通过归一化流进行领域自适应以改进 $Λ$ 信号提取
Improving $Λ$ Signal Extraction with Domain Adaptation via Normalizing Flows
AI总结：
本研究提出利用归一化流进行 Monte Carlo 模拟与数据间的领域自适应，有效减弱了 CLAS12 实验中 $Λ$ 超子信号提取时分类器品质因数对输出阈值的依赖。
摘要：
本研究提出了一种将归一化流用于领域自适应的新应用。该研究调查了基于流的神经网络改善 CLAS12 处 $Λ$ 超子信号提取的能力。归一化流能够帮助对描述物理过程的复杂概率密度函数进行建模，从而实现诸如事件生成等用途。通过使用分类器网络，$Λ$ 信号提取已得到改善，但模拟和数据域之间的差异限制了分类器性能；本研究利用流进行 Monte Carlo 模拟和数据之间的领域自适应。我们成功训练了一个流网络，以在潜在物理空间和正态分布之间进行转换。我们还发现，应用流减弱了品质因数对分类器输出阈值的依赖性，这意味着存在更宽的范围，在该范围内阈值产生相似的品质因数。
英文摘要：
The present study presents a novel application for normalizing flows for domain adaptation. The study investigates the ability of flow based neural networks to improve signal extraction of $Λ$ Hyperons at CLAS12. Normalizing Flows can help model complex probability density functions that describe physics processes, enabling uses such as event generation. $Λ$ signal extraction has been improved through the use of classifier networks, but differences in simulation and data domains limit classifier performance; this study utilizes the flows for domain adaptation between Monte Carlo simulation and data. We were successful in training a flow network to transform between the latent physics space and a normal distribution. We also found that applying the flows lessened the dependence of the figure of merit on the cut on the classifier output, meaning that there was a broader range where the cut results in a similar figure of merit.
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