logo

Investigating the elliptic anisotropy of identified particles in p–Pb collisions with a multi-phase transport model

NUCLEAR PHYSICS AND INTERDISCIPLINARY RESEARCH

Investigating the elliptic anisotropy of identified particles in p–Pb collisions with a multi-phase transport model

Si-Yu Tang
Liang Zheng
Xiao-Ming Zhang
Ren-Zhuo Wan
Nuclear Science and TechniquesVol.35, No.2Article number 32Published in print Feb 2024Available online 28 Mar 2024
52405

The elliptic azimuthal anisotropy coefficient (v2) of the identified particles at midrapidity (|η|<0.8) was investigated in p–Pb collisions at sNN = 5.02 TeV using a multi-phase transport model (AMPT). The calculations of differential v2 based on the advanced flow extraction method of light flavor hadrons (pions, kaons, protons, and Λ) in small collision systems were extended to a wider transverse momentum (pT) range of up to 8 GeV/c for the first time. The string- melting version of the AMPT model provides a good description of the measured pT-differential v2 of the mesons but exhibits a slight deviation from the baryon v2. In addition, we observed the features of mass ordering at low pT and the approximate number-of-constituent-quark (NCQ) scaling at intermediate pT. Moreover, we demonstrate that hadronic rescattering does not have a significant impact on v2 in p–Pb collisions for different centrality selections, whereas partonic scattering dominates in generating the elliptic anisotropy of the final particles. This study provides further insight into the origin of collective-like behavior in small collision systems and has referential value for future measurements of azimuthal anisotropy.

Azimuthal anisotropySmall collision systemsTransport model
1

Introduction

The main goal of heavy-ion collisions at ultrarelativistic energies is to explore the deconfined state of strongly interacting matter created at a high energy density and temperature, known as the gluon plasma (QGP) [1, 2]. An important observation for investigating the transport properties of the QGP is anisotropic flow [3, 4], which is quantified by the flow harmonic coefficients vn obtained from the Fourier expansion of the azimuthal distribution of the produced particles [5, 6]: dNdφ1+2n=1vncos[n(φΨn)], (1) where φ is the azimuthal angle of the final-state particle angle and Ψn is the symmetry plane angle in the collision for the n-th harmonic [7, 8]. The second-order coefficient v2, referred to as the elliptic flow, is derived from the initial state spatial anisotropy of the almond-shaped collision overlap region that is propagated to the final state momentum space. The magnitude of the elliptic flow is sensitive to the fundamental transport properties of the fireball, such as the temperature-dependent equation of state and the ratio of shear viscosity to entropy density (η/s) [9, 10].

Over the past few decades, various measurements of elliptic flow in heavy-ion collisions performed at the relativistic heavy-ion collider (RHIC) [11-14] and the Large Hadron Collider (LHC) [15-18] have helped build a full paradigm of the strongly coupled QGP. Comprehensive measurements of pT-differential elliptic flow of the identified particles were conducted by the ALICE Collaboration [19, 20]. The observed mass-ordering effect (i.e., heavier particles have a smaller elliptic flow than lighter particles at the same pT) at low pT is well described by hydrodynamic calculations and is attributed to the radial expansion of the QGP [21]. At intermediate pT, the grouping of v2 of mesons and baryons was observed, with mesons exhibiting less v2 than baryons. These behaviors can be explained by the hypothesis that baryons and mesons have different production mechanisms through quark coalescence, which has been further investigated using the number-of-constituent-quark (NCQ) scaling [22-25]. Interestingly, such flow-like phenomena have been observed in small-collision systems. Long-range double-ridge structures were first measured in high-multiplicity pp and p–Pb collisions by the ALICE, ATLAS, and CMS collaborations [26-28]. The measurement of elliptic and triangular azimuthal anisotropies in central 3He+Au, d+Au, and p+Au collisions performed by the STAR Collaboration [29] suggests that sub-nucleon fluctuations also play an important role in influencing the flow coefficients in these small collision systems. In addition, these measurements were extended to the identified particles associated with the discovery of a significant positive v2 [30, 31]. The observed particle-mass dependence of v2 is similar to that measured in heavy-ion collisions [30]; however, the origin of such collective-like behavior remains unclear. Several theoretical explanations relying on either the initial state or final state effects have been proposed to understand the origin of azimuthal anisotropies in small systems. Studies that extend hydrodynamics from large to small systems based on final-state effects can well describe v2 of soft hadrons [32-36]; however, they are based on the strong assumption that there is sufficient scattering among constituents in small systems. Hydrodynamics combined with the linearized Boltzmann transport (LBT) model can also describe the identified particle v2 in a high-multiplicity small-collision system at an intermediate pT [37]. Color-Glass Condensate (CGC)-based models and IP-Glasma models that consider the effect of momentum correlations in the initial state can quantitatively describe some features of collectivity in p–Pb collisions [38, 39], but without clear conclusions, particularly regarding the dependence on collision systems and rapidity.

