272 research outputs found
Microscopic description of Gamow-Teller transitions in middle pf--shell nuclei by a realistic shell model calculation
GT transitions in nuclei are studied in terms of a large-scale
realistic shell-model calculation, by using Towner's microscopic parameters.
values to low-lying final states are reproduced with a reasonable
accuracy. Several gross properties with respect to the GT transitions are
investigated with this set of the wavefunctions and the operator. While the
calculated total GT strengths show no apparent disagreement with the
measured ones, the calculated total GT strengths are somewhat larger than
those obtained from charge-exchange experiments. Concerning the Ikeda sum-rule,
the proportionality of to persists to an excellent
approximation, with a quenching factor of 0.68. For the relative GT
strengths among possible isospin components, the lowest isospin component
gathers greater fraction than expected by the squared CG coefficients of the
isospin coupling. It turns out that these relative strengths are insensitive to
the size of model space. Systematics of the summed values are
discussed for each isospin component.Comment: IOP-LaTeX 23 pages, to appear in J. Phys. G., 5 Postscript figures
available upon reques
Low-Spin Spectroscopy of 50Mn
The data on low spin states in the odd-odd nucleus 50Mn investigated with the
50Cr(p,ngamma)50Mn fusion evaporation reaction at the FN-TANDEM accelerator in
Cologne are reported. Shell model and collective rotational model
interpretations of the data are given.Comment: 7 pages, 2 figures, to be published in the proceedings of the
"Bologna 2000 - Structure of the Nucleus at the Dawn of the Century"
Conference, (Bologna, Italy, May 29 - June 3, 2000
Flow Factorized Representation Learning
A prominent goal of representation learning research is to achieve
representations which are factorized in a useful manner with respect to the
ground truth factors of variation. The fields of disentangled and equivariant
representation learning have approached this ideal from a range of
complimentary perspectives; however, to date, most approaches have proven to
either be ill-specified or insufficiently flexible to effectively separate all
realistic factors of interest in a learned latent space. In this work, we
propose an alternative viewpoint on such structured representation learning
which we call Flow Factorized Representation Learning, and demonstrate it to
learn both more efficient and more usefully structured representations than
existing frameworks. Specifically, we introduce a generative model which
specifies a distinct set of latent probability paths that define different
input transformations. Each latent flow is generated by the gradient field of a
learned potential following dynamic optimal transport. Our novel setup brings
new understandings to both \textit{disentanglement} and \textit{equivariance}.
We show that our model achieves higher likelihoods on standard representation
learning benchmarks while simultaneously being closer to approximately
equivariant models. Furthermore, we demonstrate that the transformations
learned by our model are flexibly composable and can also extrapolate to new
data, implying a degree of robustness and generalizability approaching the
ultimate goal of usefully factorized representation learning.Comment: NeurIPS2
- …