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Advances in High Energy Physics
Volume 2016 (2016), Article ID 6848197, 5 pages
Research Article

Study of Void Probability Scaling of Singly Charged Particles Produced in Ultrarelativistic Nuclear Collision in Fractal Scenario

Deepa Ghosh Research Foundation, Kolkata 700031, India

Received 27 May 2016; Revised 18 July 2016; Accepted 31 July 2016

Academic Editor: Edward Sarkisyan-Grinbaum

Copyright © 2016 Susmita Bhaduri and Dipak Ghosh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The publication of this article was funded by SCOAP3.


We study the fractality of void probability distribution measured in -Ag/Br interaction at an incident energy of 200 GeV per nucleon. A radically different and rigorous method called Visibility Graph analysis is used. This method is shown to reveal a strong scaling character of void probability distribution in all pseudorapidity regions. The scaling exponent, called the Power of the Scale-Freeness in Visibility Graph (PSVG), a quantitative parameter related to Hurst exponent, is strongly found to be dependent on the rapidity window size.

1. Introduction

All through the recent decades, large density fluctuations in high energy interactions have got much consideration because of their imminent capacity to throw some light on the mechanism of multiparticle production. The presence of nonstatistical fluctuations in high energy interactions has been confirmed by analyzing the fluctuations of intermittent type in both fragmentation and particle production regions [13]. Also the correlation between the emission process of produced particles and slow target fragments has been established [2, 3]. Analysis of nonstatistical fluctuations was believed to provide some information on the dynamics of the particle production process, so multiple methodologies have been developed to analyze nonstatistical fluctuations. Bialas and Peschanski [4] first introduced intermittency, for analyzing large fluctuations. Scaled factorial moments of different orders showed a power-law dependence on the phase space interval size in different high energy interactions. This was indicative of self-similarity in this type of fluctuation. Sarkisyan [5] has studied multiplicity distribution of the produced hadrons in high energy interaction to describe high-order genuine correlations [6]. In this work although the parametrization fits well the measured fluctuations and correlations for low orders, they do show certain deviations at high orders. The necessity to incorporate the multiparticle character of the correlations along with the property of self-similarity to attain a good description of the measurements is established [5]. As in geometrical and statistical systems the presence of self-similarity is characteristic of fractal behavior, it has been deduced that multiparticle production process might also possess fractal characteristics and that there might be a correlation between intermittency and fractality [2, 3].

As per Hwa and Zhang [7] voids in the hadronization process are defined as region with no hadrons separating the bunches of hadrons that are formed during the complete course of hadronization process in a QGP system. The regions in the hadronization process may be with high or low hadron density and no hadrons. A region without hadrons or a void is composed of quarks and gluons in a state of deconfinement in a particular time scale and they are likely to convert to hadrons a bit later. As suggested by Hwa and Zhang [7] the system is divided into the states of confinement and deconfinement at the critical point, so voids exist at critical point. This concurrence of the two states at critical point is the cause of various significant behaviors of critical phenomena, so if, in a heavy-ion collision, there is any phase transition, hadrons will not be produced evenly in time and space giving rise to different scaling behaviors of void distribution. Hadronic clusters in space might be tough to quantify, whereas voids are comparatively easy to define [7]. To gain some physical understanding for the fluctuation pattern in particle production around the critical point, it is useful to analyze the production and the distribution of voids around the critical point in rapidity space in hadronic collision process. As rapidity of the particle produced can be determined accurately, we can calculate the rapidity gaps between the pairs of adjacent particles which are essentially one-dimensional representation of voids. The detailed properties of these gaps can be found in [7].

In the recent past, several studies have been done using techniques based on the fractal theory in the field of multiparticle emission [812]. Hwa (Gq moment) [8] and Takagi (Tq moment) [12] have developed the most popular of them. We have extensively applied both of these methods to analyze the multipion emission process, considering their merits and pitfalls [1317]. Thereafter methodologies like Detrended Fluctuation Analysis (DFA) method [18] has been used for determining monofractal scaling exponents and for recognizing long-range correlations in noisy and nonstationary time series data [19, 20]. DFA method has been extended by Kantelhardt et al. [21] to analyze nonstationary and multifractal time series. This generalized DFA is termed as the multifractal DFA (MF-DFA) method.

