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arXiv 2402.11278nucl-th
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基于随机相互作用系综的原子核基态自旋分布的神经网络研究

Neural network study on nuclear ground-state spin distribution within random interaction ensemble

Deng Liu, Alam Noor A, Zhenzhen Qin, Yang Lei

AI总结:

本研究使用分类神经网络模型,以两体相互作用矩阵元为输入特征,探究了二体随机系综(TBRE)中的原子核基态自旋分布。尽管模型无法精确预测单个样本的基态自旋,但能有效捕捉其统计特性,表明其成功学习到了人类物理学家所发现的基态自旋分布经验规律。

摘要:

本文使用一个通用的分类神经网络(NN)模型,以两体相互作用矩阵元作为输入特征,相应的基态自旋作为标签或输出预测,研究了二体随机系综(TBRE)中的原子核基态自旋分布。当涉及准确预测TBRE中每个样本的基态自旋时,量子多体系统问题似乎超出了我们优化后的神经网络的能力范围。然而,我们的神经网络模型有效地捕捉了基态自旋的统计特性。这可能归因于神经网络(NN)模型学习到了人类物理学家发现的TBRE中基态自旋分布的经验规律。

英文摘要:

The distribution of nuclear ground-state spin in the two-body random ensemble (TBRE) is studied by using a general classification neural network (NN) model with the two-body interaction matrix elements as input features and corresponding ground-state spins as labels or output predictions. It seems that quantum many-body system problem exceeds the capability of our optimized neural networks when it comes to accurately predicting the ground-state spin of each sample within the TBRE. However, our neural network model effectively captures the statistical properties of the ground-state spin. This may be attributed to the fact that the neural network (NN) model has learned the empirical regularity of the ground-state spin distribution in TBRE, as discovered by human physicists.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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