905 research outputs found
The One-Body and Two-Body Density Matrices of Finite Nuclei and Center-of-Mass Correlations
A method is presented for the calculation of the one-body and two-body
density matrices and their Fourier transforms in momentum space, that is
consistent with the requirement for translational invariance, in the case of a
nucleus (a finite self-bound system). We restore translational invariance by
using the so-called fixed center-of-mass approximation for constructing an
intrinsic nuclear ground state wavefunction by starting from a
non-translationally invariant wavefunction and applying a projection
prescription. We discuss results for the one-body and two-body momentum
distributions of the 4He nucleus calculated with the Slater determinant of the
harmonic oscillator orbitals, as the initial non-translationally invariant
wavefunction. Effects of such an inclusion of CM correlations are found to be
quite important in the momentum distributions.Comment: 5 pages, incl. 2 figures; Proc. Int. Conf. on Frontiers in Nuclear
Structure, Astrophysics and Reactions (FINUSTAR), Kos, Greece, Sept.200
Microscopic Study of Superfluidity in Dilute Neutron Matter
Singlet -wave superfluidity of dilute neutron matter is studied within the
correlated BCS method, which takes into account both pairing and short-range
correlations. First, the equation of state (EOS) of normal neutron matter is
calculated within the Correlated Basis Function (CBF) method in lowest cluster
order using the and components of the Argonne
potential, assuming trial Jastrow-type correlation functions. The
superfluid gap is then calculated with the corresponding component of the
Argonne potential and the optimally determined correlation functions.
The dependence of our results on the chosen forms for the correlation functions
is studied, and the role of the -wave channel is investigated. Where
comparison is meaningful, the values obtained for the gap within
this simplified scheme are consistent with the results of similar and more
elaborate microscopic methods.Comment: 9 pages, 6 figure
Nonperiodic delay mechanism in time-dependent chaotic scattering
We study the occurence of delay mechanisms other than periodic orbits in
systems with time dependent potentials that exhibit chaotic scattering. By
using as model system two harmonically oscillating disks on a plane, we have
found the existence of a mechanism not related to the periodic orbits of the
system, that delays trajectories in the scattering region. This mechanism
creates a fractal-like structure in the scattering functions and can possibly
occur in several time-dependent scattering systems.Comment: 12 pages, 9 figure
A Global Model of -Decay Half-Lives Using Neural Networks
Statistical modeling of nuclear data using artificial neural networks (ANNs)
and, more recently, support vector machines (SVMs), is providing novel
approaches to systematics that are complementary to phenomenological and
semi-microscopic theories. We present a global model of -decay
halflives of the class of nuclei that decay 100% by mode in their
ground states. A fully-connected multilayered feed forward network has been
trained using the Levenberg-Marquardt algorithm, Bayesian regularization, and
cross-validation. The halflife estimates generated by the model are discussed
and compared with the available experimental data, with previous results
obtained with neural networks, and with estimates coming from traditional
global nuclear models. Predictions of the new neural-network model are given
for nuclei far from stability, with particular attention to those involved in
r-process nucleosynthesis. This study demonstrates that in the framework of the
-decay problem considered here, global models based on ANNs can at
least match the predictive performance of the best conventional global models
rooted in nuclear theory. Accordingly, such statistical models can provide a
valuable tool for further mapping of the nuclidic chart.Comment: Proceedings of the 16th Panhellenic Symposium of the Hellenic Nuclear
Physics Societ
Nuclear mass systematics by complementing the Finite Range Droplet Model with neural networks
A neural-network model is developed to reproduce the differences between
experimental nuclear mass-excess values and the theoretical values given by the
Finite Range Droplet Model. The results point to the existence of subtle
regularities of nuclear structure not yet contained in the best
microscopic/phenomenological models of atomic masses. Combining the FRDM and
the neural-network model, we create a hybrid model with improved predictive
performance on nuclear-mass systematics and related quantities.Comment: Proceedings for the 15th Hellenic Symposium on Nuclear Physic
The Effect of the Short-Range Correlations on the Generalized Momentum Distribution in Finite Nuclei
The effect of dynamical short-range correlations on the generalized momentum
distribution in the case of , -closed shell
nuclei is investigated by introducing Jastrow-type correlations in the
harmonic-oscillator model. First, a low order approximation is considered and
applied to the nucleus He. Compact analytical expressions are derived and
numerical results are presented and the effect of center-of-mass corrections is
estimated. Next, an approximation is proposed for of
heavier nuclei, that uses the above correlated of He.
Results are presented for the nucleus O. It is found that the effect of
short-range correlations is significant for rather large values of the momenta
and/or and should be included, along with center of mass corrections
for light nuclei, in a reliable evaluation of in the whole
domain of and .Comment: 29 pages, 8 figures. Further results, figures and discussion for the
CM corrections are added. Accepted by Journal of Physics
Statistical Global Modeling of Beta-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines
In this work, the beta-decay halflives problem is dealt as a nonlinear
optimization problem, which is resolved in the statistical framework of Machine
Learning (LM). Continuing past similar approaches, we have constructed
sophisticated Artificial Neural Networks (ANNs) and Support Vector Regression
Machines (SVMs) for each class with even-odd character in Z and N to global
model the systematics of nuclei that decay 100% by the beta-minus-mode in their
ground states. The arising large-scale lifetime calculations generated by both
types of machines are discussed and compared with each other, with the
available experimental data, with previous results obtained with neural
networks, as well as with estimates coming from traditional global nuclear
models. Particular attention is paid on the estimates for exotic and halo
nuclei and we focus to those nuclides that are involved in the r-process
nucleosynthesis. It is found that statistical models based on LM can at least
match or even surpass the predictive performance of the best conventional
models of beta-decay systematics and can complement the latter.Comment: 8 pages, 1 fiqure, Proceedings of the 17th HNPS Symposiu
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