发表机构
organization= Instituto de F\' sica Corpuscular, CSIC - Universidad de Valencia, Spain , postcode= 46980 , state= Valencia , country= Spain
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究对比总吸收γ能谱去卷积的监督机器学习与响应矩阵方法，发现前者精度更优、后者初始解稳健，支持混合策略提升整体性能。
AI 中文摘要
从总吸收γ能谱中提取β衰变 feeding 分布是一个具有挑战性的逆问题，尤其适用于具有大量激发态的复杂衰变方案的原子核。在这种情况下，测量到的能谱由众多探测器响应函数叠加而成，因此确定单个 feeding 本质上是不适定的，且对所采用的方法高度敏感。本工作使用实验总吸收谱仪的真实蒙特卡罗模拟，对监督机器学习技术与响应矩阵方法进行了系统比较。监督机器学习方法在训练阶段后构建非参数估计器，从测量能谱中推断能级 feeding；而响应矩阵方法则通过最小化测量能谱与重建能谱之间的差异来确定 feeding 分布。结果表明，监督机器学习技术在单个 feeding 强度的重建中实现了更优的精度，而响应矩阵方法提供了稳健且符合物理规律的初始解。这些发现支持一种混合策略：首先使用响应矩阵方法获得初始 feeding 估计，随后用监督机器学习方法对其进行优化，以实现更高的整体精度。
英文摘要
The extraction of $β$-feeding distributions in Total Absorption $γ$-ray Spectroscopy constitutes a challenging inverse problem, particularly in nuclei with complex decay schemes involving a large number of excited states. In such cases, the measured spectrum arises from the superposition of many detector response functions, making the determination of the individual feedings intrinsically ill-posed and highly sensitive to the methodology employed. In this work, we present a systematic comparison between supervised Machine-Learning techniques and Response-Matrix methods using realistic Monte Carlo simulations of an experimental Total Absorption Spectrometer. Supervised Machine-Learning approaches construct a non-parametric estimator that infers level feedings from the measured spectrum after a training stage, whereas Response-Matrix methods determine the feeding distribution by directly minimizing the difference between measured and reconstructed spectra. Our results show that supervised Machine-Learning techniques achieve superior accuracy in the reconstruction of individual feeding intensities, whereas Response-Matrix methods provide robust and physically consistent initial solutions. These findings support a hybrid strategy in which a Response-Matrix method is first used to obtain an initial feeding estimate, which is then refined using a supervised Machine-Learning approach to achieve improved overall accuracy.
