Growth of Long Range Forward-Backward Multiplicity Correlations with Centrality in $\mathrm{Au}+\mathrm{Au}$ Collisions at $\sqrt{s_{NN}}=200\mathrm{GeV}$

Forward-backward multiplicity correlation strengths have been measured with the STAR detector for $\mathrm{Au}+\mathrm{Au}$ and $p+p$ collisions at $\sqrt{s_{NN}}=200\mathrm{GeV}$. Strong short- and long-range correlations (LRC) are seen in central $\mathrm{Au}+\mathrm{Au}$ collisions. The magnitude of these correlations decrease with decreasing centrality until only short-range correlations are observed in peripheral $\mathrm{Au}+\mathrm{Au}$ collisions. Both the dual parton model (DPM) and the color glass condensate (CGC) predict the existence of the long-range correlations. In the DPM, the fluctuation in the number of elementary (parton) inelastic collisions produces the LRC. In the CGC, longitudinal color flux tubes generate the LRC. The data are in qualitative agreement with the predictions of the DPM and indicate the presence of multiple parton interactions.

Figure 1

(a) FB correlation strength for 0–10% (circle) and ZDC based centrality (square) (b) FB correlation strength for 10–20, 20–30, 30–40, 40–50 and 50–80% (square) $\mathrm{Au}+\mathrm{Au}$ and (c) for $p+p$ collisions as a function of $\Delta \eta$ at $\sqrt{s_{NN}}=200\mathrm{GeV}$. The error bars represent the systematic point-to-point error. The boxes show the correlated systematic errors.

Figure 2

(a) Backward-forward dispersion, $D_{bf}^2$, and forward-forward dispersion, $D_{ff}^2$, for 0–10% centrality as a function of $\Delta \eta$ for $\mathrm{Au}+\mathrm{Au}$ collisions at $\sqrt{s_{NN}}=200\mathrm{GeV}$. (b) $D_{bf}^2$, and $D_{ff}^2$ for $p+p$ collisions at $\sqrt{s}=200\mathrm{GeV}$. The error bars represent the systematic point-to-point error. The boxes show the correlated systematic errors.

Figure 3

The FB correlation strength for 0–10% most central $\mathrm{Au}+\mathrm{Au}$ collisions and $p+p$ from data (circle), HIJING (triangle) and PSM (square). The error bars shown are for data.