267 citations · 1k across the 39 of their papers we have counts for
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Towards Reliable Neural Generative Modeling of Detectors
Lucio Anderlini, Matteo Barbetti, Denis Derkach +3
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 produc…
Generative Adversarial Networks for the fast simulation of the Time Projection Chamber responses at the MPD detector
A. Maevskiy, F. Ratnikov, A. Zinchenko +3
The detailed detector simulation models are vital for the successful operation of modern high-energy physics experiments. In most cases, such detailed models require a significant…
Simulating the Time Projection Chamber responses at the MPD detector using Generative Adversarial Networks
A. Maevskiy, F. Ratnikov, A. Zinchenko +1
High energy physics experiments rely heavily on the detailed detector simulation models in many tasks. Running these detailed models typically requires a notable amount of the comp…
Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks
Artem Maevskiy, Denis Derkach, Nikita Kazeev +3
The increasing luminosities of future Large Hadron Collider runs and next generation of collider experiments will require an unprecedented amount of simulated events to be produced…