Probing Neutron Skin through Event-by-Event Pion Asymmetry in Heavy-ion collisions

TL;DR

This paper introduces a new method to measure neutron skin thickness in gold nuclei by analyzing event-by-event pion yield asymmetries in heavy-ion collisions, using simulations to establish linear relationships with the neutron skin.

Contribution

It proposes a novel approach linking pion asymmetry distributions to neutron skin thickness and compares different collision models to improve measurement accuracy.

Findings

Mean pion yield difference scales linearly with neutron skin.

Certain pion yield pairs are most sensitive to neutron skin variations.

Model-dependent slopes can help identify the most accurate collision model.

Abstract

In this work, we propose a novel approach for probing the neutron skin thickness of gold (Au) by analyzing the event-by-event distribution of $\pi^{-}$ and $\pi^{+}$ yield differences. This is achieved through SMASH simulations of ultra-peripheral Au+Au collisions at $\sqrt{s_{\rm NN}}=3$ GeV. Our results demonstrate that the mean value of $\Delta n_{\pi} = n_{\pi^{-}} - n_{\pi^{+}}$, along with the Pearson correlation and mutual information between $(\pi^{-}+\pi^{+})$ and $(\pi^{-}-\pi^{+})$, all scale linearly with the neutron skin thickness. Moreover, the slope of the line connecting two distinct $\Delta n_{\pi}$ values in the event-by-event distribution also exhibits a linear dependence on the neutron skin thickness. The most sensitive $\Delta n_{\pi}$ pairs are identified as $(-1, 1)$, $(-1, 2)$, $(0, 1)$, and $(0, 2)$. These findings establish a new pathway for determining the neutron skin thickness. Finally, by comparing SMASH and UrQMD simulations under identical initial conditions, we observe that individual slope values depend on the specific collision model. However, by extracting slopes from multiple $\Delta n_{\pi}$ pairs in experimental event-by-event data and inferring the corresponding neutron skin thickness, one can assess which model better aligns with the true physical value.