Thu·Aug 27, 2015Thursday·August 27, 2015

1. 01*

   Recent direct reaction experimental studies with radioactive tin beams

   K.L. Jones🇺🇸 · S. Ahn🇺🇸 · J.M. Allmond🇺🇸 · A. Ayres · D.W. Bardayan🇺🇸 · T. Baugher🇺🇸 · D. Bazin🇺🇸 · J.S. Berryman🇺🇸 · A. Bey🇺🇸 · C. Bingham🇺🇸 · L. Cartegni · G. Cerizza and 31 other authors

   Direct reaction techniques are powerful tools to study the single-particle nature of nuclei. Performing direct reactions on short-lived nuclei requires radioactive ion beams produced either via fragmentation or the Isotope Separation OnLine (ISOL) method. Some of the most interesting regions to study with direct reactions are close to the magic numbers where changes in shell structure can be tracked. These changes can impact the final abundances of explosive nucleosynthesis. The structure of the chain of tin isotopes is strongly influenced by the Z=50 proton shell closure, as well as the neutron shell closures lying in the neutron-rich, N=82, and neutron-deficient, N=50, regions. Here we present two examples of direct reactions on exotic tin isotopes. The first uses a one-neutron transfer reaction and a low-energy reaccelerated ISOL beam to study states in 131Sn from across the N=82 shell closure. The second example utilizes a one-neutron knockout reaction on fragmentation beams of neutron-deficient 106,108Sn. In both cases, measurements of gamma rays in coincidence with charged particles proved to be invaluable.

   nucl-exActa Phys.Polon.B(2015)·3 citations
2. 02*

   Nuclear Mass Predictions for the Crustal Composition of Neutron Stars: A Bayesian Neural Network Approach

   R. Utama🇮🇩 · J. Piekarewicz🇺🇸 · H. B. Prosper🇺🇸

   Besides their intrinsic nuclear-structure value, nuclear mass models are essential for astrophysical applications, such as r-process nucleosynthesis and neutron-star structure. To overcome the intrinsic limitations of existing "state-of-the-art" mass models, we propose a refinement based on a Bayesian Neural Network (BNN) formalism. A novel BNN approach is implemented with the goal of optimizing mass residuals between theory and experiment. A significant improvement (of about 40%) in the mass predictions of existing models is obtained after BNN refinement. Moreover, these improved results are now accompanied by proper statistical errors. Finally, by constructing a "world average" of these predictions, a mass model is obtained that is used to predict the composition of the outer crust of a neutron star. The power of the Bayesian neural network method has been successfully demonstrated by a systematic improvement in the accuracy of the predictions of nuclear masses. Extension to other nuclear observables is a natural next step that is currently under investigation.

   ↳ nucl-thastro-ph.HEastro-ph.SRnucl-exPRC(2016)·211 citations
3. 03*

   Nuclear Reactions For Nucleosynthesis Beyond Fe

   T. Rauscher🇬🇧

   Many more nuclear transitions have to be known in the determination of stellar reactivities for trans-iron nucleosynthesis than for reactions of light nuclei. This requires different theoretical and experimental approaches. Some of the issues specific for trans-iron nucleosynthesis are discussed.

   ↳ nucl-thastro-ph.HEnucl-exAIP Conf.Proc.(2015)·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.