<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>α</mml:mi></mml:math>
-decay chain of the short-lived isotope 
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mmultiscripts><mml:mi mathvariant="bold">Pa</mml:mi><mml:mprescripts /><mml:none /><mml:mn>220</mml:mn></mml:mmultiscripts></mml:math>
 established using a digital pulse processing technique

Huang, T. H.; Zhang, W.Q.; Sun, Mingze; Liu, Z.; Wang, J. G.; Liu, X. Y.; Ding, Bing; Gan, Z. G.; Ma, Long; Yang, H. B.; Zhang, Z. Y.; Yu, Lingfei; Jiang, Jian; Wang, K. L.; Wang, Y. S.; Liu, M. L.; Li, Z. H.; Li, J.; Wang, X.; Lu, Haiyan; Lin, C. J.; Sun, L. J.; Ma, N. R.; Ren, Zhongzhou; Zhang, F. S.; Zou, W.; Zhou, Xianming; Xu, H. S.; Xiao, Guoqing

doi:10.1103/physrevc.96.014324

Cited by 17 publications

(4 citation statements)

References 13 publications

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“…Here, the Gaussian process training sets consist of, respectively, a set of 101 actinide nuclei with available experimental α-decay energies and a set of 102 actinide nuclei with available experimental α-decay half-lives (see the supplementary material). The corresponding experimental α-decay energies and half-lives are obtained from the AME2016 [71,72] and the NUBASE2016 [73], respectively. Regarding the nuclei with newly reported α-decay properties, such as [5,74], [3,75], and [1, 2, 7], we select the newest experimental results as the outputs for the Gaussian process training sets, while the α-decay properties of the newest neutron-deficient nucleus are not included.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

“…Regarding the nuclei with newly reported α-decay properties, such as [5,74], [3,75], and [1, 2, 7], we select the newest experimental results as the outputs for the Gaussian process training sets, while the α-decay properties of the newest neutron-deficient nucleus are not included. For the new experimental α-decay halflives with asymmetric uncertainties, their uncertainties are symmetrized as in NUBASE2016 [73]. To provide the systemic error for the Gaussian process, three different kernel functions are used in this work.…”

Section: Numerical Results and Discussionmentioning

confidence: 99%

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Theoretical predictions on α-decay properties of some unknown neutron-deficient actinide nuclei using machine learning *

Yuan¹,

Bai²,

Ren³

et al. 2022

Chinese Phys. C

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Neutron-deficient actinide nuclei provide a valuable window to probe heavy nuclear systems with large proton-neutron ratios. In recent years, several new neutron-deficient Uranium and Neptunium isotopes have been observed using α-decay spectroscopy [Z. Y. Zhang et al., Phys. Rev. Lett. 122, 192503 (2019); L. Ma et al., Phys. Rev. Lett. 125, 032502 (2020); Z. Y. Zhang et al., Phys. Rev. Lett. 126, 152502 (2021)]. In spite of these achievements, some neutron-deficient key nuclei in this mass region are still unknown in experiments. Machine learning algorithms have been applied successfully in different branches of modern physics. It is interesting to explore their applicability in α-decay studies. In this work, we propose a new model to predict the α-decay energies and half-lives within the framework based on a machine learning algorithm called the Gaussian process. We first calculate the α-decay properties of the new actinide nucleus . The theoretical results show good agreement with the latest experimental data, which demonstrates the reliability of our model. We further use the model to predict the α-decay properties of some unknown neutron-deficient actinide isotopes and compare the results with traditional models. The results may be useful for future synthesis and identification of these unknown isotopes.

show abstract

Section: Numerical Results and Discussionmentioning

confidence: 99%

Section: Numerical Results and Discussionmentioning

confidence: 99%

Theoretical predictions on α-decay properties of some unknown neutron-deficient actinide nuclei using machine learning *

Yuan¹,

Bai²,

Ren³

et al. 2022

Chinese Phys. C

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show abstract

“…At the neutron-deficient side of the nuclear chart, the α decay is one of the dominating decay modes for heavy nuclides. In recent years, by utilising a gas-filled separator, many new nuclides in the A≈200 region close to the proton drip line were newly synthesized successfully via heavy-ion induced fusion-evaporation reactions [1][2][3][4][5][6][7][8][9]. The known α-decay characteristics of such nuclides and their descendants help to study the new properties in the decay chains.…”

Section: Introductionmentioning

confidence: 99%

α-decay study of ²¹⁸Ac and ²²¹Th in ⁴⁰Ar+¹⁸⁶W reaction *

Zhang¹,

Ma²,

Gan³

et al. 2021

Chinese Phys. C

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In this study, 218Ac and 221Th nuclides were produced via the heavy-ion induced fusion evaporation reaction 40Ar + 186W. Their decay properties were studied with the help of the gas-filled recoil spectrometer SHANS and a digital data acquisition system. The cross section ratio between 222Pa and 218Ac was extracted experimentally, with measured value 0.69(9). Two new possible α decay branches to 221Th are suggested. The valence neutron configurations for the daughter 217Ra are discussed in terms of the hindrance factors.

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“…, [214][215][216]221 U [3,[6][7][8], 219,220,222−224 Np [1,2,[9][10][11], 220,221 Pa [12,13], 236 Bk [14], 240 Es [14], 和 224 Md [15]. 在这些研究中，α衰变是一种重要手段，通过对α衰变的研究，可以得到原子核的基态半衰期，结合能, 壳结构信息，是重核合成的重要鉴别手段。对 α 衰变的理论研究始于19世纪， Gamow [16]， Gurney [17] [24][25][26][27][28][29][30][31][32]，多道团簇模型(MCCM) [33,34]，通用衰变律(GLDM) [35,36]，四次波函数方法 [37,38]，以及四因子模型 [39]。一些经验公式 [40][41][42] [40,41]。从已有的经验公式中，α衰变的Q值,原子核的质子数和质量数Z，A被用来计算半衰期，半衰期与Q值成负相关，与Z和A成正相关。我们对源数据作如下处理:…”

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基于神经网络预测重核α衰变半衰期

Bu¹,

Wu²,

Bai³

2022

Sci. Sin.-Phys. Mech. Astron.

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α -decay chain of the short-lived isotope Pa220 established using a digital pulse processing technique

Cited by 17 publications

References 13 publications

Theoretical predictions on α-decay properties of some unknown neutron-deficient actinide nuclei using machine learning *

Theoretical predictions on α-decay properties of some unknown neutron-deficient actinide nuclei using machine learning *

α-decay study of ²¹⁸Ac and ²²¹Th in ⁴⁰Ar+¹⁸⁶W reaction *

基于神经网络预测重核α衰变半衰期

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α -decay chain of the short-lived isotope Pa220 established using a digital pulse processing technique

Cited by 17 publications

References 13 publications

Theoretical predictions on α-decay properties of some unknown neutron-deficient actinide nuclei using machine learning *

Theoretical predictions on α-decay properties of some unknown neutron-deficient actinide nuclei using machine learning *

α-decay study of 218Ac and 221Th in 40Ar+186W reaction *

基于神经网络预测重核&alpha;衰变半衰期

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α-decay study of ²¹⁸Ac and ²²¹Th in ⁴⁰Ar+¹⁸⁶W reaction *

基于神经网络预测重核α衰变半衰期