TY - JOUR

T1 - Neural network approach to impact parameter estimation in high-energy collisions using the microchannel plate detector data

AU - Галактионов, Кирилл Александрович

AU - Руднев, Владимир Александрович

AU - Валиев, Фархат Фагимович

PY - 2024/1/17

Y1 - 2024/1/17

N2 - Abstract: Estimating the impact parameter in a single high-energy ion collision event is an important problem in data analysis in particle physics, because knowledge of the impact parameter is crucial for extracting information about the properties of nuclear matter. In this study, we present the use of a neural network approach for estimating the impact parameter and determining the collision class (head-on or peripheral collisions). We have modeled the data sourced from microchannel plate detectors in two geometries based on the (Formula Presented.) collision dataset at energies (Formula Presented.) GeV obtained by the QGSM MC event generator. We utilized the spatial distribution of particles and their time-of-flight data as event features. The addition of time-of-flight information improves the quality of impact parameter estimation. By comparing two detector geometries with different pseudorapidity acceptances (Formula Presented.), we demonstrated that a wider interval significantly enhances the results. The proposed algorithm was able to successfully classify more than 98 \% of Au+Au head-on collision events with an impact parameter of less than 5 fm and can be further useful as a fast trigger system. We also discuss further developments and improvements for possible applications of this technique in future experimental setups.

AB - Abstract: Estimating the impact parameter in a single high-energy ion collision event is an important problem in data analysis in particle physics, because knowledge of the impact parameter is crucial for extracting information about the properties of nuclear matter. In this study, we present the use of a neural network approach for estimating the impact parameter and determining the collision class (head-on or peripheral collisions). We have modeled the data sourced from microchannel plate detectors in two geometries based on the (Formula Presented.) collision dataset at energies (Formula Presented.) GeV obtained by the QGSM MC event generator. We utilized the spatial distribution of particles and their time-of-flight data as event features. The addition of time-of-flight information improves the quality of impact parameter estimation. By comparing two detector geometries with different pseudorapidity acceptances (Formula Presented.), we demonstrated that a wider interval significantly enhances the results. The proposed algorithm was able to successfully classify more than 98 \% of Au+Au head-on collision events with an impact parameter of less than 5 fm and can be further useful as a fast trigger system. We also discuss further developments and improvements for possible applications of this technique in future experimental setups.

KW - neural networks

KW - machine learning

KW - detector system

KW - trigger

KW - microchannel plates

KW - heavy ion collision

UR - https://dlcp2023.sinp.msu.ru/doku.php/dlcp2023/proceedings

UR - https://www.mendeley.com/catalogue/7bc6f134-854c-3119-a37c-02255877cd34/

U2 - 10.3103/s0027134923070081

DO - 10.3103/s0027134923070081

M3 - Article

VL - 78

SP - S52-S58

JO - Moscow University Physics Bulletin (English Translation of Vestnik Moskovskogo Universiteta, Fizika)

JF - Moscow University Physics Bulletin (English Translation of Vestnik Moskovskogo Universiteta, Fizika)

SN - 0027-1349

IS - 1

ER -