1. Introduction
Studies of strange particles produced in high-energy collisions of hadrons and heavy ions provide insight into the dynamics of collision processes. Previous measurements reported by the RHIC at BNL and SPS at CERN demonstrated increased strangeness production compared to proton–proton collisions [
1,
2]. This phenomenon is believed to be attributed to the creation of a quark–gluon medium with high energy density [
3]. The observed quantities of strange particles at various center-of-mass energies are well reproduced by thermal statistical models [
4,
5,
6,
7]. In RHIC’s gold–gold (AuAu) collisions, notable correlations were detected in the azimuthal direction among the final-state hadrons, which suggests the creation of a medium that exhibits characteristics akin to those of a nearly perfect fluid undergoing an anisotropic expansion driven by pressure [
2]. Similarly, investigations of the production and behavior of strange and light-flavored particles in heavy-ion collisions have yielded a deeper understanding of the fluid-like nature of the medium, which shows the collective behavior of the medium at the partonic level [
2,
8].
In this paper, we provide analyses of strange-particle spectra in
,
, and
collisions taken from [
9], observing their variation with event multiplicity. We focus on the spectra of
,
, and
particles, considering the charge-conjugate states for
and
particles. For the analyses, we applied both the thermodynamically consistent Tsallis distribution functions (with and without the transverse flow velocity)to extract parameters pertinent to the collective characteristics of nuclear matter. The extensive utilization of the Tsallis distribution function [
10,
11,
12,
13] within high-energy proton–proton collisions finds justification in its remarkably effective description of experimental
spectra for hadrons. This is achieved using a minimal set of parameters: the first being the effective temperature (
), the second being the non-extensivity parameter (
q), which captures deviations from the Boltzmann–Gibbs exponential distribution, and the third parameter being the fitting constant, proportionally associated with system volume. In contrast to other statistical distributions, the Tsallis distribution function stands out due to its distinctive advantage: a direct link to thermodynamics through entropy [
14]. The non-extensivity index (
q) of the Tsallis function holds significant relevance in quantifying the extent of departure of the particle’s transverse momentum distribution from the exponential Boltzmann–Gibbs distribution. Importantly, the parameter
q also acts as an indicator of non-equilibrium or the degree of non-thermalization within a system [
15]. Its significance within non-extensive statistical mechanics is reaffirmed by recent work [
16] conducted by Tsallis. This underscores the intrinsic value of the
q parameter and its pivotal role in understanding system dynamics and deviations from traditional statistical behavior.
Several adaptations of the Tsallis function have been equally successful in accurately characterizing the
distributions of end-state hadrons in proton–proton collisions, encompassing the entire range of available
values observed in experiments at both the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC) [
17,
18,
19,
20]. Various transverse flow models have been integrated with Tsallis statistics to elucidate the
distributions of hadrons in nuclear collisions at both RHIC and LHC [
21,
22,
23,
24,
25,
26,
27,
28]. We employed the Tsallis formula with and without an intrinsic flow velocity to deduce the kinetic freeze-out temperature, transverse expansion velocity, and non-extensive parameter [
13,
24,
25,
29].
2. The Method and Formalism
In this study, we conducted an analysis of experimental data gathered by the CMS collaboration [
9] from collisions involving
,
, and
at energies of 7, 5.02, and 2.76 TeV, respectively. The track selection was established based on the specific kinematic criteria of |
| < 2.4 and
> 0.4 GeV/c [
30,
31]. These criteria were chosen to ensure the precision and reliability of the track selection process. In the source paper [
9], data from pp, pPb, and PbPb collisions were sorted using
, representing the number of offline reconstructed tracks. Correction for detector and algorithm inefficiencies was performed within the kinematic region (
and
GeV) denoted by
. The analysis determined fractions of total multiplicity within
intervals and tracked the average number of corrected tracks. The tracking efficiency uncertainty was 3.9% for single tracks [
32]. For pp data, six multiplicity intervals were created, inclusive for lower bounds, matching
for minimum-bias events. PbPb and pPb data featured eight intervals, with pPb’s
not in the center-of-mass frame. In the latter case, when using the CMS frame, an insignificant difference is disregarded. Detector stability was confirmed across various multiplicities [
9]. To gain insights into the collective characteristics of strongly interacting matter, we employed the thermodynamically consistent simple Tsallis distribution function, and the one incorporating a definition for flow velocity. These functions were utilized to extract parameters that offer insights into the overall properties of the strongly interacting medium. The following simple form of the Tsallis distribution function is used to fit the data.
Here, is the transverse mass which is given by where and are the transverse momentum and rest mass of the observed particle, respectively, (Effective Temperature) reflects the average thermal motion of particles within the system. It governs how particles are distributed in terms of their transverse momentum. A higher generally corresponds to particles with higher transverse momenta and more energy. The q is the non-extensivity parameter that characterizes deviations from the standard Boltzmann–Gibbs distribution. A value of q close to 1 indicates a distribution resembling the conventional exponential distribution, while q values different from 1 signify non-extensive effects that might arise from complex interactions or correlations among particles. The symbol C is used to reflect the Normalization Constant that ensures that the probability density integrates to unity over the entire range of possible values. It helps scale the distribution function appropriately to match the experimental data.