In addition, an approach called parton escape shows that few scatterings can also create sufficient azimuthal anisotropies, which have been investigated using multiphase transport (AMPT) [40, 41]. The v2 values of light hadrons measured in p–Pb collisions are well described in AMPT, where the contribution of anisotropic parton escape rather than hydrodynamics plays an important role [41]. In this study, we extend the AMPT calculations of the pT-differential v2 for identified particles (π±, K±, p(p¯), Λ(Λ¯)) to higher pT region in p–Pb collisions at 5.02 TeV, in order to systematically test the mass-ordering effect and baryon-meson grouping at low- and intermediate-pT, respectively. We also investigate how the key mechanisms implemented in AMPT, such as the parton cascade and hadronic rescattering, affect elliptic anisotropy in small collision systems. In addition, various nonflow subtraction methods with different sensitivities to jet-like correlations were studied.

2

Model and Methodology

2.1
A multiphase transport model

The string-melting version of the AMPT model (v2.26t9b, available online) [40, 42] was employed in this study to calculate v2 of the final-state particles in high-multiplicity p–Pb at 5.02 TeV. The AMPT model includes four main processes: initial conditions, partial scattering, hadronisation, and hadronic interactions. The initial conditions are generated from the heavy ion jet interaction generator (HIJING) model [43, 44], where minijet partons and soft-excited strings are produced and then converted to primordial hadrons based on Lund fragmentation. Under the string-melting mechanism, primordial hadrons are converted into partons, a process determined by their flavor and spin structures. Elastic scattering between the partons was simulated using Zhang’s parton cascade (ZPC) model [45], which includes two-body scattering with a cross-section described by the following simplified equation: σgg9παs22μ2. (2) In this study, the strong coupling constant αs was set to 0.33, and the Debye screening mass μ = 2.2814 fm-1, resulting in a total parton scattering cross section of σ= 3 mb. To isolate the effect of partonic scattering, σ is adjusted to be close to 0 by increasing μ (see set "w/o parton scat." in Tab. 1). Once the partonic interaction ceases, hadronization with a quark coalescence model is implemented to combine the nearest two (or three) quarks into mesons (or baryons) [40]. The formed hadrons enter the subsequent hadronic rescattering process using a relativistic transport (ART) model [46], in which both elastic and inelastic scattering are considered for baryon-baryon, baryon-meson, and meson-meson interactions. The hadronic interaction time was set by default to tmax = 30 fm/c. Alternatively, tmax is set to 0.4 fm/c to effectively turn off the hadron scattering process while still considering the resonance decay [47](see set "w/o hadron scat." in Tab. 1). In addition, the random orientation of the reaction plane was turned on and the shadowing effect was considered in this analysis.

Table 1
Details of three configurations
Description σ (mb) tmax (fm/c)
w/ all 3 30
w/o parton scat. ~0 30
w/o hadron scat. 3 0.4
Show more
2.2
Two-particle correlation and nonflow subtraction

The two-particle correlation (2PC) method is widely used to extract the flow signal in small collision systems because it can suppress the non-flow contribution from long-range jet correlations [26-28, 30, 48]. Similar to Eq. 1, the azimuthal correlation between two emission particles can be represented by Npairs pairs of emitted particles (labeled as Cφ)) as a function of the relative angle Δφ=φa-φb between particles a and b and expanded in the Fourier series as follows: C(Δφ)=dNpairdΔφ1+2n=1VnΔ(pTa,pTb)cos[n(Δφ)], (3) where VnΔ refers to the two-particle n-th order harmonic. In a pure hydrodynamic scenario, because particle emissions are independent, VnΔ(pTa,pTb) can be factorized into the product of a single-particle flow vna and vnb: VnΔ(pTa,pTb)=vn(pTa)vn(pTb). (4) Based on the factorization assumption, vn of a single particle a can be obtained using the 3×2PC method, which was recently proposed by the PHENIX Collaboration [49]. This requires the formation of two-particle correlations between three groups of particles (labeled a, b and c) and the extraction of the flow coefficients for three combinations: vn(pTa)=VnΔ(pTa,pTb)VnΔ(pTa,pTc)VnΔ(pTb,pTc). (5) In small-collision systems, two main types of nonflow contributions to the flow signal are the near-side jet and away-side jet (recoil jet) correlations. The former can be effectively removed by introducing a large rapidity gap between the trigger and associated particles during the construction of the correlations. Several methods have been developed to subtract the latter [50]. A traditional approach is to directly subtract the correlation function distribution obtained from low-multiplicity events [27, 30] from that obtained from high-multiplicity events. This method assumes that the yield and shape of dijets are identical for both collision types as follows: CHM(Δφ)CLM(Δφ)1+2n=1VnΔcos[n(Δφ)]=a0+2n=1ancos[n(Δφ)], (6) where CLMφ) and CHMφ) represent the correlation function distributions obtained for low- and high-multiplicity events, respectively. This method relies on the "zero yield at minimum" (ZYAM) hypothesis [27, 30] that a flat combinatoric component should be subtracted from the correlation function in low-multiplicity events. Therefore, the fit parameter a2 is the absolute modulation in the subtracted correlation function distribution and characterizes the modulation relative to a baseline, assuming that such a modulation is not present in the low-multiplicity class below the baseline. In this case, the flow coefficient VnΔ is calculated as VnΔ=an/(a0+b), (7) where b is the baseline, estimated using the minimum correlation function for low-multiplicity events. However, the measurement of jet-like correlations in p–Pb collisions indicates that the dependence of the dijet yield on the particle multiplicity cannot be ignored. In this case, a new template fit method was developed by the ATLAS collaboration [51], where the correlation function distribution obtained in high-multiplicity events is assumed to result from the superposition of the distribution obtained in low-multiplicity events scaled up by a multiplicative factor F and a constant modulated by cos(nΔφ) for n > 1, as shown in C(Δφ)=FCLM(Δφ)+G(1+2n=13VnΔcos(nΔφ)), (8) where G denotes the normalization factor that maintains the integral of Cφ) equal to CHMφ). Furthermore, an improved template fitting method [52] developed in recent years was tested. It applies a correction procedure to the default template fit method by considering the multiplicity dependence of the remaining ridge in low-multiplicity events, as shown in VnΔ=VnΔ(tmp)FGLMGHM(VnΔ2(tmp)VnΔ2(LM)), (9) where VnΔ(tmp) and VnΔ2(LM) are obtained by using the default template method for high- and low-multiplicity events. All these nonflow subtraction methods are implemented in this study, and their different sensitivities to nonflow effect are also discussed.

