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Study of the η\eta to π0\pi^{0} Ratio in Heavy-Ion Collisions

Yuanjie Ren yuanjie@mit.edu Department of Physics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA    Axel Drees axel.drees@stonybrook.edu Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11790, USA
Abstract

We demonstrate that the pTp_{T} dependence of the η/π0\eta/\pi^{0} ratio is universal within a few percent for high energy pp+pp, pp+A and dd+A collisions, over a broad range of collision energies. The η/π0\eta/\pi^{0} ratio increases with pTp_{T} up to 4 to 5 GeV/cc where it saturates at a nearly constant value of 0.487±\pm0.024. Above pT=5p_{T}=5 GeV/cc the same constant value is also observed in A+A collisions independent of collision system, energy, and centrality. At lower pTp_{T}, where accurate η/π0\eta/\pi^{0} data is absent for A+A collisions, we estimate possible deviations from the universal behavior, which could arise due to the rapid radial hydrodynamic expansion of the A+A collision system. For A+A collisions at RHIC we find that possible deviations are limited to the pTp_{T} range from 0.4 to 3 GeV/cc, and remain less than 20% for the most central collisions.

pacs:
25.75.Cj, 25.75.Dw, 25.75.Ld

I Introduction

Photons are generally considered ideal probes to study the quark gluon plasma (QGP) created in heavy ion collisions [1], since they have a long mean free path and leave the collision volume without final state interactions. Of particular interest are low momentum or thermal photons with energies of up to several times the temperature of the QGP. The measurement of thermal photons has only recently been possible with the advance of the heavy ion programs at RHIC [2, 3, 4] and LHC [5].

One of the experimental key challenges for these measurements is to estimate and subtract photons from hadron decays that constitute the bulk of photons measured in experiments. The two major contributions of photons result from π0γ+γ\pi^{0}\rightarrow\gamma+\gamma and ηγ+γ\eta\rightarrow\gamma+\gamma decays. Precise knowledge of the parent π0\pi^{0} and η\eta pTp_{T} spectra is necessary to estimate the decay photon background. While spectra of pions from heavy ion collisions are well measured at RHIC and LHC, less data exists for η\eta spectra, in particular below pTp_{T} of 2 GeV/cc. Therefor experiments need to make assumptions how to model the η\eta spectra below 2 GeV/cc, which leads to sizable systematic uncertainties. Frequently, experiments have based this extrapolation on the hypothesis of transverse mass mTm_{T} scaling of meson spectra [3, 4, 5]. However, it is known since the late 1990’s [6] and was recently pointed out again [7] that mTm_{T} scaling does not hold below 3 GeV for the η\eta meson.

In this paper we propose a new empirical approach to model the η\eta spectrum that is based on the universality of the η/π0\eta/\pi^{0} ratio across collision systems, beam energies, and centrality selections in heavy ion collisions. With a good understanding of the η/π0\eta/\pi^{0} ratio as function of transverse momentum pTp_{T} and measured π0\pi^{0} spectra, which are readily available for many collision systems, one can construct a more accurate pTp_{T} distribution for η\eta mesons.

The paper is organized as follows. In the next section we elaborate more on the failure of mTm_{T} scaling. In section III we will discuss two empirical fits and a Gaussian Process Regression (GPR) to describe the η/π0\eta/\pi^{0} ratio for pp+pp and pp+A collisions, and document in section IV the universality of η/π0\eta/\pi^{0} across different collision systems (pp+pp, pp+A, A+A), energies, and collision centrality. In Section V, we estimate possible deviation from the universal trend at low pTp_{T} due to radial flow in heavy ion collisions. We provide our result for η/π0\eta/\pi^{0} for RHIC and LHC energies with systematic uncertainties in the final part.

II The Failure of Transverse Mass Scaling

For measurements of direct photons from heavy ion collisions, the photons from η\eta and heavier meson decays are frequently estimated using measured π0\pi^{0} spectra in conjunction with the mTm_{T} scaling hypothesis. A typical implementation of this method [8] starts with a fit to the π0\pi^{0} spectra with a functional form like a modified Hagedorn function [9]:

12πpTd2NdydpT=A(MX)(eag(pT)bg(pT)2+g(pT,MX)p0)n\displaystyle\frac{1}{2\pi p_{T}}\frac{\differential^{2}N}{\differential y\differential p_{T}}=A(M_{X})\quantity(e^{-ag(p_{T})-bg(p_{T})^{2}}+\frac{g(p_{T},M_{X})}{p_{0}})^{-n}

with MXM_{X} being the meson mass and g(pT,MX)=pT2+mX2mπ2g(p_{T},M_{X})=\sqrt{p_{T}^{2}+m_{X}^{2}-m^{2}_{\pi}}. In this implementation the spectra of the η\eta and heavier mass mesons follow the same distribution with respect to transverse mass mTm2+pT2m_{T}\equiv\sqrt{m^{2}+p_{T}^{2}} as the π0\pi^{0}. The normalisation constant A(MX)A(M_{X}) is the only free parameter, all other parameters are fixed by the fit to the π0\pi^{0} data. A(MX)A(M_{X}) is fitted to experimental data whenever such data exists.