Likewise, transverse flow is introduced into the Tsallis function given above through a transformation involving the replacement of the term
with
[
33]. This manipulation results in a functional form of the Tsallis distribution function given below.
The parameters used in the function are:
is the Average Boost Factor given by
=
,
is the average flow velocity that signifies the average speed at which particles collectively flow,
is the kinetic freeze-out Temperature denoting the temperature at which particles cease interactions resulting in the transverse momentum
of the particle constant, and
q is the non-extensivity parameter defined above. The function was originally derived in Ref. [
33] and utilized in Refs. [
33,
34], to study final particles within high-energy heavy-ion and proton–proton collisions. To achieve this, a straightforward Lorentz transformation was employed, facilitating the inclusion of transverse expansion velocity into the simple and thermodynamically consistent Tsallis functions. This approach proved successful in examining the centrality dependence of the
distributions of identified hadrons in Xe + Xe collisions at a collision energy of
= 5.44 TeV [
33] and PbPb collisions at 5.02 TeV [
34]. These specific distributions were initially recorded by the ALICE Collaboration and documented in Refs. [
35,
36].
3. Results and Discussion
In this section, we present the analyses of the transverse momentum spectra of strange hadrons through the application of Tsallis statistics. The strange particles , , and are investigated using diverse collision scenarios, including , , and collisions, each occurring at energies of 7, 5.02, and 2.76 TeV, respectively. The production of strange particles in high multiplicity pp events is one of the signatures of the existence of the parton dominant medium, i.e., QGP. Therefore, it is important to analyze the strange particles to have an insight into the strongly interacting matter. The data employed for these analyses were collected using the advanced CMS detector situated at the CERN Large Hadron Collider (LHC).
The
distributions of the particles are studied in several event-based charged-particle multiplicities. The research explores freeze-out parameters in relation to particle species and event multiplicity. The study aimed to understand how particles freezeout and how this process is connected to the produced particles in various collisions. In
Figure 1,
Figure 2 and
Figure 3, we applied scaling to the data and performed the line fitting using a scaling factor defined as scale_factor = scale_factor × N. The initial scale factor was set to 10, and ’N’ is an integer starting from 1. As a result, the data were scaled by factors such as 10, 20, 60, 240, and so forth. This approach was adopted to improve the clarity of visualizing the fit results and to prevent data overlap.
Figure 1,
Figure 2 and
Figure 3 depict
distributions of charged particles categorized by various multiplicity classes. The geometric symbols represent experimental data, while solid and dashed lines indicate fits generated by the Tsallis without and with flow, respectively.
Figure 1 shows
distributions of
,
, and
in 7 TeV
collisions;
Figure 2 illustrates the same for
collisions at 5.02 TeV; and
Figure 3 portrays the scenario in
collisions at 2.76 TeV. The overlaid lines demonstrate an excellent fit of both functions to the data. Beneath each plot, a ratio of data to fit is presented, indicating the accuracy of the function’s representation. While the solid and dashed lines distinguish the two equations in the graphs, the distinction is marked by full and hollow geometric symbols in the ratio graphs. Solid symbols represent the Tsallis function (given as Equation (
1)), while hollow symbols signify the function given as Equation (
2). The results obtained from the fitting procedure using Equations (
1) and (
2) are tabulated in six separate tables.
Table 1,
Table 2 and
Table 3 provide fitting results for
Figure 1,
Figure 2 and
Figure 3, respectively, using the Tsallis function (Equation (
1)). Correspondingly,
Table 4,
Table 5 and
Table 6 present fitting outcomes for the same figures using Equation (
2).
Figure 4 shows the behavior of the extracted parameters from the Equation (1) with varying charged particle event multiplicity.
Figure 5 (see below) shows the variation in
obtained by the Tsallis distribution function in relation to their respective
q parameter. The
Figure 6 shows the behavior of the extracted parameters from the Equation (
2) Tsallis models, with varying charged particle event multiplicity. Finally,
Figure 7 shows the variation in
obtained by the Equation (
2) of the Tsallis function in relation to their respective
q parameter. Different colors and geometric symbols are used to represent different strange particles. Errors have also been drawn in all figures for both the quantities laying along both axes. In cases where one or even both error bars are not visible in the graphs, it means that the errors in the parameters are so small that they become comparable to the size of the data point.
