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arXiv · 2204.09947

Towards Reliable Neural Generative Modeling of Detectors

Lucio Anderlini·Matteo Barbetti·Denis Derkach·Nikita Kazeev·Artem Maevskiy·Sergei Mokhnenko
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Abstract

The increasing luminosities of future data taking at Large Hadron Collider and next generation collider experiments require an unprecedented amount of simulated events to be produced. Such large scale productions demand a significant amount of valuable computing resources. This brings a demand to use new approaches to event generation and simulation of detector responses. In this paper, we discuss the application of generative adversarial networks (GANs) to the simulation of the LHCb experiment events. We emphasize main pitfalls in the application of GANs and study the systematic effects in detail. The presented results are based on the Geant4 simulation of the LHCb Cherenkov detector.

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Lucio Anderlini, Matteo Barbetti, Denis Derkach, Nikita Kazeev, Artem Maevskiy, Sergei Mokhnenko. 2022-04-21. Towards Reliable Neural Generative Modeling of Detectors. https://doi.org/10.1088/1742-6596%2F2438%2F1%2F012130

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