AI 中文总结
本研究将Λ(1405)共振复极点结构的歧义转化为CNN分类问题，利用CLAS数据的差分特征训练模型，确定其为两态系统，解析结构以[bt]叶两极点和[bb]叶一极点为主导。
AI 中文摘要
我们将Λ(1405)共振的复极点结构这一长期存在的歧义重新定义为卷积神经网络（CNN）的分类问题，通过使用CLAS实验数据上的针对性差分特征训练模型，识别近简并线形上的细微几何变化，在大推断样本上建立了数据驱动的共识。我们的结果表明，以[bt]叶上的两个极点和[bb]叶上的额外极点为特征的解析结构占据主导地位。基于推断的极点参数提取显示，Λ(1405)是一个两态系统，由位于$\bar{K}N$阈值下方的分子态和位于$\bar{\rm Σ}\bar{\rm π}$阈值上方的非分子态构成。
英文摘要
We revisit the long-standing ambiguity surrounding the complex pole structure of the $Λ(1405)$ resonance by reframing it as a classification problem for convolutional neural networks (CNNs). By training our models to recognize subtle geometric variations on nearly degenerate lineshapes using targeted differential feature on empirical CLAS data, we establish a data-driven consensus on large inference samples. Our results show that the analytic structure characterized by two poles on the $[bt]$ sheet and additional pole on the $[bb]$ sheet globally dominates. The inference-guided pole parameter extraction reveals that the $Λ(1405)$ is a two-state system, characterized by a molecular state sitting below the $\bar{K}N$ threshold and a non-molecular state lying above the $Σπ$ threshold.
Comments14 pages, 5 figures, comments are welcome
