L2LFlows: Generating High-Fidelity 3D Calorimeter Images

Abstract

We explore the use of normalizing flows to emulate Monte Carlo detector simulations of photon showers in a high-granularity electromagnetic calorimeter prototype for the International Large Detector (ILD). Our proposed method -- which we refer to as "Layer-to-Layer-Flows" (L$2$LFlows) -- is an evolution of the CaloFlow architecture adapted to a higher-dimensional setting (30 layers of $10\times 10$ voxels each). The main innovation of L$2$LFlows consists of introducing $30$ separate normalizing flows, one for each layer of the calorimeter, where each flow is conditioned on the previous five layers in order to learn the layer-to-layer correlations. We compare our results to the BIB-AE, a state-of-the-art generative network trained on the same dataset and find our model has a significantly improved fidelity.

v2: 28 pages, 13 figures; matches version accepted for publication in JINST. Neither SISSA Medialab Srl nor IOP Publishing Ltd is responsible for any errors or omissions in this version of the manuscript or any version derived from it. Published version available via DOI

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• Deep generative models for fast photon shower simulation in ATLAS
• Inductive Simulation of Calorimeter Showers with Normalizing Flows
• The MadNIS Reloaded
• The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
• High-dimensional and Permutation Invariant Anomaly Detection
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
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• Normalizing Flows for High-Dimensional Detector Simulations
• Calorimeter shower superresolution
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Differentiable MadNIS-Lite
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• Accurate Surrogate Amplitudes with Calibrated Uncertainties
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
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• Extrapolating Jet Radiation with Autoregressive Transformers
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• BitHEP -- The Limits of Low-Precision ML in HEP
• Observable Optimization for Precision Theory: Machine Learning Energy Correlators
• ParaFlow: fast calorimeter simulations parameterized in upstream material configurations