论文标题
修改的经验公式和机器学习，用于$α$ -DECAY SYSTAYPATS
Modified empirical formulas and machine learning for $α$-decay systematics
论文作者
论文摘要
最新的实验和评估的$α$ -DECAY HAFFILS在82 $ \ leq $ z $ \ leq $ 118之间已用于修改两个经验公式：（i）Horoi缩放法律[J.物理。 g \ textbf {30}，945（2004）]和sobiczewski公式[acta phys。 pol。 b \ textbf {36}，3095（2005）]通过添加依赖性术语（$ i $和$ i^2 $）并对其进行改装。与其他21种公式相比，发现这些改良的公式的结果有显着改善，因此用于预测$α$ decear的半衰期，在未知的超汗区域中具有更精确的状态。 Bao \ textit {et al。}提出的自发裂变（SF）半衰期的公式[J.物理。 G \ TextBf {42}，085101（2015）]通过使用FRDM 2012年FRDM-2012进行的地面壳加上配对校正进一步修改，并使用最新的实验和评估的自发裂变半衰期在82 $ \ leq $ z $ Z $ \ leq $ \ leq $ 118之间。使用这些修改后的公式，在112美元$ \ leq $ z $ \ leq $ 118范围内探测了$α$ -Decay和SF之间的竞赛，因此估计了可能的半衰期和衰减模式。 $^{286-302} $ og和$^{287-303} $ 119（168 $ \ leq $ n $ \ leq $ 184：稳定岛）的潜在衰减链被非常出色地达成了可用的实验数据。此外，使用四种不同的机器学习模型：XGBoost，Random Forest（RF），决策树（DTS）和多层感知器（MLP）神经网络用于培训$α$ - $ - 订单和SF半衰期预测的预测变量。使用XGBoost和MLP的衰减模式的预测与可用的实验衰变模式以及我们通过上述修改的公式获得的预测非常吻合。
Latest experimental and evaluated $α$-decay half-lives between 82$\leq$Z$\leq$118 have been used to modify two empirical formulas: (i) Horoi scaling law [J. Phys. G \textbf{30}, 945 (2004)], and Sobiczewski formula [Acta Phys. Pol. B \textbf{36}, 3095 (2005)] by adding asymmetry dependent terms ($I$ and $I^2$) and refitting of the coefficients. The results of these modified formulas are found with significant improvement while compared with other 21 formulas, and, therefore, are used to predict $α$-decay half-lives with more precision in the unknown superheavy region. The formula of spontaneous fission (SF) half-life proposed by Bao \textit{et al.} [J. Phys. G \textbf{42}, 085101 (2015)] is further modified by using ground-state shell-plus-pairing correction taken from FRDM-2012 and using latest experimental and evaluated spontaneous fission half-lives between 82$\leq$Z$\leq$118. Using these modified formulas, contest between $α$-decay and SF is probed for the nuclei within the range 112$\leq$Z$\leq$118 and consequently probable half-lives and decay modes are estimated. Potential decay chains of $^{286-302}$Og and $^{287-303}$119 (168$\leq$N$\leq$184: island of stability) are analyzed which are found in excellent agreement with available experimental data. In addition, four different machine learning models: XGBoost, Random Forest (RF), Decision Trees (DTs), and Multilayer Perceptron (MLP) neural network are used to train a predictor for $α$-decay and SF half-lives prediction. The prediction of decay modes using XGBoost and MLP are found in excellent agreement with available experimental decay modes along with our predictions obtained by above mentioned modified formulas.
