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Application of Neural Networks for Event-by-Event Evaluation of the Impact Parameter. / Galaktionov, K.; Rudnev, V.; Valiev, F.

In: Physics of Particles and Nuclei, Vol. 54, No. 3, 01.06.2023, p. 446-448.

Research output: Contribution to journal › Article › peer-review

Harvard

Galaktionov, K, Rudnev, V & Valiev, F 2023, 'Application of Neural Networks for Event-by-Event Evaluation of the Impact Parameter', Physics of Particles and Nuclei, vol. 54, no. 3, pp. 446-448. https://doi.org/10.1134/s1063779623030152

APA

Galaktionov, K., Rudnev, V., & Valiev, F. (2023). Application of Neural Networks for Event-by-Event Evaluation of the Impact Parameter. Physics of Particles and Nuclei, 54(3), 446-448. https://doi.org/10.1134/s1063779623030152

Vancouver

Galaktionov K, Rudnev V, Valiev F. Application of Neural Networks for Event-by-Event Evaluation of the Impact Parameter. Physics of Particles and Nuclei. 2023 Jun 1;54(3):446-448. https://doi.org/10.1134/s1063779623030152

Author

Galaktionov, K. ; Rudnev, V. ; Valiev, F. / Application of Neural Networks for Event-by-Event Evaluation of the Impact Parameter. In: Physics of Particles and Nuclei. 2023 ; Vol. 54, No. 3. pp. 446-448.

BibTeX

@article{934143b0c6c64b0a9c3a2ab048018cc7,
title = "Application of Neural Networks for Event-by-Event Evaluation of the Impact Parameter",
abstract = "Abstract: Evaluation of the impact parameter in single events is crucial for the correct and efficient data processing in collision-based nuclear and particle physics experiments. Estimating the impact parameter in real time allows experimentalists to preselect the most informative events at the data acquisition stage before any processing. The presented computational experiments show the applicability of neural networks combined with the microchannel plate detectors to directly estimate the impact parameter at the experimental facilities under construction.",
author = "K. Galaktionov and V. Rudnev and F. Valiev",
year = "2023",
month = jun,
day = "1",
doi = "10.1134/s1063779623030152",
language = "English",
volume = "54",
pages = "446--448",
journal = "Physics of Particles and Nuclei",
issn = "1063-7796",
publisher = "МАИК {"}Наука/Интерпериодика{"}",
number = "3",

}

RIS

TY - JOUR

T1 - Application of Neural Networks for Event-by-Event Evaluation of the Impact Parameter

AU - Galaktionov, K.

AU - Rudnev, V.

AU - Valiev, F.

PY - 2023/6/1

Y1 - 2023/6/1

N2 - Abstract: Evaluation of the impact parameter in single events is crucial for the correct and efficient data processing in collision-based nuclear and particle physics experiments. Estimating the impact parameter in real time allows experimentalists to preselect the most informative events at the data acquisition stage before any processing. The presented computational experiments show the applicability of neural networks combined with the microchannel plate detectors to directly estimate the impact parameter at the experimental facilities under construction.

AB - Abstract: Evaluation of the impact parameter in single events is crucial for the correct and efficient data processing in collision-based nuclear and particle physics experiments. Estimating the impact parameter in real time allows experimentalists to preselect the most informative events at the data acquisition stage before any processing. The presented computational experiments show the applicability of neural networks combined with the microchannel plate detectors to directly estimate the impact parameter at the experimental facilities under construction.

UR - https://www.mendeley.com/catalogue/7855e74e-861b-3688-87fc-ff5b52da7ba2/

U2 - 10.1134/s1063779623030152

DO - 10.1134/s1063779623030152

M3 - Article

VL - 54

SP - 446

EP - 448

JO - Physics of Particles and Nuclei

JF - Physics of Particles and Nuclei

SN - 1063-7796

IS - 3

ER -

ID: 106600704