信号模型参数扫描：基于归一化流
Signal model parameter scan using Normalizing Flow
AI总结：
本文提出基于归一化流的BSM信号模型参数扫描方法，利用可逆变换生成伪数据并计算似然梯度，实现高效参数搜索，并在简单数据集上验证了可行性。
摘要：
本文提出了一种基于归一化流的BSM信号模型参数扫描技术。归一化流是一种深度学习模型，通过可逆函数将简单概率分布转换为复杂概率分布。通过学习复杂多维分布（如对撞机实验中观测到的实验数据）与多维正态分布之间的可逆变换，归一化流模型能够从随机数中采样（或生成）伪实验数据，并从多维观测事件中评估对数似然值。归一化流模型还可以扩展为接受多维条件变量作为输入。因此，归一化流模型可用作由BSM模型参数条件化的伪实验数据的生成器和评估器。对数似然值（归一化流模型的输出）是条件变量的函数。因此，该模型可以快速计算对数似然相对于条件变量的梯度。基于这一特性，可以高效搜索最可能重现实验数据的条件变量集合，即BSM模型的最优参数集。本文在简单数据集上演示了该方法，并讨论了其局限性和未来扩展方向。
英文摘要：
This paper presents a parameter scan technique for BSM signal models based on normalizing flow. Normalizing flow is a type of deep learning model that transforms a simple probability distribution into a complex probability distribution as an invertible function. By learning an invertible transformation between a complex multidimensional distribution, such as experimental data observed in collider experiments, and a multidimensional normal distribution, the normalizing flow model gains the ability to sample (or generate) pseudo experimental data from random numbers and to evaluate a log-likelihood value from multidimensional observed events. The normalizing flow model can also be extended to take multidimensional conditional variables as arguments. Thus, the normalizing flow model can be used as a generator and evaluator of pseudo experimental data conditioned by the BSM model parameters. The log-likelihood value, the output of the normalizing flow model, is a function of the conditional variables. Therefore, the model can quickly calculate gradients of the log-likelihood to the conditional variables. Following this property, it is expected that the most likely set of conditional variables that reproduce the experimental data, i.e. the optimal set of parameters for the BSM model, can be efficiently searched. This paper demonstrates this on a simple dataset and discusses its limitations and future extensions.
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。
