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.

翻译：在构建生成式代理模型作为基于规则模拟的计算高效替代方案的过程中，生成样本的质量仍是一个关键前沿。迄今为止，归一化流一直属于保真度最佳的模型之一。然而，由于此类模型的潜空间需要与数据空间具有相同维度，将归一化流扩展到高维数据集并非易事。先前的L2LFlows方法通过使用一系列独立的归一化流和条件步骤序列成功规避了这一问题。在本工作中，我们将L2LFlows扩展至模拟横向轮廓扩大9倍的簇射。为实现这一目标，我们引入了卷积层和U-Net型连接结构，从掩码自回归流转向耦合层，并成功演示了对ILD电磁量热器以及公开CaloChallenge数据集中Dataset 3的簇射建模。