In the quest to build generative surrogate models as computationally
efficient alternatives to rule-based simulations, the quality of the generated
samples remains a crucial frontier. So far, normalizing flows have been among
the models with the best fidelity. However, as the latent space in such models
is required to have the same dimensionality as the data space, scaling up
normalizing flows to high dimensional datasets is not straightforward. The
prior L2LFlows approach successfully used a series of separate normalizing
flows and sequence of conditioning steps to circumvent this problem. In this
work, we extend L2LFlows to simulate showers with a 9-times larger profile in
the lateral direction. To achieve this, we introduce convolutional layers and
U-Net-type connections, move from masked autoregressive flows to coupling
layers, and demonstrate the successful modelling of showers in the ILD
Electromagnetic Calorimeter as well as Dataset 3 from the public CaloChallenge
dataset.

为了构建计算效率高且能代替基于规则的模拟的生成式替代模型，本研究扩展了 L2LFlows 以模拟具有较大剖面尺寸的洗澡水，并引入了卷积层和 U-Net 型连接，从掩蔽自回归流变为耦合层，在 ILD 电磁量热计以及公共 CaloChallenge 数据集中成功建模了洗澡水。