Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows

Abstract

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.

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• NICE: Non-linear Independent Components Estimation
• CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
• Unsupervised in-distribution anomaly detection of new physics through conditional density estimation
• Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics
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Cited by in corpus (10)

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• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
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• Amplitude Uncertainties Everywhere All at Once
• Extrapolating Jet Radiation with Autoregressive Transformers
• CaloHadronic: a diffusion model for the generation of hadronic showers
• Observable Optimization for Precision Theory: Machine Learning Energy Correlators
• ParaFlow: fast calorimeter simulations parameterized in upstream material configurations
• Ultra Fast Calorimeter Simulation with Generative Machine Learning on FPGAs