3

Analysis procedures

To directly compare the AMPT calculations with the results from ALICE, we focused on the particles within the pseudorapidity range |η| < 0.8, aligning with the TPC acceptance in ALICE [53]. In the 3×2PC method, long-range correlations were constructed between the charged particles at mid-rapidity, forward rapidity (2.5 < η < 4), and backward rapidity (-4 < η < -2.5), that is, the central-forward correlation (-4.8 < Δη < -1.7), central-backward correlation (1.7 < Δη < 4.8), and backward-forward correlations (-8 < Δη < -5). In addition, the centrality classes are defined by counting the charged particles in the acceptance of the V0A detector [53], that is, 2.8<η<5.1.

The correlation function distribution Cφ) was obtained by correcting the number of particle pairs in the same events normalized to the number of trigger particles Ntrig by using an event-mixing technique: C(Δφ,Δη)=1Ntrigd2NpairsdΔηdΔφ=S(Δφ,Δη)B(Δφ,Δη), (10) where S(Δφ,Δη)=1Ntrigd2NsamedΔηdΔφ is the correlation function in same events and B(Δφ,Δη)=αd2NmixeddΔηdΔφ is the associated yield as a function of Δφ and Δφ in mixed events. Factor α is used to normalize Bφ, Δη) to unity in the Δη region of the maximal pair acceptance. The obtained 2-D correlation function Cφ, Δη) is projected onto Δφ axis, and we follow the nonflow subtraction procedures and factorization, as discussed in Eq. 5–Eq. 9, v2 of the charged particles at |η| < 0.8 can be calculated.

4

Results and Discussions

We first investigated the pT spectrum of the identified particles before performing the flow analysis. Figure 1 illustrates the pT distribution of proton, pion, and kaon in 0–20% high-multiplicity p–Pb collisions at sNN = 5.02 TeV, which are obtained from AMPT with three different sets of configurations listed in Table 1 and ALICE experimental data [54]. The AMPT results, both with and without hadronic rescattering, are consistent. This behavior differs from previous findings in heavy-ion collisions, where the hadronic interaction significantly reduces the particle yield [55]. The spectrum obtained in the AMPT without considering the parton cascade process is enhanced compared to that obtained with partonic scattering, and this enhancement is more significant at a high pT. This outcome is expected because partons experience energy loss during the parton cascade, which reduces the production of final-state particles. In addition, the ratios of the pT spectra obtained from the AMPT calculations and data are shown. The AMPT model calculation reproduces the particle yields well at low and intermediate pT values when both partonic and hadronic scattering are included; however, it overestimates the high pT data because parton-parton inelastic collisions and, subsequently, hard parton fragmentation are absent in the model [42].

Fig. 1
(Color online) The pT distribution of pions, kaons, and protons in 0–20% high-multiplicity p–Pb collisions at sNN = 5.02 TeV, obtained from AMPT model calculations, is compared to ALICE measurement [54]. The results in AMPT without hadronic scattering and partonic scattering are also presented
pic

