论文标题
在存在检测器效应的情况下，使用神经网络进行参数估计
Parameter Estimation using Neural Networks in the Presence of Detector Effects
论文作者
论文摘要
基于直方图的模板拟合是用于估计高能物理蒙特卡洛发生器参数的主要技术。参数化的神经网络重新加权可用于将此拟合过程扩展到许多维度，并且不需要汇总。如果要使用重建数据进行拟合，则必须使用昂贵的检测器模拟来训练神经网络。我们介绍了一种新的两级拟合方法，该方法仅需要一个带有检测器仿真的数据集，然后需要一组不包括检测器效应的其他生成级数据集。使用仿真数据集证明了基于与神经网络（SRGN）的重新释放发生器级事件（SRGN）的仿真级别拟合，用于各种示例，包括简单的高斯随机变量，Parton淋浴调整和Top Quark质量提取。
Histogram-based template fits are the main technique used for estimating parameters of high energy physics Monte Carlo generators. Parametrized neural network reweighting can be used to extend this fitting procedure to many dimensions and does not require binning. If the fit is to be performed using reconstructed data, then expensive detector simulations must be used for training the neural networks. We introduce a new two-level fitting approach that only requires one dataset with detector simulation and then a set of additional generation-level datasets without detector effects included. This Simulation-level fit based on Reweighting Generator-level events with Neural networks (SRGN) is demonstrated using simulated datasets for a variety of examples including a simple Gaussian random variable, parton shower tuning, and the top quark mass extraction.
