Predicting 
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-decay energy with machine learning

Munoz, Jose M; Akkoyun, Serkan; Reyes, Zayda P.; Pachón, Leonardo A.

doi:10.1103/physrevc.107.034308

Cited by 6 publications

(1 citation statement)

References 24 publications

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“…应相关性质的研究 [21] , 如原子核的基态性质, 包括核质量(结合能) [22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38] 、电荷半径 [25,[39][40][41][42][43][44][45] 、磁矩 [46] 、分离能 [23,25,33,38] 、稳定性 [47,48] 、密度分布 [49][50][51] 、放射性核衰变分支比 [48,52] 等. 此外还有激发态 [53][54][55][56] 、 a衰变 [29,30,[57][58][59][60] 、b衰变 [52,[61][62][63]…”

Section: 目前机器学习已被广泛地应用于原子核结构和反unclassified

Prediction of ground-state spin in odd-<i>A</i> nuclei within decision tree

Wen,

Shang,

et al. 2023

Acta Phys. Sin.

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As a fundamental property of nuclei, ground-state spin has always been a hot topic in nuclear data and structure research. This paper extensively investigates the odd mass nuclei (odd-A nuclei) on the nuclide chart using decision trees, including odd proton nuclei (odd-Z nuclei) and odd neutron nuclei (odd-N nuclei), and trains ground-state spin prediction models for odd-Z nuclei and odd-N nuclei, respectively. In the case of randomly dividing the training and validation sets at a ratio of 75\% to 25\%, the accuracy rates of the training and validation sets for odd-Z nuclei reach 98.9\% and 79.3\%, respectively; The accuracy of the training set and validation set for the odd-N nuclei reach 98.6\% and 71.6\%, respectively. At the same time, through 1000 random selections of training and validation sets for repeated validation, the standard error of the accuracy obtained can be less than 5\%, further verifying the reliability and generalization performance of the decision tree; On the other hand, the accuracy of decision trees is much higher than that of theoretical models commonly used in nuclear structure research, such as Skyrme Hartree-Fock-Bogoliubov(SHFB), covariant density functional theory(CDFT), finite range droplet model(FRDM), etc. Next, using all spin-determined odd-Z and odd-N nuclei as the learning set, a total of 254 spin undetermined but recommended odd-Z and 268 corresponding odd-N nuclei were predicted for their ground-state spin values, with predicted set coincidence rates of 68.5\% and 69.0\%, respectively. Finally, four odd mass number chains, i.e., Z=59, Z=77, N=41, and N=59, are selected to compare the learning (prediction) results of the decision tree with the experimental (recommended) values of the corresponding nuclei and the results given by the three theoretical models, further demonstrating the research and application value of the decision tree in the ground-state spin of nuclei.

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