Figure 2 (left) shows the v2 of pions, kaons, protons and Λ as a function of pT in 0–20% high-multiplicity p–Pb collisions at sNN = 5.02 TeV, obtained in AMPT calculations with 3×2PC method. A comparison with the ALICE measurement for v2 of charged hadrons, pions, kaons, and protons [30] and the CMS measurement for v2 of Ks0 and Λ [31] is also presented. The AMPT calculations applied the template fit method to suppress the away-side jet contribution and considered the ZYAM assumption to enable direct comparison with the observed data. The v2 values of charged hadrons, pions, and kaons can be described well by AMPT calculations, but the v2 values of baryons (protons and Λ) cannot be reproduced. In addition, the mass-ordering effect (i.e., the v2 of baryons is lower than that of mesons) is reproduced for pT <2 GeV/c. Owing to the advanced flow extraction method, the calculations of v2 were extended to the high-pT region, up to 8 GeV/c in the AMPT model for the first time. The v2 values of protons and Λ are consistent, and both of them are observed to have a higher value of v2 value than that of the mesons for 2<pT< 7 GeV/c. The observed meson-baryon particle type grouping in heavy-ion collision flow measurements indicates collective behavior at the partonic level, leading to the coalescence of quarks into hadrons. The number-of-constituent-quarks (NCQ) scaling techniques described in [22] can be used for further studies of this grouping. v2 and pT in Fig. 2 (left) are replaced by v2/nq and pT/nq, where the nq is the number of constituent quark in mesons (nq = 2) and baryons (nq = 3), as shown in Fig. 2 (right). v2/nq obtained from the data show approximate values at intermediate pT; however, the results calculated in AMPT cannot reproduce the scaling in pT/nq>1 GeV/c. In order to consider the observed mass hierarchy of v2, we also plot the v2 of identified particle as a function of the transverse kinetic energy kET (kET=mTm0=pT2+m02m0), and its NCQ scaling in Fig. 3 (left), and Fig. 3 (right). All particle species showed a set of similar v2 values after NCQ scaling in kET/nq < 1 GeV, confirming that the quark degree of freedom in flowing matter can also be probed in the transport model. However, this NCQ scaling is violated for kET/nq > 1 GeV. This may be attributed to the hadronization mechanism implemented in the AMPT model used in this study, where baryons are produced only after the formation of mesons by simply combining the three nearest partons, regardless of the relative momentum among the coalescing partons. This results in an underestimation of the baryon v2 at intermediate pT in this study. An improved coalescence model implemented in the newer AMPT [56] introduced a new coalescence parameter to control the relative probability of a quark forming a baryon instead of a meson precisely. This improvement could have different NCQ scaling on v2 but requires more systematic studies. Further studies on v2 calculations in small collision systems with other improved hadronization mechanisms, for example, considering the Wigner function [57] and hard parton fragmentation [58], should be performed in the future.

Fig. 2
(Color online) Left: the v2 as a function of pT in 0–20% high-multiplicity p–Pb collisions at sNN = 5.02 TeV, obtained from default AMPT model calculations with 3×2PC method, is compared to ALICE and CMS measurement [30, 31]. Right: the pT-differential v2 scaled by the number of constituent quark (nq)
pic
Fig. 3
(Color online) Left: the v2 as a function of transverse kinetic energy (kET) in 0–20% high-multiplicity p–Pb collisions at sNN = 5.02 TeV, obtained from default AMPT model calculations with 3×2PC method, is compared to ALICE and CMS measurement [30, 31]. Right: the kET-differential v2 scaled by the number of constituent quark (nq)
pic

We also extend our investigation to include a study of integrated v2 within various centrality bins spanning the 0–60% range. We focus on the region where the NCQ scaling criterion is satisfied, that is, for transverse kinetic energies per constituent quark (kET/nq) ranging from 0.4 to 1 GeV. The non-flow contribution was estimated and subtracted within the 60–100% centrality class by using the template fit method. As shown in Fig. 4, the v2 values as a function of centrality exhibit a systematic decrease from central to peripheral collisions, reflecting the changing dynamic conditions and particle production mechanisms in different collision zones. Intriguingly, in the v2 measurements, we observed a distinct mass-splitting phenomenon, with baryons and mesons exhibiting distinct elliptic flow patterns. Such a mass dependence in v2 is similar to that in heavy-ion collisions at the LHC energies presented in a previous study [47]. This provides valuable insights into the collective behavior of different particle species within the evolving fireball created during these collisions.

Fig. 4
(Color online) The integrated v2 in 0.4<kET/nq<1 GeV for pion, kaon and proton varying with the centrality
pic

Moreover, to gain a deeper understanding of the NCQ scaling properties, we explored the ratios of nq-scaled integrated v2 values for protons relative to pions and kaons relative to pions as functions of centrality. The results are shown in Fig. 5. A notable trend is observed in these ratios: they tend to approach unity as the collisions become more peripheral. It indicates that the collective flow of particles in low-multiplicity events may be approaching a behavior that is closer to the expected scaling behavior based on the number of constituent quark.

Fig. 5
(Color online) The ratio of integrated v2 within 0.4<kET/nq<1 GeV for proton over pion and kaon over pion varying with the centrality. The dash line represents the location of unity ratio
pic

The effects of partonic and hadronic scattering on the elliptical anisotropy of the final-state particles were examined in this study. Figure 6 shows the calculated pT-differential v2 of pions, kaons, and protons in AMPT with and without considering hadronic rescattering process in 0—20% high-multiplicity p-Pb collisions. The results show that the ratio of the v2 values with and without hadronic rescattering is consistent with unity for all particle species, indicating that the hadronic rescattering mechanism has almost no effect on v2 in high-multiplicity p—Pb collisions. We also investigated the centrality dependence of the hadronic rescattering effects by calculating pT-integrated v2 in several centrality bins between 0 and 60%, as illustrated in Fig. 7. The results demonstrate that the influence of hadronic rescattering is independent of the centrality selection and has almost no impact on NCQ scaling in the range of 0.4<kET/nq<1 GeV.

