Speaker
Maksim Borisov
(Dubna State University)
Description
The very high data acquisition rate as 20 GB/sec data flow resulting from a 3 MHz collision frequency is planned in the future SPD NICA experiment. It implies that tracks of several events will be overlapped and recorded in a single time-slice. Thus, after the step of recognizing all tracks in a time-slice, it is necessary to group the recognized tracks by events to determine their vertices. In this paper, a deep Siamese neural network with triplet loss function is proposed for this purpose. We present preliminary results of evaluation of the efficiency and speed metrics of the neural network after training on a dataset of simulated SPD data.
Primary author
Maksim Borisov
(Dubna State University)
Co-authors
Prof.
Gennady Ososkov
(Joint Institute for Nuclear Research)
Mr
Pavel Goncharov
(Joint Institute for Nuclear Research)
Daniil Rusov
(Joint Institute for Nuclear Research)
