机器学习赋能的SECAR反冲分离器天体物理(p,n)反应测量
Machine-Learning-Enabled Measurements of Astrophysical (p,n) Reactions with the SECAR Recoil Separator
AI总结：
该研究将机器学习与离子光学模拟结合，为SECAR反冲分离器找到新方案，首次实现质量几乎不变的天体物理(p,n)反应测量，并以$^{58}$Fe(p,n)$^{58}$Co反应验证，截面20.3±6.3 mb，推动不稳定核反应研究。
摘要：
超新星中重元素的合成受到不稳定核上低能(n,p)和(p,n)反应的影响，然而关于此类反应速率的实验数据十分匮乏。位于FRIB（稀有同位素束设施）的SECAR（俘获反应分离器）反冲分离器最初设计用于测量显著改变原子核质量的 Astrophysics 反应。我们采用了一种将机器学习与离子光学模拟相结合的新方法，为分离器找到了一种离子光学解决方案，使得尽管反应后原子核质量几乎不变，仍能测量(p,n)反应。在逆运动学条件下，使用3.66±0.12 MeV/核子$^{58}$Fe束流（对应正常运动学下3.69±0.12 MeV质子能量）对$^{58}$Fe(p,n)$^{58}$Co反应进行了新测量，测得截面为20.3±6.3 mb，该测量作为新技术的基准，证明了其在达到所需性能标准方面的有效性。这一新方法标志着实验核天体物理学的重大进步，因为它为研究FRIB产生的不稳定核上具有天体物理重要性的(p,n)反应铺平了道路。
英文摘要：
The synthesis of heavy elements in supernovae is affected by low-energy (n,p) and (p,n) reactions on unstable nuclei, yet experimental data on such reaction rates are scarce. The SECAR (SEparator for CApture Reactions) recoil separator at FRIB (Facility for Rare Isotope Beams) was originally designed to measure astrophysical reactions that change the mass of a nucleus significantly. We used a novel approach that integrates machine learning with ion-optical simulations to find an ion-optical solution for the separator that enables the measurement of (p,n) reactions, despite the reaction leaving the mass of the nucleus nearly unchanged. A new measurement of the $^{58}$Fe(p,n)$^{58}$Co reaction in inverse kinematics with a 3.66$\pm$0.12 MeV/nucleon $^{58}$Fe beam (corresponding to 3.69$\pm$0.12 MeV proton energy in normal kinematics) yielded a cross-section of 20.3$\pm$6.3 mb and served as a benchmark for the new technique demonstrating its effectiveness in achieving the required performance criteria. This novel approach marks a significant advancement in experimental nuclear astrophysics, as it paves the way for studying astrophysically important (p,n) reactions on unstable nuclei produced at FRIB.
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