the dominance of hadronic re-scattering over regeneration for $K^{\ast 0}$ production in the hadronic phase of the medium.

   nucl-exPRC(2023)·36 citations
2. 02*

   Hadronic resonance production with ALICE at the LHC

   Sergey Kiselev (for the ALICE Collaboration)🇷🇺

   We present recent results on short-lived hadronic resonances obtained by the ALICE experiment at LHC energies. Results include system-size and collision-energy evolution of transverse momentum spectra, yields and ratios of resonance yields to those of longer lived particles, and nuclear modification factors. The results are compared with model predictions and measurements at lower energies.

   nucl-exPhys.Atom.Nucl.(2022)·1 citation
3. 03*

   Study of path-length dependent energy loss of jets in p--Pb and Pb--Pb collisions with ALICE

   Caitlin Beattie (for the ALICE Collaboration)🇺🇸

   Jet quenching, a standard signature of quark--gluon plasma (QGP) formation in which jets lose energy by traversing the medium, comprises a well-studied set of observables in heavy-ion collisions. Significant questions remain, however, concerning the mechanisms driving this phenomenon. Theoretical work that attempts to address these open questions offers the path-length dependence of jet energy loss as one way to better understand the underlying mechanisms of jet quenching. It has proven challenging, however, to derive explicit values for the path-length dependence from experimental data. These proceedings discuss recent results from ALICE that attempt to contribute to our understanding of this phenomenon, including results of event-shape engineered jet spectra, the jet-particle $v_2$, and correlation studies between hard triggers and hadrons.

   nucl-exActa Phys.Polon.Supp.(2023)·0 citations
4. 04*

   Nuclear binding energies in artificial neural networks

   Lin-Xing Zeng · Yu-Ying Yin · Xiao-Xu Dong · Li-Sheng Geng🇨🇳

   The binding energy (BE) or mass is one of the most fundamental properties of an atomic nucleus. Precise binding energies are vital inputs for many nuclear physics and nuclear astrophysics studies. However, due to the complexity of atomic nuclei and of the non-perturbative strong interaction, up to now, no conventional physical model can describe nuclear binding energies with a precision below 0.1 MeV, the accuracy needed by nuclear astrophysical studies. In this work, artificial neural networks (ANNs), the so called ``universal approximators", are used to calculate nuclear binding energies. We show that the ANN can describe all the nuclei in AME2020 with a root-mean-square deviation (RMSD) around 0.2 MeV, which is better than the best macroscopic-microscopic models, such as FRDM and WS4. The success of the ANN is mainly due to the proper and essential input features we identify, which contain the most relevant physical information, i.e., shell, paring, and isospin-asymmetry effects. We show that the well-trained ANN has excellent extrapolation ability and can predict binding energies for those nuclei so far inaccessible experimentally. In particular, we highlight the important role played by ``feature engineering'' for physical systems where data are relatively scarce, such as nuclear binding energies.

   ↳ nucl-thnucl-exPRC(2024)·20 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.