Speaker
Description
In our work we investigated application of artificial neural networks to event-wise analysis of heavy ion collisions data. We focused on solving the problem of impact parameter evaluation and estimation of collision vertex coordinate, using simulated data from a microchannel plate detector (MCP) [1] for potential use in NICA collider experiments [2]. Our study reveals, that such a technique can be utilized to estimate collision parameters quite accurately from raw detector data [3, 4, 5] based on QGSM
event generator [6], specifically from spatial distributions of particles and time-of-flight distributions.
However, ANNs results are highly dependent on the model of event generator used to create the dataset. Repeating the experiments with data from alternative generators [7, 8] yielded different results. Despite this model dependence of the ANNs, we discuss the way they can be utilized to build model-independent algorithms. Moreover, we have shown that the detector parameters providing the best reconstruction of the event parameters do not depend on the Monte-Carlo model of the event, and, therefore, are more likely to be optimal in future experiments.
The authors acknowledge Saint-Petersburg State University for a research project 95413904.
References:
[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 Fèvre, 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
