1. Particle Track Reconstruction with the TrackNETv2.
- Author
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Goncharov, Pavel, Ososkov, Gennady, and Baranov, Dmitriy
- Subjects
- *
RECURRENT neural networks , *PARTICLES - Abstract
Modern high-energy physical detectors provide a huge amount of data, and for future high-luminosity colliders such as NICA, an Exabyte data scale is expected. Particle track reconstruction in such a dense environment is a very challenging task. Common algorithms make use of hand-engineered features and do not scale well with detector occupancy. This work is a logical continuation of the research presented on the XXII International Scientific Conference of Young Scientists and Specialists (AYSS-2018), where we have already introduced the approach of sequential tracking with the help of ad-hoc deep recurrent neural network named TrackNET. But that first version had a number of shortcomings. To address them, we introduce here the TrackNETv2, which is faster, has much fewer parameters and is able to be trained directly from Monte-Carlo simulated events. [ABSTRACT FROM AUTHOR]
- Published
- 2019
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