Fig. 6
(Color online) The pT-differential v2 of pions, kaons, and protons calculated in AMPT model with and without considering hadronic scattering. The ratios of the two sets are also presented
pic
Fig. 7
(Color online) The integrated v2 in 0.4<kET/nq<1 GeV for pions, kaons, and protons calculated in AMPT model with and without considering hadronic scattering. The ratios of the two sets are also presented
pic

On the other hand, when we set the parton scattering cross-section σ to zero but maintain the hadronic scatterings, the V of charged particles for the central-forward (CF) and central-backward (CB) correlations is almost zero, as shown in Fig. 8. If both the partonic and hadronic scatterings are turned off, the results remain consistent with zero. This indicates that the elliptical anisotropy in high-multiplicity small-collision systems is mostly generated by parton scattering. Our conclusion is consistent with previous studies on the AMPT [41], which suggested that the majority of elliptic anisotropies comes from the anisotropic escape probability of partons.

Fig. 8
(Color online) The pT-differential V for central-forward (CF) and central-backward (CB) correlations calculated in AMPT model with and without considering partonic scattering
pic

Finally, different non-flow subtraction methods were investigated in this study. Figure 9 (left) shows the pT-differential v2 of the charged particles calculated using the 3×2PC method in 0—20% high-multiplicity p—Pb collisions. Several nonflow subtraction methods are implemented. To demonstrate how the nonflow contribution is removed, v2 obtained with a direct Fourier transform of the Cφ) correlation (as shown in Eq. 3). The results show significant suppression across all the subtraction methods, particularly at higher pT values where jet correlations are dominant. The results obtained with peripheral subtraction and template fitting were consistent, indicating that the away-side jet contribution was automatically removed using the 3×2PC method, even though the dependence of the jet correlation on multiplicity was not considered in the peripheral subtraction method. The v2 calculated using the improved template fit method was slightly lower than that from the template fit, and it was similar to the features observed in the ATLAS measurement [52]. The same conclusions were drawn for the extraction of the identified particles (pions, kaons, protons, and Λ) v2.

Fig. 9
(Color online) The pT-differential v2 of charged hadrons calculated in AMPT with different nonflow subtraction methods
pic
5

Summary

This study systematically investigated the elliptic anisotropy of identified particles (pions, kaons, protons, and Λ) in p—Pb collisions at 5.02 TeV using the AMPT model. We extended the calculation of v2 to higher pT regions, up to 8 GeV/c, using advanced nonflow subtraction techniques for the first time. We also examined the mass-ordering effect and baryon-meson grouping at low and intermediate pT, respectively. We argue that, with the approximate NCQ scaling of baryons and mesons, v2 can be reproduced well at kET/nq<1 GeV for several centrality bins. Furthermore, we demonstrate that parton interactions can simultaneously decrease the yield of light hadrons and generate significant v2. However, hadronic rescatterings had little influence on the elliptical anisotropy of the final-state particles. Thus, these findings indicate that the nonequilibrium anisotropic parton escape mechanism coupled with the quark coalescence model can also reproduce the hydro-like behavior of the identified particles observed in small collision systems. Overall, this study provides new insights into the existence of partonic collectivity in small collision systems.

References
1. E. V. Shuryak,

Quark-Gluon Plasma and Hadronic Production of Leptons, Photons and Psions

. Phys. Lett. B 78, 150 (1978). https://doi.org/10.1016/0370-2693(78)90370-2
Baidu ScholarGoogle Scholar
2. E. V. Shuryak,

Quantum Chromodynamics and the Theory of Superdense Matter

. Phys. Rept. 61, 71158 (1980). https://doi.org/10.1016/0370-1573(80)90105-2
Baidu ScholarGoogle Scholar
3. J. Y. Ollitrault,

Anisotropy as a signature of transverse collective flow

. Phys. Rev. D 46, 229245 (1992). https://doi.org/10.1103/PhysRevD.46.229
Baidu ScholarGoogle Scholar
4. S. A. Voloshin,

Anisotropic collective phenomena in ultra-relativistic nuclear collisions

. Nucl. Phys. A 827, 377C382C (2009). https://doi.org/10.1016/j.nuclphysa.2009.05.082
Baidu ScholarGoogle Scholar
5. S. Voloshin and Y. Zhang,

Flow study in relativistic nuclear collisions by Fourier expansion of Azimuthal particle distributions

. Z. Phys. C 70, 665672 (1996). https://doi.org/10.1007/s002880050141
Baidu ScholarGoogle Scholar
6. A. M. Poskanzer and S. A. Voloshin,

Methods for analyzing anisotropic flow in relativistic nuclear collisions

. Phys. Rev. C 58, 16711678 (1998). https://doi.org/10.1103/PhysRevC.58.1671
Baidu ScholarGoogle Scholar
7. B. Alver and G. Roland,

Collision geometry fluctuations and triangular flow in heavy-ion collisions

. Phys. Rev. C 81, 054905 (2010). https://doi.org/10.1103/PhysRevC.81.054905
Baidu ScholarGoogle Scholar
8. B. H. Alver, C. Gombeaud, M. Luzum et al.,

Triangular flow in hydrodynamics and transport theory

. Phys. Rev. C 82, 034913 (2010). https://doi.org/10.1103/PhysRevC.82.034913
Baidu ScholarGoogle Scholar
9. G.-Y. Qin, H. Petersen, S. A. Bass et al.,

Translation of collision geometry fluctuations into momentum anisotropies in relativistic heavy-ion collisions

. Phys. Rev. C 82, 064903 (2010). https://doi.org/10.1103/PhysRevC.82.064903
Baidu ScholarGoogle Scholar
10. D. Teaney and L. Yan.

