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arXiv 1508.06263nucl-thastro-ph.HEastro-ph.SRnucl-ex
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中子星地壳成分的核质量预测:贝叶斯神经网络方法

Nuclear Mass Predictions for the Crustal Composition of Neutron Stars: A Bayesian Neural Network Approach

R. Utama, J. Piekarewicz, H. B. Prosper

摘要:

核质量模型除了其内在的核结构价值外,对于天体物理学的应用,如r过程核合成和中子星结构,也是必不可少的。为了克服现有的“国家的最先进的”质量模型的内在局限性,我们提出了一个改进的贝叶斯神经网络(BNN)形式主义的基础上。实现了一种新的BNN方法,其目标是优化理论和实验之间的质量残差。一个显着的改善(约40%),在现有的模型的质量预测BNN细化后获得。此外,这些改进的结果现在伴随着适当的统计误差。最后,通过构建这些预测的“世界平均值”,得到一个用于预测中子星外壳组成的质量模型。贝叶斯神经网络方法的力量已经成功地证明了一个系统的改进,在核质量的预测精度。扩展到其他核可观测量是目前正在调查的自然下一步。

英文摘要:

Besides their intrinsic nuclear-structure value, nuclear mass models are essential for astrophysical applications, such as r-process nucleosynthesis and neutron-star structure. To overcome the intrinsic limitations of existing "state-of-the-art" mass models, we propose a refinement based on a Bayesian Neural Network (BNN) formalism. A novel BNN approach is implemented with the goal of optimizing mass residuals between theory and experiment. A significant improvement (of about 40%) in the mass predictions of existing models is obtained after BNN refinement. Moreover, these improved results are now accompanied by proper statistical errors. Finally, by constructing a "world average" of these predictions, a mass model is obtained that is used to predict the composition of the outer crust of a neutron star. The power of the Bayesian neural network method has been successfully demonstrated by a systematic improvement in the accuracy of the predictions of nuclear masses. Extension to other nuclear observables is a natural next step that is currently under investigation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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