基于多层前馈神经网络和支持向量机的β衰变系统统计全局建模
Statistical Global Modeling of Beta-Decay Halflives Systematics Using Multilayer Feedforward Neural Networks and Support Vector Machines
摘要：
在这项工作中，β衰变半衰期问题被处理为一个非线性优化问题，这是解决机器学习（LM）的统计框架。继续过去类似的方法，我们已经构建了复杂的人工神经网络（ANN）和支持向量回归机（SVMs）为每个类的偶奇字符在Z和N的全局模型的系统的衰变100%的β-负模式在其基态的核。这两种类型的机器所产生的大规模的寿命计算进行了讨论和相互比较，与现有的实验数据，与以前的结果与神经网络，以及来自传统的全球核模型的估计。特别注意的是支付估计异国情调和晕核，我们专注于这些核素参与的r-过程核合成。结果发现，统计模型的基础上LM至少可以匹配，甚至超过最好的传统模型的预测性能的β衰变系统学，并可以补充后者。
英文摘要：
In this work, the beta-decay halflives problem is dealt as a nonlinear optimization problem, which is resolved in the statistical framework of Machine Learning (LM). Continuing past similar approaches, we have constructed sophisticated Artificial Neural Networks (ANNs) and Support Vector Regression Machines (SVMs) for each class with even-odd character in Z and N to global model the systematics of nuclei that decay 100% by the beta-minus-mode in their ground states. The arising large-scale lifetime calculations generated by both types of machines are discussed and compared with each other, with the available experimental data, with previous results obtained with neural networks, as well as with estimates coming from traditional global nuclear models. Particular attention is paid on the estimates for exotic and halo nuclei and we focus to those nuclides that are involved in the r-process nucleosynthesis. It is found that statistical models based on LM can at least match or even surpass the predictive performance of the best conventional models of beta-decay systematics and can complement the latter.
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。