Triangularity and Dipole Asymmetry in Heavy Ion Collisions

. Phys. Rev. C 83, 064904 (2011). https://doi.org/10.1103/PhysRevC.83.064904
Baidu ScholarGoogle Scholar
11. I. Arsene, I. G. Bearden, D. Beavis et al.,

Quark gluon plasma and color glass condensate at RHIC? The Perspective from the BRAHMS experiment

. Nucl. Phys. A 757, 127 (2005). https://doi.org/10.1016/j.nuclphysa.2005.02.130
Baidu ScholarGoogle Scholar
12. K. Adcox, S. S. Adler, S. Afanasiev et al.,

Formation of dense partonic matter in relativistic nucleus-nucleus collisions at RHIC: Experimental evaluation by the PHENIX collaboration

. Nucl. Phys. A 757, 184283 (2005). https://doi.org/10.1016/j.nuclphysa.2005.03.086
Baidu ScholarGoogle Scholar
13. B. B. Back, M. D. Baker, M. Ballintijn et al.,

The PHOBOS perspective on discoveries at RHIC

. Nucl. Phys. A 757, 28101 (2005). https://doi.org/10.1016/j.nuclphysa.2005.03.084
Baidu ScholarGoogle Scholar
14. J. Adams, M. M. Aggarwal, Z. Ahammed et al.,

Experimental and theoretical challenges in the search for the quark gluon plasma: The STAR Collaboration’s critical assessment of the evidence from RHIC collisions

. Nucl. Phys. A 757, 102183 (2015). https://doi.org/10.1016/j.nuclphysa.2005.03.085
Baidu ScholarGoogle Scholar
15. K. Aamodt, B. Abelev, A. A. Quintana et al.,

Higher harmonic anisotropic flow measurements of charged particles in Pb–Pb collisions at sNN = 2.76 TeV

. Phys. Rev. Lett. 107, 032301 (2011). https://doi.org/10.1103/PhysRevLett.107.032301
Baidu ScholarGoogle Scholar
16. S. Acharya, F. T. -. Acosta, D. Adamová et al.,

Energy dependence and fluctuations of anisotropic flow in Pb–Pb collisions at sNN = 5.02 and 2.76 TeV

. J. High Energy Phys. 07, 103 (2018). https://doi.org/10.1007/JHEP07(2018)103
Baidu ScholarGoogle Scholar
17. G. Aad, B. Abbott, J. Abdallah et al.,

Measurement of the azimuthal anisotropy for charged particle production in sNN = 2.76 TeV lead-lead collisions with the ATLAS detector

. Phys. Rev. C 86, 014907 (2012). https://doi.org/10.1103/PhysRevC.86.014907
Baidu ScholarGoogle Scholar
18. S. Chatrchyan, V. Khachatryan, A. M. Sirunyan et al.,

Measurement of Higher-Order Harmonic Azimuthal Anisotropy in PbPb Collisions at sNN = 2.76 TeV

. Phys. Rev. C 89, 044906 (2014). https://doi.org/10.1103/PhysRevC.89.044906
Baidu ScholarGoogle Scholar
19. S. Acharya, D. Adamová, A. Adler et al.,

Anisotropic flow and flow fluctuations of identified hadrons in Pb–Pb collisions at sNN = 5.02 TeV

. J. High Energy Phys. 05, 243 (2023). https://doi.org/10.1007/JHEP05(2023)243
Baidu ScholarGoogle Scholar
20. S. Acharya, D. Adamová, A. Adler et al.,

Anisotropic flow of identified hadrons in Xe-Xe collisions at sNN = 5.44 TeV

. J. High Energy Phys. 10, 152 (2021). https://doi.org/10.1007/JHEP10(2021)152
Baidu ScholarGoogle Scholar
21. Y.-G. Ma,

The Collective Flow from the Degree of Freedom of Nucleons to Quarks

. Journal of Fudan University (Natural Science). 62, 273292,309 (2023). https://doi.org/10.15943/j.cnki.fdxb-jns.20230525.001
Baidu ScholarGoogle Scholar
22. D. Molnar and S. A. Voloshin,

Elliptic flow at large transverse momenta from quark coalescence

. Phys. Rev. Lett. 91, 092301 (2003). https://doi.org/10.1103/PhysRevLett.91.092301
Baidu ScholarGoogle Scholar
23. Z.-W. Lin and D. Molnar,

Quark coalescence and elliptic flow of charm hadrons

. Phys. Rev. C 68, 044901 (2003). https://doi.org/10.1103/PhysRevC.68.044901
Baidu ScholarGoogle Scholar
24. M. Wang, J.-Q. Tao, H. Zheng et al.,

Number-of-constituent-quark scaling of elliptic flow: a quantitative study

. Nucl. Sci. Tech. 33, 37 (2022). https://doi.org/10.1007/s41365-022-01019-9
Baidu ScholarGoogle Scholar
25. T.-Z. Yan, Y.-G. Ma, X.-Z. Cai et al.,

Scaling of anisotropic flow and momentum-space densities for light particles in intermediate energy heavy ion collisions

. Phys. Lett. B 638, 5054, (2006). https://doi.org/10.1016/j.physletb.2006.05.018
Baidu ScholarGoogle Scholar
26. S. Chatrchyan, V. Khachatryan, A. M. Sirunyan et al.,

