Deep generative models for detector signature simulation: A taxonomic review

Hashemi, Baran; Krause, Claudius

doi:10.1016/j.revip.2024.100092

Reviews in Physics

2024

DOI: 10.1016/j.revip.2024.100092

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Deep generative models for detector signature simulation: A taxonomic review

Baran Hashemi,

Claudius Krause

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2024

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Cited by 10 publications

References 126 publications

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Convolutional L2LFlows: generating accurate showers in highly granular calorimeters using convolutional normalizing flows

Buss,

Gaede,

Kasieczka

et al. 2024

J. Inst.

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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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Convolutional L2LFlows: generating accurate showers in highly granular calorimeters using convolutional normalizing flows

Buss,

Gaede,

Kasieczka

et al. 2024

J. Inst.

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Calibrating Bayesian generative machine learning for Bayesiamplification

Bieringer,

Diefenbacher,

Kasieczka

et al. 2024

Mach. Learn.: Sci. Technol.

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Recently, combinations of generative and Bayesian deep learning have been introduced in particle physics for both fast detector simulation and inference tasks.These neural networks aim to quantify the uncertainty on the generated distribution originating from limited training statistics.The interpretation of a distribution-wide uncertainty however remains ill-defined.We show a clear scheme for quantifying the calibration of Bayesian generative machine learning models.For a Continuous Normalizing Flow applied to a low-dimensional toy example, we evaluate the calibration of Bayesian uncertainties from either a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights, to gauge their behaviour on unsteady distribution edges. Well calibrated uncertainties can then be used to roughly estimate the number of uncorrelated truth samples that are equivalent to the generated sample and clearly indicate data amplification for smooth features of the distribution.

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Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry

Kobylianskii,

Soybelman,

Dreyer

et al. 2024

Phys. Rev. D

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Denoising diffusion models have gained prominence in various generative tasks, prompting their exploration for the generation of calorimeter responses. Given the computational challenges posed by detector simulations in high-energy physics experiments, the necessity to explore new machine-learning-based approaches is evident. This study introduces a novel graph-based diffusion model designed specifically for rapid calorimeter simulations. The methodology is particularly well-suited for low-granularity detectors featuring irregular geometries. We apply this model to the ATLAS dataset published in the context of the Fast Calorimeter Simulation Challenge 2022, marking the first application of a graph diffusion model in the field of particle physics. Published by the American Physical Society 2024

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Deep generative models for detector signature simulation: A taxonomic review

Cited by 10 publications

References 126 publications

Convolutional L2LFlows: generating accurate showers in highly granular calorimeters using convolutional normalizing flows

Convolutional L2LFlows: generating accurate showers in highly granular calorimeters using convolutional normalizing flows

Calibrating Bayesian generative machine learning for Bayesiamplification

Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry

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