Figure 4a,c,e and
Figure 6a,d,g show the increasing behavior of the
and
, respectively, in three collision systems,
at 7 TeV,
at 5.02 TeV and
at 2.76 TeV, with increasing multiplicity. This is because of the reason that higher multiplicities are associated with a highly energetic collision, which results in greater temperature. The same plots also show that heavier particles have greater
and
than the lighter particles which proves the early decoupling of the heavier particles from the fireball. The high energetic collisions of particles produce the deconfined state of matter called QGP. Due to the pressure gradient and to attain equilibrium with the surroundings, it expands and continues to cool down as long as it expands. The heavier particles, due to their greater inertia, are not able to move with the expanding system for a longer time and are left behind from the system and hence decouple earlier from the system, while the lighter particles, due to smaller inertia are able to remain intact with the system throughout its expansion for longer time. As with the passage of time, the system (QGP) expands and cools down, therefore, the heavier particles have greater temperature being decoupled earlier and hence taking the temperature characteristic to the system at that earlier time while the lighter particles have smaller temperature being decoupled from the system later and hence taking the temperature characteristic to the system at that later time. Furthermore, there is a sharper increase in the case of heavier particles with multiplicity than the lighter one which is in accordance with the literature.
Figure 4b,d,f and
Figure 6b,e,h show the decreasing trend of the non-extensive parameter (q), in all three collision systems, which indicates that particles at higher multiplicities are closer to equilibrium. The same plots also show that heavier particles have smaller
q than lighter particles which confirms the quick equilibration of heavier particles compared to lighter particles. Heavier particles have greater temperature and smaller value of
q, where
q measures the deviation of the system from the Boltzmann–Gibbs statistic which assumes the system to be in thermal equilibrium. Therefore, smaller values of
q for heavier particles proves the quick equilibration compared to lighter particles. Again, there is a sharp decrease in
q with multiplicity as compared to the lighter one. At higher multiplicity, transverse flow velocity (
) is observed to be maximum for all collision systems in
Figure 6c,f,i. The reason behind this correlation is that higher multiplicity results from the violent collision which produces a huge pressure gradient in the collision zone, which results in the rapid expansion of the system. The zero-flow velocity for low multiplicity classes may imply that in such low multiplicity scenarios, there is a lack of collective transverse motion among the charged hadrons. In other words, the particles do not exhibit a significant expansion or flow in the transverse direction. This might suggest that the collision events in these low multiplicity classes are less central and involve fewer participating particles, leading to a different dynamic compared to high multiplicity events where transverse flow is more prominent.
One more interesting observation in the context of symmetric proton–proton (), lead–lead (), and asymmetric proton–lead () collisions pertains to the distinct behavior of parameter values for heavier baryons. Specifically, these values exhibit a more pronounced rise/fall compared to the corresponding parameter associated with the lighter meson in the and scenarios. But in the case of asymmetric collisions, the parameter values for both heavier baryons and the lighter meson follow a linear progression, maintaining equal intervals between them.
Figure 5 shows the variation in
obtained by the Tsallis distribution function, and
Figure 7 shows the variation in
obtained by the Equation (
2) of the Tsallis function in relation to their respective
q parameter. Within these figures, distinct geometrical symbols and colors are utilized to represent different multiplicity segments, indicated as legends in the upper right corner of each plot. The trend showcased in these plots reveals that an increase in the
q parameter corresponds to a decrease in both
and
. This observation further leads to the inference that heavier particles exhibit smaller
q values, accompanied by larger
and
values. This pattern lends support to the notion of attaining fast equilibrium and thermal state among the more massive particles. The symmetry and asymmetry observation highlighted earlier is underscored by a distinct and more pronounced distinction observed in the correlation between
and
q in asymmetric proton–lead (
) collisions compared to symmetric proton–proton (
) and lead–lead (
) collisions.
To provide a comprehensive comparison with existing literature, it is important to note that the study conducted by Murad Badshah and colleagues [
13] extensively utilized data from RHIC [
37,
38,
39,
40], ALICE [
41,
42,
43], and CMS [
44]. Their primary objective was to investigate the centrality dependence of
spectra for identified charged hadrons at both 200 GeV and 2.76 TeV. Their results demonstrated a clear trend where both temperature (T) and the average transverse flow velocity (
) increased as centrality increased. These findings are notably consistent with the observations reported by Olimov et al. [
33,
34,
45]. In our own research, we have observed a similar pattern, with these parameters showing an increase in response to growing particle multiplicity.
In the literature, there are three distinct scenarios: (i) Single Freeze-Out Scenario: in this scenario, particles decouple from the system at the same time or temperature [
46]. (ii) Triple Freeze-Out Scenario: this scenario involves three distinct decoupling times or temperatures for identified particles, single strange particles, and multi-strange particles [
47]. (iii) Multi-Freeze-Out Scenario: In this scenario, each particle decouples from the system at different times or temperatures. Extensive references to this scenario can be found in the literature [
13,
48], among others. In the case of simultaneous fitting (in single or even triple freeze-out scenarios), the combined properties such as temperature, flow velocity, and non-extensivity parameters are determined for groups of different particle species. However, this approach does not provide insight into the properties of each individual particle within the group. This can be misleading and lead to a loss of information for each individual particle. Conversely, in the separate fit (multi-freeze-out scenario), the focus is on the properties and interaction mechanisms of each individual particle. This approach minimizes the chances of losing valuable information for each particle.