Modified empirical formulas and machine learning for α-decay systematics

Abstract

Latest experimental and evaluated α-decay half-lives between 82 ⩽ Z ⩽ 118 have been used to modify two empirical formulas: (i) Horoi scaling law (2004 J. Phys. G: Nucl. Part. Phys. 30 945), and Sobiczewski formula (2005 Acta Phys. Pol. B 36 3095) 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 et al (2015 J. Phys. G: Nucl. Part. Phys. 42 085101) is further modified by using ground-state shell-plus-pairing correction taken from FRDM-2012 and using the latest experimental and evaluated SF half-lives between 82 ⩽ Z ⩽ 118. Using these modified formulas, contest between α-decay and SF is probed for the nuclei within the range 112 ⩽ Z ⩽ 118 and consequently probable half-lives and decay modes are estimated. Potential decay chains of 286−302Og and 287−303119 (168 ⩽ N ⩽ 184: island of stability) are analyzed which are found to be in excellent agreement with available experimental data. In addition, four different machine learning models: XGBoost, random forest, decision trees, 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 to be in excellent agreement with available experimental decay modes along with our predictions obtained by the above-mentioned modified formulas.

Structural properties and α-decay chains of transfermium nuclei (101≤Z≤110)

“…The variance in decay modes is found only in a few cases in Tables 4 and 5 which may be due to the fact that the spontaneous fission half-lives are calculated by using the formula of Silisteanu et al [87] that is fitted by experimental data upto 2015. A new fit with latest experimental data in the formula of spontaneous fission formula may lead to better agreement which is one of the objective of our upcoming work [90]. Tables 4 and 5 show the decay chains starting from Ds and Mt for odd and even A nuclei.…”

Study on nuclear $α$-decay energy by an artificial neural network with pairing and shell effects

“…In recent years, machine learning has been used in the research of nuclear physics [26][27][28][29][30][31][32]. The artificial neural networks is employed on experimental nuclear charge radii and ground-state energies of nuclei [26,33].…”

“…For the α-decay energy, to the best of our knowledge, except for the studies in Refs. [31] and [32], it is rarely investigated by using the machine learning method. In Ref.…”

Cluster radioactivity in 294,296Og

“…Few new efforts are underway in this superheavy region [30][31][32][33]. Due to the large number of nucleons, these nuclei play a very important role in nuclear and atomic physics, chemistry and astrophysics [34][35][36][37] as well as also have been recently explained in some of our recent works [38][39][40][41].…”

“…In the present work, we have discussed the fission phenomenon of superheavy nuclei 294,296 Og into possible even-even isotopes (clusters) from He to Mo (Z= 2−42). Since, the half-lives of α-decay and cluster emission mainly depend on disintegration energy (Qvalue) [42], therefore, the estimated Q-values of unknown superheavy nuclei are taken from WS4 mass model [43] which is found with minimum RMSE [40,44] while compared to other similar mass models to predict the half-lives of α-decay. For the calculations of halflives of α-decay and cluster emission, we use Horoi formula [45], the formula by Ren et al [46], NRDX formula [47], universal decay formula (UDL) [48], and universal curve formula (UNIV) [49].…”