32,623 research outputs found
Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects
Bayesian neural network (BNN) approach is employed to improve the nuclear
mass predictions of various models. It is found that the noise error in the
likelihood function plays an important role in the predictive performance of
the BNN approach. By including a distribution for the noise error, an
appropriate value can be found automatically in the sampling process, which
optimizes the nuclear mass predictions. Furthermore, two quantities related to
nuclear pairing and shell effects are added to the input layer in addition to
the proton and mass numbers. As a result, the theoretical accuracies are
significantly improved not only for nuclear masses but also for single-nucleon
separation energies. Due to the inclusion of the shell effect, in the unknown
region, the BNN approach predicts a similar shell-correction structure to that
in the known region, e.g., the predictions of underestimation of nuclear mass
around the magic numbers in the relativistic mean-field model. This manifests
that better predictive performance can be achieved if more physical features
are included in the BNN approach.Comment: 15 pages, 4 figures, and 3 table
Self-consistent relativistic quasiparticle random-phase approximation and its applications to charge-exchange excitations and -decay half-lives
The self-consistent quasiparticle random-phase approximation (QRPA) approach
is formulated in the canonical single-nucleon basis of the relativistic
Hatree-Fock-Bogoliubov (RHFB) theory. This approach is applied to study the
isobaric analog states (IAS) and Gamov-Teller resonances (GTR) by taking Sn
isotopes as examples. It is found that self-consistent treatment of the
particle-particle residual interaction is essential to concentrate the IAS in a
single peak for open-shell nuclei and the Coulomb exchange term is very
important to predict the IAS energies. For the GTR, the isovector pairing can
increase the calculated GTR energy, while the isoscalar pairing has an
important influence on the low-lying tail of the GT transition. Furthermore,
the QRPA approach is employed to predict nuclear -decay half-lives. With
an isospin-dependent pairing interaction in the isoscalar channel, the
RHFB+QRPA approach almost completely reproduces the experimental -decay
half-lives for nuclei up to the Sn isotopes with half-lives smaller than one
second. Large discrepancies are found for the Ni, Zn, and Ge isotopes with
neutron number smaller than , as well as the Sn isotopes with neutron
number smaller than . The potential reasons for these discrepancies are
discussed in detail.Comment: 34 pages, 14 figure
Nuclear /EC decays in covariant density functional theory and the impact of isoscalar proton-neutron pairing
Self-consistent proton-neutron quasiparticle random phase approximation based
on the spherical nonlinear point-coupling relativistic Hartree-Bogoliubov
theory is established and used to investigate the /EC-decay half-lives
of neutron-deficient Ar, Ca, Ti, Fe, Ni, Zn, Cd, and Sn isotopes. The isoscalar
proton-neutron pairing is found to play an important role in reducing the decay
half-lives, which is consistent with the same mechanism in the decays
of neutron-rich nuclei. The experimental /EC-decay half-lives can be
well reproduced by a universal isoscalar proton-neutron pairing strength.Comment: 12 pages, 4 figure
-decay half-lives of neutron-rich nuclei and matter flow in the -process
The -decay half-lives of neutron-rich nuclei with are systematically investigated using the newly developed fully
self-consistent proton-neutron quasiparticle random phase approximation (QRPA),
based on the spherical relativistic Hartree-Fock-Bogoliubov (RHFB) framework.
Available data are reproduced by including an isospin-dependent proton-neutron
pairing interaction in the isoscalar channel of the RHFB+QRPA model. With the
calculated -decay half-lives of neutron-rich nuclei a remarkable
speeding up of -matter flow is predicted. This leads to enhanced -process
abundances of elements with , an important result for the
understanding of the origin of heavy elements in the universe.Comment: 14 pages, 4 figure
Antimagnetic Rotation Band in Nuclei: A Microscopic Description
Covariant density functional theory and the tilted axis cranking method are
used to investigate antimagnetic rotation (AMR) in nuclei for the first time in
a fully self-consistent and microscopic way. The experimental spectrum as well
as the B(E2) values of the recently observed AMR band in 105Cd are reproduced
very well. This gives a further strong hint that AMR is realized in specific
bands in nuclei.Comment: 10 pages, 4 figure
Bidirectional optimization of the melting spinning process
This is the author's accepted manuscript (under the provisional title "Bi-directional optimization of the melting spinning process with an immune-enhanced neural network"). The final published article is available from the link below. Copyright 2014 @ IEEE.A bidirectional optimizing approach for the melting spinning process based on an immune-enhanced neural network is proposed. The proposed bidirectional model can not only reveal the internal nonlinear relationship between the process configuration and the quality indices of the fibers as final product, but also provide a tool for engineers to develop new fiber products with expected quality specifications. A neural network is taken as the basis for the bidirectional model, and an immune component is introduced to enlarge the searching scope of the solution field so that the neural network has a larger possibility to find the appropriate and reasonable solution, and the error of prediction can therefore be eliminated. The proposed intelligent model can also help to determine what kind of process configuration should be made in order to produce satisfactory fiber products. To make the proposed model practical to the manufacturing, a software platform is developed. Simulation results show that the proposed model can eliminate the approximation error raised by the neural network-based optimizing model, which is due to the extension of focusing scope by the artificial immune mechanism. Meanwhile, the proposed model with the corresponding software can conduct optimization in two directions, namely, the process optimization and category development, and the corresponding results outperform those with an ordinary neural network-based intelligent model. It is also proved that the proposed model has the potential to act as a valuable tool from which the engineers and decision makers of the spinning process could benefit.National Nature Science Foundation of China, Ministry of Education of China, the Shanghai Committee of Science and Technology), and the Fundamental Research Funds for the Central Universities
Topological Bose-Mott Insulators in a One-Dimensional Optical Superlattice
We study topological properties of the Bose-Hubbard model with repulsive
interactions in a one-dimensional optical superlattice. We find that the Mott
insulator states of the single-component (two-component) Bose-Hubbard model
under fractional fillings are topological insulators characterized by a nonzero
charge (or spin) Chern number with nontrivial edge states. For ultracold atomic
experiments, we show that the topological Chern number can be detected through
measuring the density profiles of the bosonic atoms in a harmonic trap.Comment: 5 pages, published versio
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