499 research outputs found
A Kohn-Sham Scheme Based Neural Network for Nuclear Systems
A Kohn-Sham scheme based multi-task neural network is elaborated for the
supervised learning of nuclear shell evolution. The training set is composed of
the single-particle wave functions and occupation probabilities of 320 nuclei,
calculated by the Skyrme density functional theory. It is found that the
deduced density distributions, momentum distributions, and charge radii are in
good agreements with the benchmarking results for the untrained nuclei. In
particular, accomplishing shell evolution leads to a remarkable improvement in
the extrapolation of nuclear density. After a further charge-radius-based
calibration, the network evolves a stronger predictive capability. This opens
the possibility to infer correlations among observables by combining
experimental data for nuclear complex systems
A Neural Network Approach for Orienting Heavy-Ion Collision Events
A convolutional neural network-based classifier is elaborated to retrace the
initial orientation of deformed nucleus-nucleus collisions by integrating
multiple typical experimental observables. The isospin-dependent
Boltzmann-Uehling-Uhlenbeck transport model is employed to generate data for
random orientations of ultra-central uranium-uranium collisions at
. Statistically, the data-driven
polarization scheme is essentially accomplished via the classifier, whose
distinct categories filter out specific orientation-biased collision events.
This will advance the deformed nucleus-based studies on nuclear symmetry
energy, neutron skin, etc
(S)-1-[(S)-4-Benzyl-2-thioxothiaÂzolidin-3-yl]-3-hydroxyÂbutan-1-one
The title compound, C14H17NO2S2, was synthesized by asymmetric aldol condensation of N-acylÂthiaÂzolidinethione with acetaldehyde. In the molÂecule, the thiaÂzolidine five-membered ring assumes an envelope conformation. InterÂmolecular C—H⋯O and intraÂmolecular O—H⋯O and C—H⋯S hydrogen bonding helps to stabilize the structure
Impact of quadrupole deformation on intermediate-energy heavy-ion collisions
This study employs the isospin-dependent Boltzmann-Uehling-Uhlenbeck model to
simulate intermediate-energy heavy-ion collisions between prolate nuclei
Mg. The emphasis is on investigating the influence of centrality and
orientation in several collision scenarios. The final-state particle
multiplicities and anisotropic flows are primarily determined by the
eccentricity and the area of the initial overlap. This not only provides
feedback on the collision systems, but also, to some extent, provides a means
to explore the fine structure inside deformed nuclei. Additionally,
non-polarized collisions have been further discussed. These results contribute
to the understanding of the geometric effects in nuclear reactions, and aid in
the exploration of other information on reaction systems, such as the equation
of state and nuclear high-momentum tail
Assessing the Impact of Nuclear Mass Models on the Prediction of Synthesis Cross Sections for Superheavy Elements
Within the framework of the dinuclear system model, this study delves into
the impact of various nuclear mass models on evaluating the fusion probability
of superheavy nuclei. Nuclear mass models, as crucial inputs to the DNS model,
exhibit slight variations in binding energy, quadrupole deformation, and
extrapolation ability; these subtle differences can significantly influence the
model's outcomes. Specifically, the study finds that nuclear mass plays a
pivotal role in determining fusion probability, and Q-value. By numerically
solving a set of master equations, the study examines how binding energies from
different mass models affect the fusion probability of colliding nuclei, taking
the example of Ca + Am Mc. A careful
analysis of the potential energy surface (PES) reveals that the inner fusion
barriers lead to variations in fusion probabilities. Importantly, the study
demonstrates that the synthesis cross sections of superheavy nuclei calculated
using different nuclear mass models align well with experimental data, falling
within an error range of one order of magnitude. This finding underscores the
reliability of our model predictions. Looking ahead, the study utilizes five
distinct nuclear mass models to predict the synthesis cross sections of
superheavy elements 119 and 120, along with their associated uncertainties.
These predictions offer valuable insights into the feasibility of synthesizing
these elusive elements and pave the way for future experimental explorations
Calibration of nuclear charge density distribution by back-propagation neural networks
Based on the back-propagation neural networks and density functional theory,
a supervised learning is performed firstly to generate the nuclear charge
density distributions. The charge density is further calibrated to the
experimental charge radii by a composite loss function. It is found that, when
the parity, pairing, and shell effects are taken into account, about of
the nuclei in the validation set fall within two standard deviations of the
predicted charge radii. The calibrated charge density is then mapped to the
matter density, and further mapped to the binding energies according to the
Hohenberg-Kohn theorem. It provides an improved description of some nuclei in
both binding energies and charge radii. Moreover, the anomalous overbinding in
Ca implies the existence of an indispensable beyond-mean-field effect
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