Effects of Volume Corrections and Resonance Decays on Cumulants of Net-Charge Distributions in a Monte Carlo Hadron Resonance Gas Model
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Physics Letters B 765 (2017) 188–192 Contents lists available at ScienceDirect Physics Letters B www.elsevier.com/locate/physletb Effects of volume corrections and resonance decays on cumulants of net-charge distributions in a Monte Carlo hadron resonance gas model Hao-jie Xu Department of Physics and State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing 100871, China a r t i c l e i n f o a b s t r a c t Article history: The effects of volume corrections and resonance decays (the resulting correlations between positive Received 27 September 2016 charges and negative charges) on cumulants of net-proton distributions and net-charge distributions Received in revised form 20 November 2016 are investigated by using a Monte Carlo hadron resonance gas (MCHRG) model. The required volume Accepted 7 December 2016 distributions are generated by a Monte Carlo Glauber (MC-Glb) model. Except the variances of net- Available online 12 December 2016 charge distributions, the MCHRG model with more realistic simulations of volume corrections, resonance Editor: J.-P. Blaizot decays and acceptance cuts can reasonably explain the data of cumulants of net-proton distributions and net-charge distributions reported by the STAR collaboration. The MCHRG calculations indicate that both the volume corrections and resonance decays make the cumulant products of net-charge distributions deviate from the Skellam expectations: the deviations of Sσ and κσ2 are dominated by the former effect while the deviations of ω are dominated by the latter one. © 2016 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3. 1. Introduction into account in study of the net-charge fluctuations, because lots of positive–negative charge pairs are generated from resonance de- Recently, tremendous experimental and theoretical efforts have cays. been made to determine the phase diagram of Quantum Chro- For the data reported by the STAR collaboration [8], on the modynamics (QCD) [1–5]. Among various observables, the cumu- other hand, the volume corrections play significant role on the cu- lants of net-charge distributions and net-proton distributions, mea- mulants (cumulant products) of net-charge distributions [19,20]. sured by beam energy scan (BES) program from the Relativistic Therefore, besides the information of chemical potential and tem- Heavy Ion Collider (RHIC) at Brookhaven National√ Laboratory (BNL) perature investigated in previous studies, the volume information = √with a wide range of collisional energies from sNN 7.7GeVto is also important for theoretical baseline studies [20]. Meanwhile, sNN = 200 GeV [6–10], that are expected to provide us some cru- with the volume information, one can study the centrality depen- cial information about the critical end point (CEP) of QCD phase dence of multiplicity distributions in relativistic heavy ion colli- diagram. Besides the fluctuation data and theoretical studies on sions. critical fluctuations [11–14], the theoretical baselines from non- In this work, with the volume information generated by a critical statistical fluctuation studies are also important [15–30]. Monte Carlo Glauber (MC-Glb) model, I propose a Monte Carlo The hadron resonance gas (HRG) models have been employed to hadron resonance gas (MCHRG) model to study the effect of volume investigate the theoretical baselines of multiplicity distributions in corrections and resonance decays on the cumulants of net-proton various previous studies [21–30]. Especially in Ref. [29], Nahrgang distributions and net-charge distributions in a transparent way. and her collaborators split the resonance decay contributions into Instead of primordial particles used in previous studies, the ac- two parts: an average part and a probabilistic part, but ignore the ceptance cuts can be applied to the decay products in the MCHRG correlations generated from resonance decays. This method is ap- simulations. Based on these advantages, the MCHRG model can be propriate for the net-proton distributions discussed in Ref. [29], used to give more realistic baselines for the cumulants of multi- because no correlation between baryons and anti-baryons can be plicity distributions than previous HRG models. generated from resonance decays [29]. However, the correlations between positive charges and negative charges should be taken 2. Monte Carlo hadron resonance gas model In the MCHRG model, the multiplicities of different particle E-mail address: [email protected]. species in each event are randomly generated by Poisson distri- http://dx.doi.org/10.1016/j.physletb.2016.12.015 0370-2693/© 2016 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Funded by SCOAP3. H.