Book ; Online: Generative Adversarial Networks for the fast simulation of the Time Projection Chamber responses at the MPD detector
2022
Abstract: 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 amount of computing resources to run. Often this may not be afforded and ... ...
| Abstract | 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 amount of computing resources to run. Often this may not be afforded and less resource-intensive approaches are desired. In this work, we demonstrate the applicability of Generative Adversarial Networks (GAN) as the basis for such fast-simulation models for the case of the Time Projection Chamber (TPC) at the MPD detector at the NICA accelerator complex. Our prototype GAN-based model of TPC works more than an order of magnitude faster compared to the detailed simulation without any noticeable drop in the quality of the high-level reconstruction characteristics for the generated data. Approaches with direct and indirect quality metrics optimization are compared. Comment: Submitted for the proceedings of ACAT2021, https://indico.cern.ch/event/855454/contributions/4596732/ |
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| Keywords | Physics - Instrumentation and Detectors ; Computer Science - Machine Learning |
| Publishing date | 2022-03-30 |
| Publishing country | us |
| Document type | Book ; Online |
| Database | BASE - Bielefeld Academic Search Engine (life sciences selection) |
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