We present an alternative to reweighting techniques for modifying
distributions to account for a desired change in an underlying conditional
distribution, as is often needed to correct for mis-modelling in a simulated
sample. We employ conditional normalizing flows to learn the full conditional
probability distribution from which we sample new events for conditional values
drawn from the target distribution to produce the desired, altered
distribution. In contrast to common reweighting techniques, this procedure is
independent of binning choice and does not rely on an estimate of the density
ratio between two distributions.
In several toy examples we show that normalizing flows outperform reweighting
approaches to match the distribution of the target.We demonstrate that the
corrected distribution closes well with the ground truth, and a statistical
uncertainty on the training dataset can be ascertained with bootstrapping. In
our examples, this leads to a statistical precision up to three times greater
than using reweighting techniques with identical sample sizes for the source
and target distributions. We also explore an application in the context of high
energy particle physics.

通过条件归一化流来学习完整条件概率分布以匹配目标分布，消除模拟样本中的误建模，较之常见的重新加权技术，该方法独立于分组选择且不依赖于两个分布之间密度比率的估计，其可以提供高达三倍的统计精度，同时在高能粒子物理应用领域也具有一定的潜力。

条件归一化流在优化权重重构中的应用

Flow Away your Differences: Conditional Normalizing Flows as an  Improvement to Reweighting

We provide a bridge between generative modeling in the Machine Learning
community and simulated physical processes in High Energy Particle Physics by
applying a novel Generative Adversarial Network (GAN) architecture to the
production of jet images -- 2D representations of energy depositions from
particles interacting with a calorimeter. We propose a simple architecture, the
Location-Aware Generative Adversarial Network, that learns to produce realistic
radiation patterns from simulated high energy particle collisions. The pixel
intensities of GAN-generated images faithfully span over many orders of
magnitude and exhibit the desired low-dimensional physical properties (i.e.,
jet mass, n-subjettiness, etc.). We shed light on limitations, and provide a
novel empirical validation of image quality and validity of GAN-produced
simulations of the natural world. This work provides a base for further
explorations of GANs for use in faster simulation in High Energy Particle
Physics.

我们通过应用一种新颖的生成对抗网络（GAN）架构到喷气图像的生成，提供了机器学习社区中生成建模与高能粒子物理中模拟物理过程之间的桥梁，并提出了一种简单的位置感知生成对抗网络。这项工作为将 GAN 用于高能粒子物理中的快速模拟提供了基础。