Neural Networks for Nuclear Emulsion Processing
https://doi.org/10.56304/S2079562923010293
Abstract
Due to the development of precision optical and computational techniques, the number of experiments based on visual methods of processing track detector data increases. Application of neural networks for cluster extraction in images obtained with new generation scanning stations will not only speed up processing of images in nuclear emulsions, but also make the track reconstruction process an order of magnitude more efficient.
Supporting agencies: This work was supported by the Russian Science Foundation, project no. 23-12-00054, under the program “Conducting Fundamental Scientific Research and Exploratory Scientific Research by Individual Scientific Groups.”
About the Authors
V. T. Vasilev
Физический институт им. П.Н. Лебедева РАН, Москва, 119991 Россия
Russian Federation
Russian Federation
N. S. Konovalova
Физический институт им. П.Н. Лебедева РАН, Москва, 119991 Россия
Russian Federation
Russian Federation
N. M. Okateva
Физический институт им. П.Н. Лебедева РАН, Москва, 119991 Россия
Russian Federation
Russian Federation
N. G. Polukhina
Физический институт им. П.Н. Лебедева РАН, Москва, 119991 Россия
Russian Federation
Russian Federation
Zh. T. Sadykov
Физический институт им. П.Н. Лебедева РАН, Москва, 119991 Россия
Russian Federation
Russian Federation
E. N. Starkova
Физический институт им. П.Н. Лебедева РАН, Москва, 119991 Россия
Russian Federation
Russian Federation
N. I. Starkov
Физический институт им. П.Н. Лебедева РАН, Москва, 119991 Россия
Russian Federation
Russian Federation
M. M. Chernyavskiy
Физический институт им. П.Н. Лебедева РАН, Москва, 119991 Россия
Russian Federation
Russian Federation
T. V. Shchedrina
Физический институт им. П.Н. Лебедева РАН, Москва, 119991 Россия
Russian Federation
Russian Federation
References
1. <em>Alexandrov A. et al.</em> // J. Instrum. 2016. V. 11 (6). P. P06002. https://doi.org/10.1088/1748-0221/11/06/P06002
2. <em>Alexandrov A. et al.</em> // Sci. Rep. 2019. V. 9 (1). P. 2870. https://doi.org/10.1038/s41598-019-39415-8
3. <em>Ronneberger O. et al.</em> // Proc. Intl. Conf. on Medical Image Computing and Computer-Assisted Intervention. 2015. P. 234. https://doi.org/10.48550/arXiv.1505.04597
4. <em>Aleksandrov A.B. et al.</em> // Bull. Lebedev Phys. Inst. 2012. V. 39 (9). P. 269. https://doi.org/10.3103/S1068335612090059
5. <em>Alexandrov A. et al.</em> // Measurement. 2022. V. 187. P. 110244. https://doi.org/10.1016/j.measurement.2021.110244
Review
For citations:
Vasilev V.T., Konovalova N.S., Okateva N.M., Polukhina N.G., Sadykov Zh.T., Starkova E.N., Starkov N.I., Chernyavskiy M.M., Shchedrina T.V. Neural Networks for Nuclear Emulsion Processing. Nuclear Physics and Engineering. 2024;15(1):31-35. (In Russ.) https://doi.org/10.56304/S2079562923010293
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