基于替代反应数据的中子诱发反应截面：重新审视 $(n,n^{\prime})$ 和 $(n,2n)$ 反应的 Weisskopf-Ewing 近似
Cross sections for neutron-induced reactions from surrogate data: revisiting the Weisskopf-Ewing approximation for $(n,n^{\prime})$ and $(n,2n)$ reactions
AI总结：
本文评估 Weisskopf-Ewing 近似用于替代反应数据推断 $(n,n^{\prime})$ 与 $(n,2n)$ 截面的有效性，指出其仅能估计峰值，可靠截面需刻画角动量差异并扩展反应机制建模。
摘要：
早期工作已经表明，中子诱发裂变和辐射中子俘获的截面可以通过替代反应数据与理论相结合来确定。对于裂变情形，研究表明可以采用 Weisskopf-Ewing 近似，该近似显著简化了替代方法的实施。俘获截面则无法由此获得，需要对替代反应过程进行详细描述。本文考察了 Weisskopf-Ewing 近似在从替代数据确定未知 $(n,n^{\prime})$ 和 $(n,2n)$ 截面时的有效性。我们发现，峰值截面可以利用 Weisskopf-Ewing 近似进行估计；但是，如果不考虑中子诱发反应与替代反应之间的角动量差异，就无法可靠确定 $(n,n^{\prime})$ 和 $(n,2n)$ 截面的形状，尤其是在低中子能区。为了从替代反应数据中获得可靠的 $(n,n^{\prime})$ 和 $(n,2n)$ 截面，需要对替代反应机制进行详细描述。而要在与这些反应相关的复合核能量和衰变通道上实现这一点，就必须扩展当前的建模能力。
英文摘要：
Earlier work has demonstrated that cross sections for neutron-induced fission and radiative neutron capture can be determined from a combination of surrogate reaction data and theory. For the fission case, it was shown that Weisskopf-Ewing approximation, which significantly simplifies the implementation of the surrogate method, can be employed. Capture cross sections cannot be obtained, and require a detailed description of the surrogate reaction process. In this paper we examine the validity of the Weisskopf-Ewing approximation for determining unknown $(n,n^{\prime})$ and $(n,2n)$ cross sections from surrogate data. We find that peak cross sections can be estimated using the Weisskopf-Ewing approximation, but the shape of the $(n,n^{\prime})$ and $(n,2n)$ cross sections, especially for low neutron energies, cannot be reliably determined without accounting for the angular-momentum differences between the neutron-induced and surrogate reaction. To obtain reliable $(n,n^{\prime})$ and $(n,2n)$ cross sections from surrogate reaction data, a detailed description of the surrogate reaction mechanisms is required. To do so for the compound-nucleus energies and decay channels relevant to these reactions, it becomes necessary to extend current modeling capabilities.
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。
