Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913491292520448 |
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| author | Buss, Thorsten Gaede, Frank Kasieczka, Gregor Krause, Claudius Shih, David |
| author_facet | Buss, Thorsten Gaede, Frank Kasieczka, Gregor Krause, Claudius Shih, David |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_20407 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows Buss, Thorsten Gaede, Frank Kasieczka, Gregor Krause, Claudius Shih, David Instrumentation and Detectors Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology Data Analysis, Statistics and Probability 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. |
| title | Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows |
| topic | Instrumentation and Detectors Machine Learning High Energy Physics - Experiment High Energy Physics - Phenomenology Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2405.20407 |