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Using Spectator Matter for Centrality Determination in Nucleus-Nucleus Collisions

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One of the common methods to measure the centrality of nucleus-nucleus collision events consists of detecting forward spectator neutrons. Because of non-monotonic dependence of neutron numbers on centrality, other characteristics of spectator matter in 197Au–197Au collisions at NICA must be considered to improve the centrality determination. The numbers of spectator deuterons and α-particles and the forward–backward asymmetry of the numbers of free spectator nucleons were calculated with the Abrasion–Ablation Monte Carlo for Colliders (AAMCC) model as functions of event centrality. It was shown that the number of charged fragments per spectator nucleon decreases monotonically with an increase of the impact parameter, and thus can be used to estimate the collision centrality. The conditional probabilities that a given event with specific spectator characteristics belongs to a certain centrality class were calculated by means of AAMCC. Such probabilities can be used as an input to Bayesian or other machine-learning approaches to centrality determination in 197Au–197Au collisions.
Title: Using Spectator Matter for Centrality Determination in Nucleus-Nucleus Collisions
Description:
One of the common methods to measure the centrality of nucleus-nucleus collision events consists of detecting forward spectator neutrons.
Because of non-monotonic dependence of neutron numbers on centrality, other characteristics of spectator matter in 197Au–197Au collisions at NICA must be considered to improve the centrality determination.
The numbers of spectator deuterons and α-particles and the forward–backward asymmetry of the numbers of free spectator nucleons were calculated with the Abrasion–Ablation Monte Carlo for Colliders (AAMCC) model as functions of event centrality.
It was shown that the number of charged fragments per spectator nucleon decreases monotonically with an increase of the impact parameter, and thus can be used to estimate the collision centrality.
The conditional probabilities that a given event with specific spectator characteristics belongs to a certain centrality class were calculated by means of AAMCC.
Such probabilities can be used as an input to Bayesian or other machine-learning approaches to centrality determination in 197Au–197Au collisions.

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