Documents

We apply artificial neural networks (ANN) to event-wise analysis of simulated data from a
microchannel plate detector (MCP)[1] being considered for installation in future experiments on NICA
collider [2]. We have demonstrated, that neural networks can estimate the parameters of the collision not
only from spatial distribution of particles, but also benefit from high resolution time-of-flight distributions
that can be obtained from MCP. From this data we estimate the impact parameter and the collision point
of an event. We have performed the analysis based on several Monte-Carlo models of the event. Even
though the quality of the existing event models is not sufficient for a reliable model-independent estimation
of the event parameters, the proposed parameter reconstruction procedure allows us to evaluate - and to
optimize - the technical characteristics of the detector. These characteristics include the geometry of the
device, its placement, the number of sensors, and the time resolution.
In [3, 4, 5] we have demonstrated that – subject to the detector geometry – the collision point and the
impact parameter of each event can be estimated quite accurately only from the raw detector data. Our
approach exploits Monte-Carlo models of high energy collisions. As we have demonstrated in [3, 4, 5], the
data from QGSM generator [6] allows us to estimate the impact parameter within an uncertainty of about

1 fm, and to reconstruct the collision point with uncertainty about 1 cm. This result, however, is model-
dependent, and processing data from alternative generators [7, 8] leads to different ANN parameters.

Despite this model dependence of the ANNs, the detector parameters providing the best reconstruction
of the event parameters do not depend on the Monte-Carlo model of the event.

We report the results of ANN training and suggest the optimal MCP configuration which is model-
independent and, thus, can be used in future detector specification.

1. A.A.Baldin, G.A. Feofilov, P. Har’yuzov, and F.F. Valiev // Nucl. Instrum. Meth.A 2020,
V.958,P.162154. https://doi.org/10.1016/j.nima.2019.04.108
2. https://nica.jinr.ru/
3. K.A. Galaktionov, V.A. Roudnev, and F.F. Valiev, Neural network approach to impact parameter
estimation in high-energy collisions using the microchannel plate detector data // Moscow University
Physics Bulletin 2023, V. 78, P. S52-S58
4. Galaktionov K.A., Roudnev V.A., Valiev F.F. Artificial Neural Networks Application in Estimating
the Impact Parameter in Heavy Ion Collisions Using the Microchannel Plate Detector Data: Physics of
Atomic Nuclei //Phys. At. Nucl. 2023 V.86(6), P.1426-1432. https://doi.org/10.1134/S1063778823060248
5. Galaktionov, K., Roudnev, V., Valiev, F., Application of Neural Networks for Event-by-Event
Evaluation of the Impact Parameter // Physics of Particles and Nuclei 2023 ,V. 54, P. 446-448
6. Amelin N. S., Gudima K. K., Toneev V. D. Ultrarelativistic nucleus-nucleus collisions within a
dynamical model of independent quark - gluon strings // Sov. J. Nucl. Phys. 1990. V. 51(6), P. 1730-
1743
7. Werner, Klaus and Liu, Fu-Ming and Pierog, Tanguy Parton ladder splitting and the rapidity
dependence of transverse momentum spectra in deuteron-gold collisions at the BNL Relativistic Heavy
Ion Collider // Physical Review C 2006, V. 74
8. Aichelin, J. and Bratkovskaya, E. and Le F`evre, A. and Kireyeu, V. and Kolesnikov, V. and Leifels,
Y. and Voronyuk, V. and Coci, G. Parton-hadron-quantum-molecular dynamics: A novel microscopic
n-body transport approach for heavy-ion collisions, dynamical cluster formation, and hypernuclei
production // Physical Review C 2020, V. 101
Original languageEnglish
StatePublished - 1 Jul 2024
EventNucleus-2024: LXXIV International conference : Fundamental problems and applications - Объединенный институт ядерных исследований, Дубна, Russian Federation
Duration: 1 Jul 20245 Jul 2024
Conference number: LXXIV
https://indico.jinr.ru/event/4304/

Conference

ConferenceNucleus-2024: LXXIV International conference
Abbreviated titleNucleus-2024
Country/TerritoryRussian Federation
CityДубна
Period1/07/245/07/24
Internet address

    Scopus subject areas

  • Nuclear and High Energy Physics

ID: 124242413