Refer to caption
Figure 1: The η/π0\eta/\pi^{0} ratio of p+p and p+A collisions. Also plotted are the η/π0\eta/\pi^{0} determined by mTm_{T}-scaling and from a pythia calculation.

Fig. 1 compiles available data of η/π0\eta/\pi^{0} for pp+pp [10, 11, 12, 13, 14] and pp+A [6, 15] collisions. Also shown on the figure is the result of mTm_{T} scaling for two different normalisation constants A(Mη)A(M_{\eta}) [12, 11, 16] and the expectation from a pythia-6 calculation from [17, 12]. While pythia and the mTm_{T} scaling hypothesis agree well, a significant deviation from the data is seen at low pTp_{T}. This was originally discovered at the CERN SPS by CERES/TAPS [6] more than 20 years ago and recently confirmed by ALICE at the LHC [10]. Clearly the mTm_{T} scaling hypothesis is not correct and should not be used to extrapolate meson spectra to low pTp_{T} for systems where no data exists.

III Description of the η/π0\eta/\pi^{0} ratio for p+p and p+A collisions

The quantitative agreement of the η/π0\eta/\pi^{0} data shown in Fig. 1 is striking, consider the data covers more than 2 orders of magnitude in collision energy. In this section we will test different methods to obtain an empirical description of η/π0\eta/\pi^{0}. The first two methods (A,B) fit a functional shape of the ratio, while the third method (GPR) is a Gaussian Process Regression that does not assume a specific functional shape. All methods yield similar results below 10 GeV/cc, at larger pTp_{T} the deviations are sizable and we will include these deviations in our evaluation of systematic uncertainties.

III.1 Empirical fit A

Method A starts with a ratio of two functions of the form given in Equation LABEL:Eq:modified_hagedorn. The mTm_{T}-scaling hypothesis is used to reduce the number of parameters:

Rη/π0(pT)=R(eag(pT)bg(pT)2+g(pT)p0)n(eapTbpT2+pTp0)n.R^{\eta/\pi^{0}}(p_{T})=R^{\infty}\frac{\quantity(e^{-a\cdot g(p_{T})-b\cdot g(p_{T})^{2}}+\frac{g(p_{T})}{p_{0}})^{-n}}{\quantity(e^{-ap_{T}-bp_{T}^{2}}+\frac{p_{T}}{p_{0}})^{-n}}. (2)

The advantage of this method is that it preserves a realistic functional form for the pTp_{T} spectra with an exponential decrease at low pTp_{T} and power law shape at high pTp_{T}. In principle, this ensures that at high pTp_{T} the η/π0\eta/\pi^{0} ratio approaches a constant value RR^{\infty}. However, unlike starting from the π0\pi^{0} spectrum, the parameters are fitted to the η/π0\eta/\pi^{0} ratio from p+p and p+A collisions shown in Fig. 1. We achieve a good fit, though the values of the fit parameters are nonphysical and do not describe the individual pTp_{T} spectra. The result is depicted in Fig. 2.

The band represents the total uncertainty of the fit function from two sources, the uncertainty of fit parameters, and the systematic uncertainties from data points. The former can be calculated analytically thanks to the explicit fit function while the latter can be obtained via a “data shuffling approach” which uses a Monte Carlo technique to vary individual data sets within their systematic uncertainties. This approach is discussed in Appendix B. The total uncertainty shown on the figure represents the quadratic sum of statistical and systematic uncertainties.

III.2 Empirical fit B

The second empirical fit function has a very similar form, except that normalization of the exponential and power law component in the numerator are decoupled by introducing an additional parameter. This is implemented such that RR^{\infty} remains the asymptotic value at high pTp_{T}.

