A new mass model for nuclear astrophysics: crossing 200 keV accuracy

Shelley, Matthew; Pastore, Alessandro

doi:10.48550/arxiv.2102.07497

Cited by 2 publications

(2 citation statements)

References 32 publications

(57 reference statements)

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“…Focusing on nuclear mass, besides BNN in Refs. [19][20][21], the Levenberg-Marquardt neural network approach [33], Gaussian processes [34,35], decision tree algorithm [36], the Multilayer Perceptron (MLP) algorithm [37] also have been applied to refine nuclear mass models.…”

Section: Introductionmentioning

confidence: 99%

Machine learning the nuclear mass

Gao¹,

Wang²,

Lü³

et al. 2021

Preprint

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Background:The masses of about 2500 nuclei have been measured experimentally, however more than 7000 isotopes are predicted to exist in the nuclear landscape from H (Z=1) to Og (Z=118) based on various theoretical calculations. Exploring the mass of the remains is a hot topic in nuclear physics. Machine learning has been served as a powerful tool in learning complex representations of big data in many fields. Purpose: We use Light Gradient Boosting Machine (LightGBM) which is a highly efficient machine learning algorithm to predict the masses of unknown nuclei and to explore the nuclear landscape in neutron-rich side from learning the measured nuclear masses. Methods: Several characteristic quantities (e.g., mass number, proton number) are fed into LightGBM algorithm to mimic the patterns of the residual δ(Z, A) between the experimental binding energy and the theoretical one given by the liquid-drop model (LDM), Duflo-Zucker (DZ) mass model, finite-range droplet model (FRDM), as well as the Weizsäcker-Skyrme (WS4) model, so as to refine these mass models. Results: By using the experimental data of 80 percent of known nuclei as the training dataset, the root mean square deviation (RMSD) between the predicted and the experimental binding energy of the remaining 20% is about 0.234±0.022 MeV, 0.213±0.018 MeV, 0.170±0.011 MeV, and 0.222±0.016 MeV for the LightGBM-refined LDM, DZ, WS4, and FRDM models, respectively. These values are of about 90%, 65%, 40%, and 60% smaller than the corresponding origin mass models. The RMSD for 66 newly measured nuclei that appeared in AME2020 is also significantly improved on the same foot. One-neutron and two-neutron separation energies predicted by these refined models are in consistence with several theoretical predictions based on various physical models. In addition, the two-neutron separation energy of several newly measured nuclei (e.g., some isotopes of Ca, Ti, Pm, Sm) predicted with LightGBM-refined mass models are also in good agreement with the latest experimental data. Conclusions: LightGBM can be used to refine theoretical nuclear mass models so as to predict the binding energy of unknown nuclei. Moreover, the correlation between the input characteristic quantities and the output can be interpreted by SHapley Additive exPlanations (SHAP, a popular explainable artificial intelligence tool), this may provide new insights on developing theoretical nuclear mass models.

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Section: Introductionmentioning

confidence: 99%

Machine learning the nuclear mass

Gao¹,

Wang²,

Lü³

et al. 2021

Preprint

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“…There have been numerous attempts toward a global description of nuclear masses based on phenomenological models [8][9][10][11][12], the microscopic-macroscopic models [13][14][15][16][17], the shell model [18], the HFB model [19,20], and using machine learning [21][22][23]. For a reliable description of unknown nuclei, there has been a rapid progress in modeling a microscopic approach considering a nuclear * E-mail me at: kyoshida@ruby.scphys.kyoto-u.ac.jp system as a many-body system interacting with nuclear forces.…”

Section: Introductionmentioning

confidence: 99%

KIDS density functional for deformed nuclei: Examples of the even-even Nd isotopes

Gil,

Hinohara,

Hyun

et al. 2021

Preprint

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Background: A global description of the ground-state properties of nuclei in a wide mass range in a unified manner is desirable not only for understanding exotic nuclei but for providing nuclear data for applications.Purpose: We demonstrate the KIDS functional describes the ground states appropriately with respect to the existing data and predictions for a possible application of the functional to all the nuclei by taking Nd isotopes as examples.Method: The Kohn-Sham-Bogoliubov equation is solved for the Nd isotopes with the neutron numbers ranging from 60 to 160 by employing the KIDS functionals constructed to satisfy both neutron-matter equation of state or neutron star observation and selected nuclear data.Results: Considering the nuclear deformation improves the description of the binding energies and radii. We find that the discrepancy from the experimental data is more significant for neutron-rich/deficient isotopes and this can be made isotope independent by changing the slope parameter of the symmetry energy. Conclusions:The KIDS functional is applied to the mid-shell nuclei for the first time. The onset and evolution of deformation are nicely described for the Nd isotopes. The KIDS functional is competent to a global fitting for a better description of nuclear properties in the nuclear chart.

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A new mass model for nuclear astrophysics: crossing 200 keV accuracy

Cited by 2 publications

References 32 publications

Machine learning the nuclear mass

Machine learning the nuclear mass

KIDS density functional for deformed nuclei: Examples of the even-even Nd isotopes

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