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Электронный каталог: Borisov, M. - Unraveling Time-Slices of Events in the SPD Experiment
Borisov, M. - Unraveling Time-Slices of Events in the SPD Experiment
Статья
Автор: Borisov, M.
Физика элементарных частиц и атомного ядра: Unraveling Time-Slices of Events in the SPD Experiment : [Abstract]
б.г.
ISBN отсутствует
Автор: Borisov, M.
Физика элементарных частиц и атомного ядра: Unraveling Time-Slices of Events in the SPD Experiment : [Abstract]
б.г.
ISBN отсутствует
Статья
Borisov, M.
Unraveling Time-Slices of Events in the SPD Experiment : [Abstract] / M.Borisov, P.Goncharov, G.Ososkov, D.Rusov // Физика элементарных частиц и атомного ядра. – 2024. – Т. 55, № 3. – P. 592. – URL: http://www1.jinr.ru/Pepan/v-55-3/40_Borisov_ann.pdf.
The very high data acquisition rate as 20 GB/s 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.
Спец.(статьи,препринты) = Ц 840 в - Программы обработки экспериментальных данных и управление физическими установками$
ОИЯИ = ОИЯИ (JINR)2024
Borisov, M.
Unraveling Time-Slices of Events in the SPD Experiment : [Abstract] / M.Borisov, P.Goncharov, G.Ososkov, D.Rusov // Физика элементарных частиц и атомного ядра. – 2024. – Т. 55, № 3. – P. 592. – URL: http://www1.jinr.ru/Pepan/v-55-3/40_Borisov_ann.pdf.
The very high data acquisition rate as 20 GB/s 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.
Спец.(статьи,препринты) = Ц 840 в - Программы обработки экспериментальных данных и управление физическими установками$
ОИЯИ = ОИЯИ (JINR)2024