Speaker
Description
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. A roadmap for integrating such a model into a production environment is also outlined.
Significance
We present for the first time a fast simulation model of TPC that demonstrates production-ready quality in the high-level tracking characteristics. We also outline a roadmap for integrating such a model into a production environment. This result may be of high interest for other HEP experiments in general, and for the ones that utilize TPC detectors as their main tracking system in particular.
References
https://doi.org/10.1140/epjc/s10052-021-09366-4
| Speaker time zone | Compatible with Europe |
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