Fri·Jan 17, 2020Friday·January 17, 2020

1. 01*

   D-meson production in Pb-Pb collisions with ALICE at the LHC

   Stefano Trogolo (for the ALICE Collaboration)🇮🇹

   Heavy quarks, like charm and beauty, are sensitive probes to investigate the colour-deconfined medium created in high-energy heavy-ion collisions, the Quark-Gluon Plasma (QGP). The ALICE Collaboration measured the production of D${}^0$, D${}^{+}$, D${}^{\ast +}$ and D${}_{\mathrm{s}}^{+}$ in Pb-Pb collisions at $\sqrt{s_{\mathrm{NN}}}$ = 5.02 TeV. The measurement of the nuclear modification factor ($R_{\mathrm{AA}}$) provides informations on the in-medium parton energy loss. In addition, the comparison between D${}_{\mathrm{s}}^{+}$ and the non-strange D-meson $R_{\mathrm{AA}}$ allows to investigate possible modifications of the charm-quark hadronisation mechanism in the QGP. The most recent results for these observables, which were obtained by analysing the latest 2018 data sample of Pb-Pb collisions, are presented as well as the comparison with theoretical model calculations.

   nucl-exNPA(2021)·3 citations
2. 02*

   Quantified limits of the nuclear landscape

   Léo Neufcourt🇺🇸 · Yuchen Cao🇺🇸 · Samuel A. Giuliani🇺🇸 · Witold Nazarewicz🇺🇸 · Erik Olsen🇧🇪 · Oleg B. Tarasov🇺🇸

   The chart of the nuclides is limited by particle drip lines beyond which nuclear stability to proton or neutron emission is lost. Predicting the range of particle-bound isotopes poses an appreciable challenge for nuclear theory as it involves extreme extrapolations of nuclear masses beyond the regions where experimental information is available. Still, quantified extrapolations are crucial for a variety of applications, including the modeling of stellar nucleosynthesis. We use microscopic nuclear mass models and Bayesian methodology to provide quantified predictions of proton and neutron separation energies as well as Bayesian probabilities of existence throughout the nuclear landscape all the way to the particle drip lines. We apply nuclear density functional theory with several energy density functionals. To account for uncertainties, Bayesian Gaussian processes are trained on the separation-energy residuals for each individual model, and the resulting predictions are combined via Bayesian model averaging. This framework allows to account for systematic and statistical uncertainties and propagate them to extrapolative predictions. We characterize the drip-line regions where the probability that the nucleus is particle-bound decreases from $1$ to $0$. In these regions, we provide quantified predictions for one- and two-nucleon separation energies. According to our Bayesian model averaging analysis, 7759 nuclei with $Z\le 119$ have a probability of existence $\ge 0.5$. The extrapolations obtained in this study will be put through stringent tests when new experimental information on exotic nuclei becomes available. In this respect, the quantified landscape of nuclear existence obtained in this study should be viewed as a dynamical prediction that will be fine-tuned when new experimental information and improved global mass models become available.

   ↳ nucl-thnucl-exstat.MLPRC(2020)·114 citations
3. 03*

   Correlations between Background Radiation inside a Multilayer Interleaving Structure, Geomagnetic Activity, and Cosmic Radiation: A Fourth Order Cumulant-based Correlation Analysis

   M. E. Iglesias-Martínez · J. C. Castro-Palacio · F. Scholkmann🇨🇭 · V. Milián-Sánchez · P. Fernández de Córdoba🇪🇸 · A. Mocholí-Salcedo · F. Mocholí · V. A. Kolombet · V.A. Panchelyuga · G. Verdú

   In this work, we analyzed time-series of background radiation inside a multilayer interleaving structure, geomagnetic activity and cosmic-ray activity using the Pearson correlation coefficient and a new correlation measure based on the one-dimensional component of the fourth order cumulant. The new method is proposed based on the fact that the cumulant of a random process is zero if it is of Gaussian nature. The results show that this methodology is useful for detecting correlations between the analyzed variables.

   ↳ physics.data-annucl-exMathematics(2020)·2 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.