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L2LFlows: Generating High-Fidelity 3D Calorimeter Images

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arxiv 2302.11594 v2 pith:REFHI2EZ submitted 2023-02-22 physics.ins-det hep-exhep-phphysics.data-an

L2LFlows: Generating High-Fidelity 3D Calorimeter Images

Sascha Diefenbacher , Engin Eren , Frank Gaede , Gregor Kasieczka , Claudius Krause , Imahn Shekhzadeh , David Shih This is my paper
classification physics.ins-det hep-exhep-phphysics.data-an
keywords calorimeterdetectorflowslayerslflowsnormalizingadaptedarchitecture
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 3 Pith papers

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  1. CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

    hep-ex 2026-06 conditional novelty 6.0

    A one-step generative model for calorimeter showers, using MeanFlow, a learned Gaussian-mixture prior, and a physics-constrained loss, matches diffusion-model quality at far fewer evaluations.

  2. CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters

    hep-ex 2026-06 unverdicted novelty 5.0

    Presents CaloTrilogy, a unified one-step generative model for high-granularity calorimeter showers that combines velocity field integration, learned priors, and physics losses to match SOTA quality.

  3. Amplitude Uncertainties Everywhere All at Once

    hep-ph 2025-08 unverdicted novelty 4.0

    Compares ensemble, Bayesian, and evidential regression approaches for uncertainty quantification in amplitude surrogates and shows they detect localized training data issues.