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arXiv 2506.08044nucl-th
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利用多层感知器神经网络估算 6Li(d,α)4He 反应中的电子屏蔽势

Estimation of Electron Screening Potential in the 6Li(d,α)4He Reaction Using Multi-Layer Perceptron Neural Network

D. Chattopadhyay

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AI总结：

本文将基于 MLP 的人工神经网络用于重新分析 6Li(d,α)4He 反应的 S 因子数据，提取裸 S 因子并估算电子屏蔽势，得到 147.95 eV。

摘要：

在亚库仑能量下，轻带电核之间的反应在天体物理环境中至关重要，但准确的截面测量受到电子屏蔽的阻碍。包括多项式外推和特洛伊木马法在内的传统方法，通常会得到超过绝热预测的屏蔽势。在基于 MLP 的人工神经网络（ANN）成功用于 6Li(p,α)3He 反应的基础上[1]，本工作将同一方法应用于 6Li(d,α)4He 反应。研究使用 ANN 对能量依赖的 S 因子建模，重新分析了文献中的实验天体物理 S 因子数据。裸 S 因子从屏蔽效应极小的 70 keV 以上数据中提取，并通过与低能区比较获得屏蔽势。所得屏蔽势为 147.95 eV，证明了基于 ANN 的方法在评估涉及轻核的低能核反应中电子屏蔽的稳健性。

英文摘要：

Reactions between light charged nuclei at sub-Coulomb energies are crucial in astrophysical environments, but accurate cross-section measurements are hindered by electron screening. Traditional methods, including polynomial extrapolation and the Trojan Horse Method, often yield screening potentials exceeding adiabatic predictions. Building on the success of an MLP-based Artificial Neural Network (ANN) for the 6Li(p, α)3He reaction [1], this work applies the same approach to the 6Li(d, α)4He reaction. Experimental astrophysical S-factor data from literature are reanalyzed using the ANN to model the energy-dependent S-factor. The bare S-factor is extracted from data above 70 keV, where screening effects are minimal, and the screening potential is obtained by comparing with the low-energy region. The resulting screening potential is 147.95 eV, demonstrating the robustness of ANN-based methods for evaluating electron screening in low-energy nuclear reactions involving light nuclei.

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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