Observation of Long-Range Near-Side Angular Correlations in Proton-Lead Collisions at the LHC

. Phys. Lett. B 718, 795814 (2013). https://doi.org/10.1016/j.physletb.2012.11.025
Baidu ScholarGoogle Scholar
27. B. Abelev, J. Adam, D. Adamová et al.,

Long-range angular correlations on the near and away side in p–Pb collisions at sNN = 5.02 TeV

. Phys. Lett. B 719, 2941 (2013). https://doi.org/10.1016/j.physletb.2013.01.012
Baidu ScholarGoogle Scholar
28. G. Aad, T. Abajyan, B. Abbott et al.,

Observation of Associated Near-Side and Away-Side Long-Range Correlations in sNN=5.02 TeV Proton-Lead Collisions with the ATLAS Detector

. Phys. Rev. Lett. 110, 182302 (2013). https://doi.org/10.1103/PhysRevLett.110.182302
Baidu ScholarGoogle Scholar
29. M. I. Abdulhamid, B. E. Aboona, J. Adam et al.,

Measurements of the Elliptic and Triangular Azimuthal Anisotropies in Central 3He+Au, d+Au and p+Au Collisions at sNN = 200 GeV

. Phys. Rev. Lett. 130, 242301 (2023). https://doi.org/10.1103/PhysRevLett.130.242301
Baidu ScholarGoogle Scholar
30. B. Abelev, J. Adam, D. Adamová et al.,

Long-range angular correlations of π, K and p in p–Pb collisions at sNN = 5.02 TeV

. Phys. Lett. B 726, 164177 (2013). https://doi.org/10.1016/j.physletb.2013.08.024
Baidu ScholarGoogle Scholar
31. A. M. Sirunyan, A. Tumasyan, W. Adam et al.,

Elliptic flow of charm and strange hadrons in high-multiplicity pPb collisions at sNN = 8.16 TeV

. Phys. Rev. Lett. 121, 082301 (2018). https://doi.org/10.1103/PhysRevLett.121.082301
Baidu ScholarGoogle Scholar
32. P. Bozek,

Collective flow in p-Pb and d-Pd collisions at TeV energies

. Phys. Rev. C 85, 014911 (2012). https://doi.org/10.1103/PhysRevC.85.014911
Baidu ScholarGoogle Scholar
33. P. Bozek and W. Broniowski,

Correlations from hydrodynamic flow in p-Pb collisions

. Phys. Lett. B 718, 15571561 (2013). https://doi.org/10.1016/j.physletb.2012.12.051
Baidu ScholarGoogle Scholar
34. J. L. Nagle and W. A. Zajc,

Small System Collectivity in Relativistic Hadronic and Nuclear Collisions

. Ann. Rev. Nucl. Part. Sci. 68, 211235 (2018). https://doi.org/10.1146/annurev-nucl-101916-123209
Baidu ScholarGoogle Scholar
35. G. Nijs, W. van der Schee, U. Gürsoy et al.,

Bayesian analysis of heavy ion collisions with the heavy ion computational framework Trajectum

. Phys. Rev. C 103, 054909 (2021). https://doi.org/10.1103/PhysRevC.103.054909
Baidu ScholarGoogle Scholar
36. G. Nijs, W. van der Schee, U. Gürsoy et al.,

Transverse Momentum Differential Global Analysis of Heavy-Ion Collisions

. Phys. Rev. Lett. 126, 202301 (2021). https://doi.org/10.1103/PhysRevLett.126.202301
Baidu ScholarGoogle Scholar
37. W.-B. Zhao, C.-M. Ko, Y.-X. Liu et al.,

Probing the Partonic Degrees of Freedom in High-Multiplicity p–Pb collisions at sNN = 5.02 TeV

. Phys. Rev. Lett. 125, 072301 (2020). https://doi.org/10.1103/PhysRevLett.125.072301
Baidu ScholarGoogle Scholar
38. K. Dusling and R. Venugopalan,

Comparison of the color glass condensate to dihadron correlations in proton-proton and proton-nucleus collisions

. Phys. Rev. D 87, 094034 (2013). https://doi.org/10.1103/PhysRevD.87.094034
Baidu ScholarGoogle Scholar
39. K. Dusling and R. Venugopalan,

Evidence for BFKL and saturation dynamics from dihadron spectra at the LHC

. Phys. Rev. D 87, 051502 (2013). https://doi.org/10.1103/PhysRevD.87.051502
Baidu ScholarGoogle Scholar
40. Z.-W. Lin, C. M. Ko, B.-A. Li et al.,

A Multi-phase transport model for relativistic heavy ion collisions

. Phys. Rev. C 72, 064901 (2005). https://doi.org/10.1103/PhysRevC.72.064901
Baidu ScholarGoogle Scholar
41. L. He, T. Edmonds, Z.-W. Lin et al.,

Anisotropic parton escape is the dominant source of azimuthal anisotropy in transport models

. Phys. Lett. B 753, 506510 (2016). https://doi.org/10.1016/j.physletb.2015.12.051
Baidu ScholarGoogle Scholar
42. Z.-W. Lin and L. Zheng,