Rη/π0(pT)=A(eag(pT)bg(pT)2+(RA)1ng(pT)p0)n(eapTbpT2+pTp0)n.R^{\eta/\pi^{0}}(p_{T})=A\frac{\quantity(e^{-a\cdot g(p_{T})-b\cdot g(p_{T})^{2}}+\quantity(\frac{R^{\infty}}{A})^{-\frac{1}{n}}\frac{g(p_{T})}{p_{0}})^{-n}}{\quantity(e^{-ap_{T}-bp_{T}^{2}}+\frac{p_{T}}{p_{0}})^{-n}}. (3)

The handling of fit and the calculation of the uncertainties is identical to Method A. The result is also shown in Fig. 2. In contrast to Method A, which only gradually approaches the asymptotic value at high pTp_{T}, Method B reaches the constant at pTp_{T} of about 5 GeV/cc and at a lower R=0.487±0.024R^{\infty}=0.487\pm 0.024 value, which will be used as a reference throughout this article. We note that the change to the constant value is rather abrupt.

III.3 Gaussian Process Regression (GPR)

Both previous methods have a built-in assumption that the η/π0\eta/\pi^{0} has a constant asymptotic value at high pTp_{T}. However, the data suggest that there might be a maximum around 8 GeV followed by a decrease towards higher pTp_{T}. In order to avoid any assumptions about the shape we resort to a machine learning technique called Gaussian Process Regression (GPR), which possesses no physical knowledge but gives full trust to the data it is given. Details about the GPR can be found in [18], and comments about the specific implementation we use are summarised in Appendix A. In general the GPR works best in the region where many consistent data points are available. Less data points or inconsistent data sets lead to larger uncertainties, and unlike the fitting methods the GPR can not reliably extrapolate much beyond the range covered by data.

The result of the GPR is presented in Fig. 2, with the band indicating the uncertainties. Over most of the pTp_{T} range the GPR gives an equally good description of the data compared to Methods A and B. As expected, it follows the data and peaks near 8 GeV/cc. Towards higher pTp_{T} η/π0\eta/\pi^{0} from the GPR decreases. Whether the drop at high pTp_{T} is physical or an artefact of different data sets with different pTp_{T} ranges not being perfectly consistent in the range from 3 to 10 GeV/cc will only be resolved with more precise data.

Refer to caption
Figure 2: Data for the η/π0\eta/\pi^{0} ratio from pp+pp and pp+A collisions compared to three different methods to describe the data with a universal shape: empirical fit A, empirical fit B, and GPR.
Refer to caption
Figure 3: Result of combining the three empirical methods to one universal estimate of η/π0\eta/\pi^{0} as function of pTp_{T}. Also shown for reference are the estimates based on the mTm_{T} scaling hypothesis and the result of a pythia calculation, both from Fig. 1.

Since we do not know the correct functional form of η/π0\eta/\pi^{0}, in particular at high pTp_{T}, we combine the results obtained with the three methods as our best estimate for a universal η/π0\eta/\pi^{0} ratio for pp+pp and pp+A collisions. This is achieved by assigning every pTp_{T} value the minimum of the lower uncertainty range of the three methods as the lower bound and the maximum as the upper bound. The average of the lower and upper bound is used as central value. In the following we will use (η/π0)ppmc(\eta/\pi^{0})_{pp}^{mc} to refer to this combined result, with the superscript mcmc referring to maximal coverage of uncertainties. The result is given in Fig. 3 and compared to the mTm_{T}-scaling prediction as well as the pythia  calculation already shown in Fig. 1. One can see that all of the theoretical predictions overestimate the ratio for pTp_{T} below 3-4 GeV/cc.

IV Universality of η/π0\eta/\pi^{0} ratio systems at high pTp_{T}

In the previous section we established that the η/π0\eta/\pi^{0} ratios measured in pp+pp and pp+A collisions are consistent with being constant at high pTp_{T} with a value of R=0.487±0.024R^{\infty}=0.487\pm 0.024 (Section III.B). Here we demonstrate that all available data from pp+pp, pp+A, and A+B collisions listed in Table 1 are consistent with this RR^{\infty} value independent of the collision energy, collision system, or collisions centrality.

Table 1: References and systems quoted in this article are collected in this table. For each A+A system, if different centralities have different pTp_{T} ranges, the one of the minimum bias is presented.

For this demonstration we adopt the functional form from Eq. 3 (empirical Fit B). The parameters are fixed using the simultaneous fit to the pp+pp and pp+A data to the following values: a=1.24a=-1.24, b=0.482b=0.482, p0=4.15p_{0}=4.15, n=5.07n=5.07, and the composite parameter R/A=2.28R^{\infty}/A=2.28. The final fit parameter RR^{\infty} is determined individually for each data set using the data shuffling method. For each data set we vary the points many times within their systematic uncertainties, as discussed in Appendix B, and create an ensemble of RR^{\infty} and σR\sigma_{R^{\infty}} values. The mean of the RR^{\infty} ensemble is used as the measurement of RR^{\infty} for η/π0{\eta/\pi^{0}} and the standard deviation is quoted as the systematic uncertainty. The mean of the σR\sigma_{R^{\infty}} ensemble is quoted as the statistical uncertainty.

