解读β衰变系统学：β ^-半衰期的全局统计模型
Decoding Beta-Decay Systematics: A Global Statistical Model for Beta^- Halflives
摘要：
核数据的统计建模为核系统学提供了一种新的方法，与基于量子理论的理论和唯象方法互补。在机器学习理论的一般框架内继续进行全局统计建模的先前研究，我们实现了旨在改进泛化的训练算法的进展，应用于复制和预测由β ^-模式衰减100%的核基态的半衰期的问题。更具体地说，全连接，多层前馈人工神经网络模型开发使用Levenberg-Marquardt优化算法与贝叶斯正则化和交叉验证。从广泛的计算机实验中出现的模型的预测性能相比，传统的微观和现象学模型，以及与其他学习系统的性能，包括早期的神经网络模型，以及支持向量机最近应用到同一个问题。在讨论的结果，重点放在预测的核是远离稳定线，特别是那些参与的r-过程核合成。结果发现，新的统计模型可以匹配，甚至超过传统的β衰变系统学模型的预测性能，因此应该提供一个有价值的额外的工具，探索不断扩大的核景观。
英文摘要：
Statistical modeling of nuclear data provides a novel approach to nuclear systematics complementary to established theoretical and phenomenological approaches based on quantum theory. Continuing previous studies in which global statistical modeling is pursued within the general framework of machine learning theory, we implement advances in training algorithms designed to improved generalization, in application to the problem of reproducing and predicting the halflives of nuclear ground states that decay 100% by the beta^- mode. More specifically, fully-connected, multilayer feedforward artificial neural network models are developed using the Levenberg-Marquardt optimization algorithm together with Bayesian regularization and cross-validation. The predictive performance of models emerging from extensive computer experiments is compared with that of traditional microscopic and phenomenological models as well as with the performance of other learning systems, including earlier neural network models as well as the support vector machines recently applied to the same problem. In discussing the results, emphasis is placed on predictions for nuclei that are far from the stability line, and especially those involved in the r-process nucleosynthesis. It is found that the new statistical models can match or even surpass the predictive performance of conventional models for beta-decay systematics and accordingly should provide a valuable additional tool for exploring the expanding nuclear landscape.
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