-j. Xu / Physics Letters B 765 (2017) 188–192 189 1 butions, and the Poisson parameters are calculated by [31] positive and negative charges. The kinetic freeze-out parameters T = 120 MeV and β = 0.6are used to include the effect of 2 kin S Tch gim μi mi λ = V i exp K , (1) flow and more realistic simulation of acceptance cuts. The chemi- i 2 2 = = (2π) Tch Tch cal freeze-out parameters Tch 151 MeV and μB 98.5MeVare determined by the multiplicity ratios p/π and p/p¯ , which are con- where gi is degeneracy factor, mi is particle mass, Tch is chem- sist with the parameters obtained in Ref. [24]. Since I do not focus ical freeze-out temperature. The chemical potential μi = BiμB + on the details of strangeness fluctuations, the chemical√ freeze-out SiμS + Q i μQ with the baryon number Bi , strangeness number Si , parameter μS is just obtained from μS = dS /(1 + eS sNN) with charge number Q i and the corresponding chemical potentials μB , ds = 0.214 and eS = 0.161 [23]. The chemical freeze-out parame- μS , μQ . K is modified Bessel function. Note that I have neglected ter μQ is determined by the multiplicity ratio of positive charges the effects of quantum statistics on multiplicity fluctuations. and negative charges. The mean multiplicity of net-charges is the To study the effect of resonance decays, I use 319 primordial remainder of two large values, i.e. mean multiplicity of positive particle species as inputs and 26 stable particle species after per- and negative charges, which are two orders of magnitude larger forming Monte Carlo resonance decays, as the particle species used than the remainder. To give a good description of centrality depen- in [29]. The resonance decay channels are taken from [32] and the dence of mean multiplicity of net-charges, therefore, a centrality- contributions from weak decays are not taken into account in the dependent μQ is parameterized as present study. For more realistic simulations of acceptance cuts, the transverse μQ = μQ 0 + aQ tanh 0.4( npart − 8) , (6) momentum (pT ) spectra of primordial particles are simulated by =− = the blast-wave model [33] with μQ 0 4.7 MeV and aQ 1.5MeV. Besides μQ , in general, all the chemical and kinetic freeze-out parameters are centrality- 1 dependent for more precise constraint. For simplicity in this work, dN p⊥ sinh ρ m⊥ cosh ρ ∝ xdxm⊥ I0 K1 , (2) all of them except μQ are set to constants to roughly reproduce pT dpT Tkin Tkin the mean multiplicity of positive charges, negative charges, net- 0 charges [8], protons, anti-protons and net-protons [10] with some 2 2 specific acceptance cuts. Note that the main conclusions obtained where mT = p + m , Tkin is kinetic freeze-out temperature, T in the present study are almost independent of the selection of = −1 = ρ tanh (β) and β βS x with βS being the velocity at volume collision energy and model parameters, and the impact of Glauber boundary. And the rapidity (y) distribution is modeled by [34] parameters on cumulant calculations has been investigated in my dN previous study [20]. ∝ exp ( (L2 − y2)) (3) I apply the same acceptance cuts as used in experiment [8,10]. dy | | √ More specifically, η < 0.5, 0.2 < pT < 2.0GeVfor fluctuation ± ± ¯ with L = ln( sNN/mp) and mp is the mass of proton. measures (π , K and p/p after removing protons and anti- To study the centrality dependence of multiplicity distributions, protons with pT < 0.4GeV) and 1.0 > |η| > 0.5, 0.2 < pT < as well as the volume corrections on high order cumulants of 2.0GeVfor reference particles (total charged hadrons) in the net- multiplicity distribution in MCHRG model, a Monte Carlo Glauber charge case; |y| < 0.5, 0.4 < pT < 0.8GeVfor fluctuation mea- (MC-Glb) model [35] is employed to generate the volume infor- sures (p/p¯ ) and |η| < 1.0, 0.2 < pT < 2.0GeVfor reference par- ± ± mation in each event (thermal system), ticles (π and K ) in the net-proton case. Here η is pseudo- rapidity. 1 − x V = h npart + xnncoll , (4) 2 3. Results and discussions where npart is the number of participant nucleons and ncoll is the Fig. 1 shows the centrality-dependent cumulants of net-proton number of binary nucleon–nucleon collisions. distributions and net-charge distributions for Au+Au collisions at With the events generated from the MCHRG model, the first √ s = 39 GeV. To study the effect of volume corrections and four cumulants of multiplicity distributions are calculated by [7, NN resonance decays on cumulants of multiplicity distributions, some 8,10] other baselines are also shown in the figures: one is the Skellam baseline, which is obtained by c1 =N≡M, = 2≡ 2 S = S = + − − S = S = + + −; c2 ( N) σ , c1 c3 c1 c1 , c2 c4 c1 c1 (7) 3 3 c3 =( N) ≡Sσ , the other one is the independent product (IP) baseline, which is = 4− 2 ≡ 4 obtained by c4 ( N) 3c2 κσ , (5) + − cIP = c + (−1)nc .