Bayesian approach to model-based extrapolation of nuclear observables
Bayesian approach to model-based extrapolation of nuclear observables
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DOI：
10.1103/physrevc.98.034318
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发表时间：
2018-06
影响因子：
3.1
通讯作者：
L. Neufcourt;Yuchen Cao;W. Nazarewicz;F. Viens
中科院分区：
文献类型：
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作者：
L. Neufcourt;Yuchen Cao;W. Nazarewicz;F. Viens
质量或结合能是原子核的基本性质。它决定了它的稳定性，反应和衰变速率。对原子核的结合进行量化对于理解宇宙中元素的起源是很重要的。负责恒星核合成的天体物理过程通常发生在远离稳定谷的地方，那里的实验质量是未知的。在这种情况下，缺失的核信息必须通过使用极端外推的理论预测来提供。贝叶斯机器学习技术可以通过充分利用实验质量和计算质量之间的偏差中包含的信息来改进预测。我们考虑了10个基于核密度泛函理论的全局模型以及两个唯象质量模型。利用贝叶斯高斯过程和贝叶斯神经网络构造了S2n残差和置信区间定义理论误差棒的仿真器。我们考虑了一个大型的训练数据集，这些数据集与2003年之前测量的原子核质量有关。对于测试数据集，我们考虑了那些在2003年之后确定质量的奇异核。然后，我们对2n滴线进行了外推。虽然高斯过程和贝叶斯神经网络都显着降低了实验的均方根偏差，但GP提供了更好和更稳定的性能。预测能力的增加是相当惊人的：测试数据集上实验的均方根偏差与更多现象学模型的均方根偏差相似。我们获得的经验覆盖概率曲线非常匹配的参考值，这是非常可取的，以确保诚实的不确定性量化，和预测的可信区间估计，使人们有可能评估个别模型的预测能力。
The mass, or binding energy, is the basis property of the atomic nucleus. It determines its stability, and reaction and decay rates. Quantifying the nuclear binding is important for understanding the origin of elements in the universe. The astrophysical processes responsible for the nucleosynthesis in stars often take place far from the valley of stability, where experimental masses are not known. In such cases, missing nuclear information must be provided by theoretical predictions using extreme extrapolations. Bayesian machine learning techniques can be applied to improve predictions by taking full advantage of the information contained in the deviations between experimental and calculated masses. We consider 10 global models based on nuclear Density Functional Theory as well as two more phenomenological mass models. The emulators of S2n residuals and credibility intervals defining theoretical error bars are constructed using Bayesian Gaussian processes and Bayesian neural networks. We consider a large training dataset pertaining to nuclei whose masses were measured before 2003. For the testing datasets, we considered those exotic nuclei whose masses have been determined after 2003. We then carried out extrapolations towards the 2n dripline. While both Gaussian processes and Bayesian neural networks reduce the rms deviation from experiment significantly, GP offers a better and much more stable performance. The increase in the predictive power is quite astonishing: the resulting rms deviations from experiment on the testing dataset are similar to those of more phenomenological models. The empirical coverage probability curves we obtain match very well the reference values which is highly desirable to ensure honesty of uncertainty quantification, and the estimated credibility intervals on predictions make it possible to evaluate predictive power of individual models.