Fig. 4 shows the results as a function of the nucleon-nucleon center of mass energy sNN\sqrt{s_{NN}} for the minimum bias data samples of all collision systems. Also shown on the figure is the RR^{\infty} value obtained from the combined fit to the pp+pp and pp+A data sets using method B. Within uncertainties all data sets are consistent with this value and there is no evidence for a sNN\sqrt{s_{NN}} dependence of RR^{\infty}.

For most publications of η/π0{\eta/\pi^{0}} from heavy ion collisions, the data was also presented for centrality selected event classes. In order to include these in the comparison, we plot RR^{\infty} as a function of the number of produced particle dNch/dη|η=0\evaluated{dN_{ch}/d{\eta}}_{\eta=0}. The dNch/dηd{N_{ch}/{d\eta}} values used are summarized in Tab. 2.

The results are given in Fig. 5. Again all values are consistent with a universal value within uncertainties. This analysis strongly suggest that RR^{\infty} does not depend on the collision systems, sNN\sqrt{s_{NN}}, or the centrality of the collisions and that any apparent differences are likely due to systematic effects specific to individual data sets.

Refer to caption
Figure 4: Values of R=η/π0(pT)R^{\infty}=\eta/\pi^{0}(p_{T}\rightarrow\infty) as a function of sNN\sqrt{s_{NN}} for the minimum bias pp+pp, pp+A and A+B data sets. Statistical errors are shown as bars, systematic uncertainties as bands. Also shown is a band representing 0.487±0.0240.487\pm 0.024, the result of the empirical fit B to the combined pp+pp and pp+A data. Note that the A+B data at 200 GeV are offset in sNN\sqrt{s_{NN}} to avoid overlap of data sets.
Refer to caption
Figure 5: Values of R=η/π0(pT)R^{\infty}=\eta/\pi^{0}(p_{T}\rightarrow\infty) as a function of dNch/dηdN_{ch}/d\eta. The presentation is identical to Fig. 4, however, for A+B collisions results from different centrality classes are shown rather than for the minimum bias sample.
Table 2: Values for dNch/dη\differential N_{ch}/\differential\eta at mid-rapidity for all collision systems and centrality selections used in this work. For p+p collisions, the numbers correspond to the inelastic pp+pp cross section as given in [25]. For all other cases, whenever a reference is given, the values are taken directly for the publication. For PHENIX data we use data tabulated in [26]. The symbol * in the reference indicates that the value was extrapolated beyond what was tabulated. All minimum bias values (MB) that are marked by ** were calculated from the centrality selected data sets for the same system. For all data the uncertainties were calculated assuming that the values quoted in the reference are fully correlated. Reference [6] does not give an uncertainty on the multiplicity value.

V The effect of radial flow

We have shown that η/π0\eta/\pi^{0} can be described by one common function for all pp+pp and pp+A collisions over the measured pTp_{T} range from 0.1 to 20 GeV/cc. Furthermore, above pTp_{T}=5 GeV/cc the same function describes all data from heavy ion collisions. Whether this universal function also describes heavy ion data at lower pTp_{T} can not be tested due to the absence of accurate experimental data. However, there are reasons to believe that this universality does not hold at low pTp_{T}.

Evidence for strong collective motion of the bulk of the produced particles has been observed in all high energy heavy ion collision. This motion is consistent with a Hubble like hydrodynamic expansion of the collision volume, with a linear velocity profile in radial direction. In this velocity profile heavier particles gain more momentum than lighter ones. Radial flow effectively depletes the particle yields at low pTp_{T} and enhances them in an intermediate pTp_{T} range, which is determined by the mass of the particle. For pTp_{T} much larger than the particle’s mass radial flow becomes negligible. Fig. 6 shows the effect schematically by comparing π,K\pi,K, and pp spectra at RHIC energies. The spectra shown are roughly to scale and consistent with experimental data from 200 GeV Au+Au collisions. They are normalized per particle of the corresponding type at mid rapidity.

Since the η\eta meson has about the same mass as the kaon, one would expect that in the momentum range from a few hundred MeV/cc to a few GeV/cc radial flow increases the yield of η\eta mesons significantly more than that of π0\pi^{0}. This in turn would increase the η/π0\eta/\pi^{0} ratio in heavy ion collisions compared to that observed in pp+pp and pp+A collisions.

