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Электронный каталог: Nikolskaiia, A. - Point Cloud Transformer for Elementary Particle Signals Segmentation
Nikolskaiia, A. - Point Cloud Transformer for Elementary Particle Signals Segmentation
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Статья
Автор: Nikolskaiia, A.
Физика элементарных частиц и атомного ядра: Point Cloud Transformer for Elementary Particle Signals Segmentation : [Abstract]
б.г.
ISBN отсутствует
Автор: Nikolskaiia, A.
Физика элементарных частиц и атомного ядра: Point Cloud Transformer for Elementary Particle Signals Segmentation : [Abstract]
б.г.
ISBN отсутствует
Статья
Nikolskaiia, A.
Point Cloud Transformer for Elementary Particle Signals Segmentation : [Abstract] / A.Nikolskaiia, P.Goncharov, G.Ososkov, D.Rusov, D.Starikov // Физика элементарных частиц и атомного ядра. – 2024. – Т. 55, № 3. – P. 597. – URL: http://www1.jinr.ru/Pepan/v-55-3/42_Nikolskaia_ann.pdf.
Each experimental setup in high-energy physics experiments has its own specifics of tracking detectors and data acquisition system. For instance, SPD NICA track detectors will produce a huge number of fake measurements and other noisy signals, which can exceed the number of true ones by two orders of magnitude. In this paper, we present the Transformer-based architecture for the elimination of fake measurements from the simulated data for the SPD experiment. We describe an efficient method for utilizing self-attention modules with squared algorithmic and memory complexity to the simulated data by voxelization procedure.
Спец.(статьи,препринты) = Ц 840 в - Программы обработки экспериментальных данных и управление физическими установками$
ОИЯИ = ОИЯИ (JINR)2024
Nikolskaiia, A.
Point Cloud Transformer for Elementary Particle Signals Segmentation : [Abstract] / A.Nikolskaiia, P.Goncharov, G.Ososkov, D.Rusov, D.Starikov // Физика элементарных частиц и атомного ядра. – 2024. – Т. 55, № 3. – P. 597. – URL: http://www1.jinr.ru/Pepan/v-55-3/42_Nikolskaia_ann.pdf.
Each experimental setup in high-energy physics experiments has its own specifics of tracking detectors and data acquisition system. For instance, SPD NICA track detectors will produce a huge number of fake measurements and other noisy signals, which can exceed the number of true ones by two orders of magnitude. In this paper, we present the Transformer-based architecture for the elimination of fake measurements from the simulated data for the SPD experiment. We describe an efficient method for utilizing self-attention modules with squared algorithmic and memory complexity to the simulated data by voxelization procedure.
Спец.(статьи,препринты) = Ц 840 в - Программы обработки экспериментальных данных и управление физическими установками$
ОИЯИ = ОИЯИ (JINR)2024