Estimation of initial-state structures in high-energy heavy-ion collisions using principal component analysis

In high-energy heavy-ion collisions, structures in the initial collision zone are a matter of intense investigation, both from theory and experimental points of views. A large number of models have been developed to represent the initial state of the collision including the Glauber model and color glass condensate (CGC), among others. Another important aspect of the study is to investigate proper observables that will be sensitive to the initial collision zone. In this work, we discuss a formalism to implement the spatial clusters at the partonic level in the string melting version of the ampt model for PbPb collisions at $\sqrt{s_{NN}}=200\mathrm{GeV}$. These clusters are then propagated through the ampt hadronization scheme. Principal component analysis (PCA) has been used on the $\eta$, $\varphi$, and $p_T$ distributions of the produced charged particles and the eigenvalues have been compared before and after the implementation of the clustering. It is found that for all these three different distributions, all the prominent PCA modes show sensitivity to the clustering. A centrality dependent study has also been performed on those eigenvalues.

Figure 1

$X\text{−}Y$ distribution of the partons before (top) and after (bottom) clustering.

Figure 2

$\eta$, $\varphi$, and $p_T$ distributions of the partons before and after clustering for minimum-bias PbPb collisions at $\sqrt{s_{NN}}=200\mathrm{GeV}$.

Figure 3

$\eta$, $\varphi$, and $p_T$ distributions of the produced particles before and after clustering for minimum bias PbPb collisions at $\sqrt{s_{NN}}=200\mathrm{GeV}$.

Figure 4

$p_T$ distributions of $v_2$ for two scenarios, i.e., before and after clustering (upper panel) and the ratio of $v_2$ vs $p_T$ distributions after clustering with respect to the one before.

Figure 5

Eigenvalues as obtained for $\eta$ distribution before and after clustering.

Figure 6

Eigenvalues as obtained for $p_T$ distribution before and after clustering.

Figure 7

Eigenvalues as obtained for $\varphi$ distribution before and after clustering.

Figure 8

Eigenvalues as obtained for $\eta$ distribution before and after clustering.

Figure 9

Eigenvalues as obtained for $\varphi$ distribution before and after clustering.

Figure 10

Eigenvalues as obtained for $p_T$ distribution before and after clustering.