Refer to caption
Figure 6: Schematic comparison of π,K,p\pi,K,p spectra from Au+Au and p+p collisions at sNN\sqrt{s_{NN}}=200 GeV. All spectra are approximately normalize to their rapidity density at mid rapidity. Different particle types are separated by factors of 10 for clarity.

To quantify the size of the modification due to radial flow we will use a double ratio RflowR_{flow} defined as follows:

Rflow(ηπ0)Ci(ηπ0)p+p(K±π±)Ci(K±π±)p+p(RAAK±)Ci(RAAπ±)Ci,R_{flow}\equiv\frac{\quantity(\frac{\eta}{\pi^{0}})_{C_{i}}}{\quantity(\frac{\eta}{\pi^{0}})_{p+p}}\approx\frac{\quantity(\frac{K^{\pm}}{\pi^{\pm}})_{C_{i}}}{\quantity(\frac{K^{\pm}}{\pi^{\pm}})_{\text{p+p}}}\equiv\frac{\quantity(R_{AA}^{K^{\pm}})_{C_{i}}}{\quantity(R_{AA}^{\pi^{\pm}})_{C_{i}}}, (4)

where we take advantage of the fact the momentum boost from radial flow is mostly determined by the particle mass and that mK±mηm_{K^{\pm}}\approx m_{\eta}. Also charged pions are used instead of neutral pions, since π±\pi^{\pm} and kaons are typically measured simultaneously with the same detector systems and thus most systematic uncertainties on the measurement cancel in the double ratio. The subscript CiC_{i} refers to a specific collision system, energy and centrality selection.

Fig. 7 presents RflowR_{flow} for different centrality classes of Au+Au collisions at 200 GeV. The values were calculated from data published by PHENIX [29]. The data cover the pTp_{T} range from 0.5 to 2 GeV/cc and GPR is used to extrapolate somewhat beyond the measured range. According to this estimate the η/π0\eta/\pi^{0} ratio is enhanced in central collisions in a pTp_{T} region from 0.4 to 3 GeV/cc with a maximum of about 25% near 1 GeV/c. The enhancement is reduced for more peripheral collisions and nearly vanishes for the 60-92% selection.

Refer to caption
Figure 7: Double ratio, obtained by (RAAK)Ci/(RAAπ)Ci\quantity(R_{AA}^{K})_{C_{i}}/\quantity(R_{AA}^{\pi})_{C_{i}}.
Refer to caption
Figure 8: Double ratio (the flow ratio) for K±/π±K^{\pm}/\pi^{\pm} and η/π0\eta/\pi^{0} in Pb+Pb collisions at 2.76 TeV.

In Fig. 8 we depict the estimate for RflowR_{flow} for Pb+Pb data at 2.76 TeV calculated from KK and π±\pi^{\pm} data measured by ALICE [30, 24]. Shown are results for a 0-10% centrality selections. Only statistical uncertainties are shown. The flow effect is significantly larger at LHC than at RHIC: the pTp_{T} range affected is extended to 5-6 GeV/cc, it reaches its maximum at higher pTp_{T} around 3 GeV/cc, and the maximum has increased to about 50%. All indicates that radial flow effects increase with beam energy, which is consistent with a higher initial pressure and a longer lifetime of the system at the LHC compared to RHIC.

ALICE also has published η/π0\eta/\pi^{0} for Pb+Pb collisions at 2.76 TeV [24] down to 1 GeV/cc, which can be used to verify the validity of the RflowR_{flow} estimate from K/πK/\pi. For this we have divided Pb+Pb data by the universal (η/π0)ppmc(\eta/\pi^{0})^{mc}_{pp} from Fig. 3. The result is also shown in Fig. 8, error bars represent the combine uncertainty of (η/π0)ppmc(\eta/\pi^{0})^{mc}_{pp} and the statistical uncertainty of (η/π0)PbPb(\eta/\pi^{0})_{PbPb}. The ansatz that

(K±/π±)cent(K±/π±)pp(η/π0)cent(η/π0)pp\frac{(K^{\pm}/\pi^{\pm})_{cent}}{(K^{\pm}/\pi^{\pm})_{pp}}\approx\frac{(\eta/\pi^{0})_{cent}}{(\eta/\pi^{0})_{pp}}

is consistent with the data.