Further developments of a multi-phase transport model for relativistic nuclear collisions

. Nucl. Sci. Tech. 32, 113 (2021). https://doi.org/10.1007/s41365-021-00944-5
Baidu ScholarGoogle Scholar
43. X.-N. Wang and M. Gyulassy,

HIJING: A Monte Carlo model for multiple jet production in pp, pA and AA collisions

. Phys. Rev. D 44, 35013516 (1991). https://doi.org/10.1103/PhysRevD.44.3501
Baidu ScholarGoogle Scholar
44. M. Gyulassy and X.-N. Wang,

HIJING 1.0: A Monte Carlo program for parton and particle production in high-energy hadronic and nuclear collisions

. Comput. Phys. Commun. 83, 307 (1994). https://doi.org/10.1016/0010-4655(94)90057-4
Baidu ScholarGoogle Scholar
45. B. Zhang,

ZPC 1.0.1: A Parton cascade for ultrarelativistic heavy ion collisions

. Comput. Phys. Commun. 109, 193206 (1998). https://doi.org/10.1016/S0010-4655(98)00010-1
Baidu ScholarGoogle Scholar
46. B.-A. Li and C. M. Ko,

Formation of superdense hadronic matter in high-energy heavy ion collisions

. Phys. Rev. C 52, 20372063 (1995). https://doi.org/10.1103/PhysRevC.52.2037
Baidu ScholarGoogle Scholar
47. L. Zheng, H. Li, H. Qin et al.,

Investigating the NCQ scaling of elliptic flow at LHC with a multiphase transport model

. Eur. Phys. J. A 53, 124 (2017). https://doi.org/10.1140/epja/i2017-12312-8
Baidu ScholarGoogle Scholar
48. Y.-G. Ma and W.-Q. Shen,

Correlation functions and the disappearance of rotational collective motion in nucleus-nucleus collisions below 100 MeV/nucleon

. Phys. Rev. C 51, 32563263 (1995). https://doi.org/10.1103/PhysRevC.51.3256
Baidu ScholarGoogle Scholar
49. N. J. Abdulameer, U. Acharya, A. Adare et al.,

Measurements of second-harmonic Fourier coefficients from azimuthal anisotropies in p+p, p+Au, d+Au, and 3He+Au collisions at sNN = 200 GeV

. Phys. Rev. C 107, 024907 (2023). https://doi.org/10.1103/PhysRevC.107.024907
Baidu ScholarGoogle Scholar
50. S. H. Lim, Q. Hu, R. Belmont et al.,

Examination of flow and nonflow factorization methods in small collision systems

. Phys. Rev. C 100, 024908 (2019). https://doi.org/10.1103/PhysRevC.100.024908
Baidu ScholarGoogle Scholar
51. G. Aad, B. Abbott, J. Abdallah et al.,

Observation of Long-Range Elliptic Azimuthal Anisotropies in s = 13 and 2.76 TeV pp Collisions with the ATLAS Detector

. Phys. Rev. Lett. 116, 172301 (2016). https://doi.org/10.1103/PhysRevLett.116.172301
Baidu ScholarGoogle Scholar
52. M. Aaboud, G. Aad, B. Abbott et al.,

Correlated long-range mixed-harmonic fluctuations measured in pp, p+Pb and low-multiplicity Pb+Pb collisions with the ATLAS detector

. Phys. Lett. B 789, 444471 (2019). https://doi.org/10.1016/j.physletb.2018.11.065
Baidu ScholarGoogle Scholar
53. B. Abelev, A. Abramyan, J. Adam et al.,

Performance of the ALICE Experiment at the CERN LHC

. Int. J. Mod. Phys. A 29, 1430044 (2014). https://doi.org/10.1142/S0217751X14300440
Baidu ScholarGoogle Scholar
54. J. Adam, D. Adamová, M. M. Aggarwa et al.,

Multiplicity dependence of charged pion, kaon, and (anti)proton production at large transverse momentum in p-Pb collisions at sNN = 5.02 TeV

. Phys. Lett. B 760, 720735 (2016). https://doi.org/10.1016/j.physletb.2016.07.050
Baidu ScholarGoogle Scholar
55. Z.-W. Lin, S. Pal, C. M. Ko et al.,

Charged particle rapidity distributions at relativistic energies

. Phys. Rev. C 64, 011902 (2001). https://doi.org/10.1103/PhysRevC.64.011902
Baidu ScholarGoogle Scholar
56. Y.-C. He and Z.-W. Lin,

Improved Quark Coalescence for a Multi-Phase Transport Model

. Phys. Rev. C 96, 014910 (2017). https://doi.org/10.1103/PhysRevC.96.014910
Baidu ScholarGoogle Scholar
57. F.-T. Wang and J. Xu,

Hadronization using the Wigner function approach for a multiphase transport model

. Phys. Rev. C 100, 064909 (2019). https://doi.org/10.1103/PhysRevC.100.064909
Baidu ScholarGoogle Scholar
58. C. Zhang, L. Zheng, S.-S. Shi et al.,

Resolving the RpA and v2 puzzle of D0 mesons in p-Pb collisions at the LHC

. https://arxiv.org/abs/2210.07767
Baidu ScholarGoogle Scholar
Footnote

The authors declare that they have no competing interests.