Relating Charged Particle Multiplicity to Impact Parameter in Heavy-Ion Collisions at NICA Energies

Abstract

The size and evolution of the matter created in relativistic heavy-ion collisions strongly depend on collision geometry, defined by the impact parameter. However, the impact parameter cannot be measured directly in an experiment but might be inferred from final state observables using the centrality procedure. We present the procedure of centrality determination for the Multi-Purpose Detector (MPD) at the NICA collider and its performance using the multiplicity of produced charged particles at midrapidity. The validity of the procedure is assessed using the simulated data for Au + Au collisions at sNN = 4–11 GeV.

Centrality Selection Effect on Elliptic Flow Measurements in Relativistic Heavy-Ion Collisions at NICA Energies

“…√ s NN = 7.7-62.4 GeV [20,22]. The definition of the centrality classes are based on the application of MC Glauber [12,13,28] and Γ-fit [14,15,28] methods.…”

“…The output of the MC Glauber model includes the geometrical properties of the simulated collisions: the impact parameter b, number of binary nucleon-nucleon collisions (N coll ), and the number of participating nucleons (N part ). The procedure for centrality determination includes fitting experimentally measured particle multiplicity N ch with an MC Glauber model based function [12,13,28] :…”

“…The main element is the fluctuation kernel which is used to model multiplicity fluctuations P(N ch |b) at a fixed impact parameter b. The fluctuations of the multiplicity can be described by the gamma distribution [14,15,28]:…”

“…we fit P(N ch ) to the experimental distribution of N ch using Equations ( 5) and ( 6) [14,15,28]. The fit for the reconstruction of the probability of N ch at fixed c b : P(N ch |c b ).…”

“…The ratio plots show that the Γ-fit method can reproduce the charged particle multiplicity distribution with good accuracy. Once the probability of N ch at a fixed c b is reconstructed, the probability distribution of b, at a fixed N ch , can be extracted using Bayes' theorem: P(b|N ch ) = P(N ch |b)P(b)/P(N ch ), where P(N ch |b) = P(N ch |c b ) and c b πb 2 /σ inel [14,15,28]. Extending this reconstruction to a finite centrality bin, corresponding to an interval N low ch < N ch < N high ch is straightforward upon integration over N ch :…”