499 research outputs found

    A Kohn-Sham Scheme Based Neural Network for Nuclear Systems

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    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

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    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 Ebeam=1 GeV/nucleonE_\text{beam} = 1\, \text{GeV/nucleon}. 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-thioxothia­zolidin-3-yl]-3-hydroxy­butan-1-one

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    The title compound, C14H17NO2S2, was synthesized by asymmetric aldol condensation of N-acyl­thia­zolidinethione with acetaldehyde. In the mol­ecule, the thia­zolidine five-membered ring assumes an envelope conformation. Inter­molecular C—H⋯O and intra­molecular O—H⋯O and C—H⋯S hydrogen bonding helps to stabilize the structure

    Impact of quadrupole deformation on intermediate-energy heavy-ion collisions

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    This study employs the isospin-dependent Boltzmann-Uehling-Uhlenbeck model to simulate intermediate-energy heavy-ion collisions between prolate nuclei 24^{24}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

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    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 48^{48}Ca + 243^{243}Am →\rightarrow 291^{291}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

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    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 96%96\% 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 48^{48}Ca implies the existence of an indispensable beyond-mean-field effect
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