To construct an η/π0\eta/\pi^{0} ratio for a specific collision system and centrality selection we modify the universal shape (η/π0)ppmc(\eta/\pi^{0})^{mc}_{pp} determined from pp+pp and pp+A data (see Fig. 3 from section III) with RflowR_{flow} for the selected heavy ion sample:

(ηπ0)Ci=(ηπ0)pp×Rflow(ηπ0)pp×(K±π±)Ci(K±π±)pp.\quantity(\frac{\eta}{\pi^{0}})_{C_{i}}=\quantity(\frac{\eta}{\pi^{0}})_{pp}\times R_{flow}\approx\quantity(\frac{\eta}{\pi^{0}})_{pp}\times\frac{\quantity(\frac{K^{\pm}}{\pi^{\pm}})_{C_{i}}}{\quantity(\frac{K^{\pm}}{\pi^{\pm}})_{pp}}. (5)

Since RflowR_{flow} may be available only in a limited pTp_{T} region, for example from 0.4 to 2 GeV/cc in Fig. 7, we propose the following procedure that can be applied to any A+B collisions system if π±\pi^{\pm} and KK data are available for the pTp_{T} range affected by radial flow. In the first step we create pseudo data for η/π0\eta/\pi^{0} by multiplying RflowR_{flow} point-by-point with the (η/π0)ppmc(\eta/\pi^{0})^{mc}_{pp} up to pTcutp^{cut}_{T} where Rflow(pT)1R_{flow}(p_{T})\approx 1. This range extends to 1.4 or 4.5 GeV/c for Au+Au at 200 GeV at 60-92% centrality and Pb+Pb at 2.76 TeV, respectively. To ensure that our flow estimate has the correct asymptotic behavior we add a second set of pseudo data with constant values of η/π0=0.487±0.024\eta/\pi^{0}=0.487\pm 0.024. These are added either above 4 GeV/c where all data sets can be described by a constant (see section IV) or above 1.6×pTcut1.6\times p_{T}^{cut}, which ever is larger. The combine pseudo data are processed through a GPR to obtain a smooth curve. Finally, in order to account appropriately for the systematic uncertainties at high pTp_{T} we merge the GPR describing the flow effect with (η/π0)ppmc(\eta/\pi^{0})^{mc}_{pp} above pTcutp_{T}^{cut}. The uncertainty band at low pTp_{T} is also taken to be whichever is larger.

Refer to caption
Figure 9: Estimate of the effect of radial flow on η/π0\eta/\pi^{0} for 0-20%, 60-92% Au+Au collisions at 200 GeV and 0-10% Pb+Pb collisions at 2.76 TeV. Details are discussed in the text.
Refer to caption
Figure 10: Estimate of the effect of radial flow on η/π0\eta/\pi^{0} for 0-20%, 60-92% Au+Au collisions at 200 GeV and 0-10% Pb+Pb collisions at 2.76 TeV. Details are discussed in the text.

In Fig. 10 the construction η/π0\eta/\pi^{0} is presented step by step for three examples: 0-20%, 60-92% Au+Au at 200 GeV and 0-10% Pb+Pb at 2.76 TeV, in panels (a) to (c) respectively. The pseudo data generated are represented by points, which are then processed through a GPR resulting in the hashed green bands. They are contrasted with (η/π0)ppmc(\eta/\pi^{0})^{mc}_{pp}, the blue band, and merged with it above pTcutp_{T}^{cut} to create the final green envelope representing our η/π0\eta/\pi^{0} estimates. As discussed above the largest flow effect is observed for central Pb+Pb collisions at the LHC (panel (c)). For central Au+Au collison at RHIC (panel (a)) a much smaller effect is observed, and finally peripheral collisions of the same system are consistent with no flow effect (panel (b)).

These best estimates are compared to data in Fig. 9. For the comparison we selected data sets with similar charged particle densities, so that despite the difference in collision system, centrality or sNN\sqrt{s_{NN}} matter was created under similar conditions and evolved the same way with time. In all three cases our best estimates are consistent with the η/π0\eta/\pi^{0} data.

VI Summary Discussion

We find a universal pTp_{T} dependence of η/π0\eta/\pi^{0} for all pp+pp and pp+A collisions independent of the center of mass energy from sNN\sqrt{s_{NN}}=23 GeV to 8 TeV. We note that like originally discovered in [6], below 3 GeV/cc the universal ratio is significantly below mTm_{T} scaling extrapolations from higher pTp_{T}.

That there is no sNN\sqrt{s_{NN}} dependence is surprising as the pTp_{T} spectra of all particles vary strongly with sNN\sqrt{s_{NN}} and particle production from jet fragmentation becomes increasingly prevalent at higher energies. None-the-less there seems to be no impact on the relative yield at which η\eta and π0\pi^{0} are produced. This may hint at a largely universal hadronisation process in which hadrons are always created under the same conditions, even if the underlying mechanism is considered different, for example bulk particle production or jet fragmentation.

