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SUMMARY:Artificial neural networks application in estimating the impact pa
 rameter in heavy ion collision using the microchannel plate detector data
DTSTART;VALUE=DATE-TIME:20221202T151500Z
DTEND;VALUE=DATE-TIME:20221202T153000Z
DTSTAMP;VALUE=DATE-TIME:20260719T190930Z
UID:indico-contribution-3140@cern.ch
DESCRIPTION:Speakers: Kirill Galaktionov ()\nEvaluation of the impact para
 meter in a single event of relativistic heavy ion collision is crucial for
  correct and efficient data processing and analysis. In this work\, we stu
 died the possibility of estimating the impact parameter in heavy ion colli
 sions by using artificial neural networks applied to the charged particle 
 data from the fast microchannel plate (MCP) detectors.\n\nTo carry out com
 putational event-by-event experiments\, we used simulated data from 200\,0
 00 A+A collisions of gold nuclei $(A = 197\, Z = 79)$\, at $\\sqrt{s_{NN}}
  = 11 \\mbox{ GeV}$\, obtained by the QGSM MC event generator. Charged par
 ticles multiplicity\, their spatial distribution and time-of-flight data w
 ere used as event features to be analyzed by the artificial neural network
  algorithms. \n\nWe investigated two different configurations of microchan
 nel plate detectors layout:\n\n - consisting of three pairs of small MCP s
 egmented rings with 5 cm outer and 3 cm of the inner diameter positioned i
 nside the vacuum beam-pipe symmetrically at distances of 1 m\, 1.7 m and 2
 .5 m from the center of installation\n - consisting of one pair of large a
 rea segmented MCP rings with the outer diameter of 50 cm and 6 cm of the i
 nner one positioned outside the vacuum beam-pipe symmetrically at a distan
 ce of 4 m from the center of installation \n\nThese two configurations of 
 MCP detectors layout have different data sets requirements and computation
 al requirements. In both configurations the readout anodes of the MCP ring
 s have certain segmentation in azimuth and radius. (The fast microchannel 
 plate detector of charged particles was previously proposed for experiment
 s at NICA in [1]).  \n \nWe show that the developed artificial neural netw
 orks technique is capable\, for both configurations of MCP detectors layou
 t\, to provide sufficiently good and fast results on the impact parameter 
 determination in a single heavy ion collision event. In our first exercise
 s\, the proposed algorithm was capable to successfully classify more than 
 90% of $\\mbox{Au}+\\mbox{Au}$ collision events with the impact parameter 
 less than 5 fm\, and it can be valuable as the fast trigger. We discuss al
 so further developments and possible applications of this technique in the
  future experimental setups.\n\n\n\n[1] A. A. Baldin\, G. A. Feofilov\, P.
  Har'yuzov\, F.F.Valiev\, Fast beam–beam collisions monitor for experime
 nts at NICA\, NIMA\, 958\, 162154\, 2019\, Reported at the VCI2019\, DOI:1
 0.1016/j.nima.2019.04.10\n\nhttps://indico.particle.mephi.ru/event/275/con
 tributions/3140/
LOCATION:Hotel Intourist Kolomenskoye 4* Moskvorechye 2
URL:https://indico.particle.mephi.ru/event/275/contributions/3140/
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