{"title":"用机器学习绘制偶偶数原子核中的低洼状态和[式省略]图谱","authors":"","doi":"10.1016/j.physletb.2024.139013","DOIUrl":null,"url":null,"abstract":"<div><p>A machine-learning algorithm, Light Gradient Boosting Machine, was applied for the first time to investigate the fundamental experimental observables in even-even nuclei over the Segrè chart. Specifically, we focused on the excitation energies of the <span><math><msubsup><mrow><mn>2</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup></math></span> and <span><math><msubsup><mrow><mn>4</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup></math></span> states, and the reduced electric quadrupole transition probability <span><math><mi>B</mi><mo>(</mo><mi>E</mi><mn>2</mn><mo>;</mo><msubsup><mrow><mn>0</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup><mo>→</mo><msubsup><mrow><mn>2</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup><mo>)</mo></math></span>. Present obtained results well reproduced experimental data within an accuracy of 1.07, 1.05, and 1.14 times for the <span><math><msubsup><mrow><mn>2</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup></math></span> and <span><math><msubsup><mrow><mn>4</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup></math></span> states as well as <span><math><mi>B</mi><mo>(</mo><mi>E</mi><mn>2</mn><mo>;</mo><msubsup><mrow><mn>0</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup><mo>→</mo><msubsup><mrow><mn>2</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup><mo>)</mo></math></span>, respectively, being significantly precise than the results from any state-of-the-art nuclear models and from any machine-learning-based approaches. The predictive capability of our machine learning methodology was further validated using 17 newly measured data points which were not used in the training set. Taking O, Ca, Sn, and Pb isotopes as examples, it has been found that our methodology precisely captures both the isotopic trend and absolute values, surpassing all theoretical models hitherto. Our findings reveal the double-magic nature of <sup>100</sup>Sn and the disappearance of the <span><math><mi>N</mi><mo>=</mo><mn>20</mn></math></span> shell in <sup>28</sup>O.</p></div>","PeriodicalId":20162,"journal":{"name":"Physics Letters B","volume":null,"pages":null},"PeriodicalIF":4.3000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0370269324005719/pdfft?md5=a45bf39ff4382db17c2c35b174265e2a&pid=1-s2.0-S0370269324005719-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Mapping low-lying states and B(E2;01+→21+) in even-even nuclei with machine learning\",\"authors\":\"\",\"doi\":\"10.1016/j.physletb.2024.139013\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A machine-learning algorithm, Light Gradient Boosting Machine, was applied for the first time to investigate the fundamental experimental observables in even-even nuclei over the Segrè chart. Specifically, we focused on the excitation energies of the <span><math><msubsup><mrow><mn>2</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup></math></span> and <span><math><msubsup><mrow><mn>4</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup></math></span> states, and the reduced electric quadrupole transition probability <span><math><mi>B</mi><mo>(</mo><mi>E</mi><mn>2</mn><mo>;</mo><msubsup><mrow><mn>0</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup><mo>→</mo><msubsup><mrow><mn>2</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup><mo>)</mo></math></span>. Present obtained results well reproduced experimental data within an accuracy of 1.07, 1.05, and 1.14 times for the <span><math><msubsup><mrow><mn>2</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup></math></span> and <span><math><msubsup><mrow><mn>4</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup></math></span> states as well as <span><math><mi>B</mi><mo>(</mo><mi>E</mi><mn>2</mn><mo>;</mo><msubsup><mrow><mn>0</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup><mo>→</mo><msubsup><mrow><mn>2</mn></mrow><mrow><mn>1</mn></mrow><mrow><mo>+</mo></mrow></msubsup><mo>)</mo></math></span>, respectively, being significantly precise than the results from any state-of-the-art nuclear models and from any machine-learning-based approaches. The predictive capability of our machine learning methodology was further validated using 17 newly measured data points which were not used in the training set. Taking O, Ca, Sn, and Pb isotopes as examples, it has been found that our methodology precisely captures both the isotopic trend and absolute values, surpassing all theoretical models hitherto. Our findings reveal the double-magic nature of <sup>100</sup>Sn and the disappearance of the <span><math><mi>N</mi><mo>=</mo><mn>20</mn></math></span> shell in <sup>28</sup>O.</p></div>\",\"PeriodicalId\":20162,\"journal\":{\"name\":\"Physics Letters B\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0370269324005719/pdfft?md5=a45bf39ff4382db17c2c35b174265e2a&pid=1-s2.0-S0370269324005719-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physics Letters B\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0370269324005719\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ASTRONOMY & ASTROPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physics Letters B","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0370269324005719","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 0
摘要
我们首次采用了一种机器学习算法--光梯度提升机(Light Gradient Boosting Machine)--来研究偶偶数原子核在塞格雷图上的基本实验观测数据。具体来说,我们重点研究了和状态的激发能量以及还原电四极转换概率。目前所获得的结果很好地再现了实验数据,对于和态以及Ⅴ态,精确度分别为1.07倍、1.05倍和1.14倍,比任何最先进的核模型和基于机器学习的方法的结果都要精确得多。我们使用 17 个新测量的数据点进一步验证了机器学习方法的预测能力。以 O、Ca、Sn 和 Pb 同位素为例,我们发现我们的方法精确地捕捉到了同位素趋势和绝对值,超越了迄今为止所有的理论模型。我们的研究结果揭示了 Sn 的双重魔力和 O 的外壳消失。
Mapping low-lying states and B(E2;01+→21+) in even-even nuclei with machine learning
A machine-learning algorithm, Light Gradient Boosting Machine, was applied for the first time to investigate the fundamental experimental observables in even-even nuclei over the Segrè chart. Specifically, we focused on the excitation energies of the and states, and the reduced electric quadrupole transition probability . Present obtained results well reproduced experimental data within an accuracy of 1.07, 1.05, and 1.14 times for the and states as well as , respectively, being significantly precise than the results from any state-of-the-art nuclear models and from any machine-learning-based approaches. The predictive capability of our machine learning methodology was further validated using 17 newly measured data points which were not used in the training set. Taking O, Ca, Sn, and Pb isotopes as examples, it has been found that our methodology precisely captures both the isotopic trend and absolute values, surpassing all theoretical models hitherto. Our findings reveal the double-magic nature of 100Sn and the disappearance of the shell in 28O.
期刊介绍:
Physics Letters B ensures the rapid publication of important new results in particle physics, nuclear physics and cosmology. Specialized editors are responsible for contributions in experimental nuclear physics, theoretical nuclear physics, experimental high-energy physics, theoretical high-energy physics, and astrophysics.