论文标题
用于蒙特卡洛重新加权的神经重采样器，并保留了不确定性
A Neural Resampler for Monte Carlo Reweighting with Preserved Uncertainties
论文作者
论文摘要
蒙特卡洛事件发生器是对撞机物理学数据分析的重要工具。为了包括跨量子校正，这些发电机通常需要产生负重事件，这会导致数据集的统计稀释和检测器模拟的下游计算成本。我们基于最新的重新采样方法来重新平衡直方图箱内的重新平衡，我们引入了神经重采样：基于神经网络的蒙特卡洛重新加权的无键方法，可以很好地扩展到高维和可变维相位空间。我们特别注意保留事件样本的统计特性，因此神经重采样不仅保持了任何可观察到的平均值，而且还保持其蒙特卡洛不确定性。这种不确定性保存方案是一般的，也可以应用于BINNED（非神经网络）重新采样。为了说明我们的神经重采样方法，我们在与Parton淋浴相匹配的近代Quark Pair生产的大型夸克对生产的大型强子对撞机中介绍了一项案例研究。
Monte Carlo event generators are an essential tool for data analysis in collider physics. To include subleading quantum corrections, these generators often need to produce negative weight events, which leads to statistical dilution of the datasets and downstream computational costs for detector simulation. Building on the recent proposal of a positive resampler method to rebalance weights within histogram bins, we introduce neural resampling: an unbinned approach to Monte Carlo reweighting based on neural networks that scales well to high-dimensional and variable-dimensional phase space. We pay particular attention to preserving the statistical properties of the event sample, such that neural resampling not only maintains the mean value of any observable but also its Monte Carlo uncertainty. This uncertainty preservation scheme is general and can also be applied to binned (non-neural network) resampling. To illustrate our neural resampling approach, we present a case study from the Large Hadron Collider of top quark pair production at next-to-leading order matched to a parton shower.
