Paraflow: fast calorimeter simulations parameterized in upstream material configurations
Abstract We study whether machine-learning models for fast calorimeter simulations can learn meaningful representations of calorimeter signatures that account for variations in the full particle detector’s configuration. This may open new opportunities in high-energy physics measurements, for exampl...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-08-01
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| Series: | European Physical Journal C: Particles and Fields |
| Online Access: | https://doi.org/10.1140/epjc/s10052-025-14604-0 |
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