For heavy ion collisions, η/π0\eta/\pi^{0} has the same universal behavior at high pTp_{T}, independent of collision species, collision energy, or collision centrality. For lower pTp_{T} we find evidence for modifications of the relative particle yields due to radial flow. One might speculate that the same universal harmonization process is at work but that hadrons are produced in a moving reference frame.

We have quantified the modification of the η/π0\eta/\pi^{0} ratio due to radial flow using the double ratio RAA(K)/RAA(π)R_{AA}(K)/R_{AA}(\pi). This assumes that the change of the pTp_{T} spectra depends entirely on the particle mass, but it does not make any assumptions about the similarity of η\eta and kaon spectra themselves. We note that our approach may overestimate the modification due to flow, since kaon production or generally strange quark production is enhanced in heavy ion collisions. In our estimate the modification increases with sNN\sqrt{s_{NN}}. At 200 GeV at RHIC the maximum increase of η/π0\eta/\pi^{0} is estimated to be 25% around 1 GeV/cc, in contrast at 2.76 TeV at the LHC the maximum increase is nearly 50% and occurs at higher pTp_{T} between 2 and 3 GeV/c.

With our original motivation in mind, which was to reduce systematic uncertainties on the measurement of direct photons, we proposed a new methodology to create η/π0\eta/\pi^{0} ratios. This method is more accurate than frequently used extrapolations to lower pTp_{T} based on mTm_{T} scaling, and does not suffer from the frequent lack of statistics for η\eta measurements. Our approach can be applied to all systems for which K/π±K/\pi^{\pm} is measured in the pTp_{T} range affected by radial flow. The method does not require actual measurements of η\eta production for a given system.

We have tested this method for two specific collision systems. For Au+Au collisions at 200 GeV the deviations due to flow are found to be within ±\pm15% of the minimum bias values. Even for central collisions the η/π0\eta/\pi^{0} underlying the estimate of photon from hadron decays used in direct photon measurements [2, 3, 4] is above what we propose. As a consequence direct photon yields have been slightly under estimated, though the differences are within quoted systematic uncertainties. For central Pb+Pb collisions at 2.76 TeV the flow modifications are larger and coincidentally bring η/π0\eta/\pi^{0} much closer to the mTm_{T} scaling assumption used in the measurement of direct photons published by ALICE [5].

Acknowledgements.
We acknowledge the support from the Office of Nuclear Physics in the Office of Science of the Department of Energy.

Appendix A Gaussian Process Regression

In this section we discuss the implementation of the Gaussian Process Regression (GPR) used in our analysis. Full details about the GPR can be found in [18]. We start with a selection of NN data points xix_{i}, yiy_{i}, and σi2\sigma^{2}_{i}. In our case this is typically, NN values of log10(pT)\log 10({p_{T}}), η/π0(pT)\eta/\pi^{0}(p_{T}) and its variance. We use a Square-Exponential (SE) kernel to describe the correlation between points, which is given by:

kSE(xi,xj)=σp2exp((xixj)22l2).k_{SE}(x_{i},x_{j})=\sigma_{p}^{2}\exp(-\frac{(x_{i}-x_{j})^{2}}{2l^{2}}). (6)

Here σp\sigma_{p} gives the strength of the correlation between y values and ll is a length scale that determines the range in x over which y values are correlated.

We introduce the vectors XX and YY, which have dimension NN and elements xix_{i} and yiy_{i}, i.e. the data. The correlations between y values is then defined by a covariance matrix KxxK_{xx} which has the elements

(Kxx)ij=kSE(xi,xj)+δijσi2,\quantity(K_{xx})_{ij}=k_{SE}(x_{i},x_{j})+\delta_{ij}\sigma_{i}^{2}, (7)

with δii=1\delta_{ii}=1 and δij=0\delta_{ij}=0 for iji\neq j. The term δijσi2\delta_{ij}\sigma_{i}^{2} adds noise to the diagonal elements to account for the uncertainty on the measured y values. In order to determine σp\sigma_{p} and ll, we maximize the log likelihood function:

logp(Y|σp,l)=\displaystyle\log p(Y|\sigma_{p},l)= n2log2π12YT[Kxx]1Y\displaystyle-\frac{n}{2}\log 2\pi-\frac{1}{2}Y^{T}[K_{xx}]^{-1}Y
12logdet(Kxx).\displaystyle-\frac{1}{2}\log\det(K_{xx}). (8)

Once the parameters σp\sigma_{p} and ll are set, we can predict y values for any given x value. For this we introduce a vectors XX^{*} and YY^{*} of dimension R and elements xix^{*}_{i} for which we want to predict yiy^{*}_{i}, with R typically much larger than N. We introduce two more matrices, one of dimension R×NR\times N with elements (Kxx)ijkSE(xi,xj)(K_{x^{*}x})_{ij}\equiv k_{SE}(x^{*}_{i},x_{j}), and one of dimension R×RR\times R with elements (Kxx)ijkSE(xi,xj)(K_{x^{*}x^{*}})_{ij}\equiv k_{SE}(x^{*}_{i},x^{*}_{j}). The predicted values YY^{*} and their covariance matrix Cov(Y)\text{Cov}(Y^{*}) are then calculated as follows:

Y=Kxx[Kxx]1Y,Y^{*}=K_{x^{*}x}[K_{xx}]^{-1}Y, (9)
Cov(Y)=KxxKxx[Kxx]1KxxT.\text{Cov}(Y^{*})=K_{x^{*}x^{*}}-K_{x^{*}x}[K_{xx}]^{-1}K_{x^{*}x}^{T}. (10)

The diagonal elements of Cov(Y)\text{Cov}(Y) give the variance of YY^{*} due to the statistical uncertainty on the data YY. We refer to this as vector SstatS^{*}_{stat}. We also consider the fit uncertainty on σp\sigma_{p} and ll. The variance can be calculated by the covariance matrix MM the fitting procedure provides through error propagation of Eq. 9. :

Sfit=\displaystyle S_{fit}= (ly)2Mll+2lyσpyMlσp\displaystyle(\partial_{l}y^{*})^{2}M_{ll}+2\partial_{l}y^{*}\partial_{\sigma_{p}}y^{*}M_{l\sigma_{p}}
+(σpy)2Mσpσp,yY\displaystyle+(\partial_{\sigma_{p}}y^{*})^{2}M_{\sigma_{p}\sigma_{p}},\quad\forall y^{*}\in Y^{*} (11)

with l\partial_{l} and σf\partial_{\sigma_{f}} being the partial derivatives of YY^{*} with respect to ll and σp\sigma_{p}.

In addition, we incorporate the systematic uncertainties using the data shuffling method discussed in Appendix B. We create a large ensemble of different {Yλ}\quantity{Y^{*}_{\lambda}} for the same XX^{*} by varying each data set by a Gaussian random number ϵN(0,1)\epsilon\sim N(0,1) multiplying systematic uncertainties. The pointwise variance of ensambles {Yλ}\quantity{Y^{*}_{\lambda}}, which we call SsysS^{*}_{sys}, is used as measure of the systematic uncertainty.

In all figures that show results from the GPR the center line represents YY^{*} and the vertical width of the band is Sstat+Sfit+SSys\sqrt{S_{stat}+S_{fit}+S^{*}_{Sys}}, pointwise.

Appendix B Data-shuffling method

The data-shuffling method is a Monte Carlo simulation approach that allows to estimate the effect of systematic uncertainties on the result of a fit of a function to data. To illustrate how the method works we first consider the case of one data set and assume that the systematic uncertainties are fully correlated. Here fully correlated means that the correlation matrix is ρij=1,i,j\rho_{ij}=1,\forall i,j. Suppose each data point is described by a 4-tuple (xi,yi,σistat,σisys)(x_{i},y_{i},\sigma^{stat}_{i},\sigma^{sys}_{i}). One first defines a Gaussian random variable ϵN(0,1)\epsilon\sim N(0,1). In each simulation, one shifts each yy by a small quantity to yi=yi+σisysϵy^{\prime}_{i}=y_{i}+\sigma^{sys}_{i}\epsilon accordingly. Then in each simulation, one fits with these shifted data, and gets one fit result. This is repeated LL times, which generates LL sets of fit parameters. For each set of fit parameters one can devide the xx values into RR bins. Both LL and RR are usually large numbers. This results in a LL-by-RR matrix of yλry_{\lambda r} values. For a fixed rr, the mean and standard deviation of {yλr:1λL}\quantity{y_{\lambda r}:1\leq\lambda\leq L} are calculated. The standard deviation is assigned as systematic uncertainty of the fit for the given rr.

The method is expanded to multiple data sets by generating independent Gaussian random variables for each data set. In principle, more complex correlations of uncertainties for an individual data set can be decoded in ρij\rho_{ij}, however, for the data at hand these correlations are not known and thus can not be implemented.

One can choose as the final yy value for a given rr either the mean from data-shuffling, or the fit result of the original data (i.e., the fit result when the Gaussian variables are zero). The difference between them is usually negligible.

References