The DFA function obeys a power-law relationship with the scale parameter of a time series, if it is long-range correlated. If the DFA function of the time series is, say, denoted by , it will vary with a power of its scale parameter, say denoted by , by following the equation . The exponent in this equation is termed as Hurst exponent. is related to the fractal dimension denoted by , as [22]. Then DFA method is extended to MF-DFA technique [21] which is used for this kind of analysis for its highest precision in the scaling analysis. Hurst exponent and MF-DFA parameter are used widely in nonlinear, nonstationary analysis and they have accomplished the identification of long-range correlations for different time series. Zhang et al. [23] applied MF-DFA method to analyze the multifractal structure of the distribution of shower tracks around central rapidity region of Au-Au collisions at AGeV. Multifractal analysis in particle production process has been done in various works of recent times [2426]. But the DFA method is constrained by the requirement of an infinite number of data points for the time series to give most accurate value of the Hurst exponent whereas, in a real-life scenario, for most of the times, getting infinite number of data points is not possible. So we have to use finite time series for calculation of the Hurst exponent. In this process the long-range correlations in the time series are broken into parts with finite number of data points and consequently the local dynamics relating to a specific temporal window are evidently amplified and diverge from their correct form.

In this regard more accurate result may be obtained with an entirely different, rigorous method, namely, the Visibility Graph Analysis [27, 28]. Lacasa et al. have used fractional Brownian motion (fBm) and fractional Gaussian noises (fGn) series as a theoretical framework to study real-time series in various scientific fields. Due to the inherent nonstationarity and long-range dependence in fBm, the Hurst parameter, calculated for it with various methods, often yields ambiguous results. Lacasa et al. showed how classic method of complex network analysis can be applied to quantify long-range dependence and fractality of a time series [28]. They mapped fBm and fGn series into a scale-free visibility graph having the degree distribution as a function of the Hurst exponent [28]. Visibility Graph Analysis is altogether a new concept for assessing fractality from a new perspective without assessing multifractality. This method has recently been applied widely over time series with finite number of data points, even with 400 data points [29], and has achieved reliable result in various fields of science. In our recent works we have applied Visibility Graph Analysis productively for analyzing various biological signals [30, 31]. Zebende et al. have studied long-range correlations because of temperature-driven liquid to vapor phase transition in distilled water, using DFA method, and have established that, with the temperature approaching transition temperature, the scaling exponent increases [32]. Recently Zhao et al. have confirmed that Hurst exponent may be a good indicator of phase transition for a complex system [33]. We also know, from their analysis on magnetization time series with Hurst exponent, that as the system approaches phase transition the fractal behavior of the system transforms from mono- to multifractal. Zhao et al. have applied visibility network analysis to confirm the fractality of the same time series, from complex network perspective [33], and reported that remarkable increase or decrease of the topological behavior can be used to identify the onset of phase transition. Lacasa et al. have shown that the Visibility Graph Analysis method is appropriate for distinguishing correlation and geometrical structure of a time series and have established a connection between the Hurst exponent and the Power of Scale-Freeness in Visibility Graph (PSVG) [28].

In case of high energy interactions, void occurrence probability can be defined as the probability of occurring events with zero number of particles in a particular region of phase space. So far no work has been reported on scaling analysis of void probability distribution of pions with respect to fractal and multifractal methodology. We have analyzed void probability distribution data for different windows of pseudorapidity around the central rapidity region. In this work we have attempted to analyze the scaling behavior of voids in multipion production from a completely new perspective of Visibility Graph Analysis as well as to explore phase transition, if any, in 32S-Ag/Br interaction at an incident energy of 200 GeV per nucleon. We have analyzed the data in different overlapping windows, ranging from central rapidity region to the full phase space.

The rest of the paper is organized as follows. The method of Visibility Graph technique is presented in Section 2. The details of data, our analysis, and the inferences from the test results are given in Section 3. The paper is concluded in Section 4.

2. Method of Analysis

As discussed earlier the Visibility Graph technique can be broadly used over finite time series dataset and can produce reliable results in several domains. The simple method converts a fractal time series into a scale-free graph, and its structure is related to a self-similar fractal nature and complexity of the time series [27]. In this technique each node of the scale-free graph which is also called the visibility graph represents a time sample of the time series, and an edge between two nodes shows the corresponding time samples that can view each other.

2.1. Visibility Graph Algorithm

The algorithm is a one-to-one mapping from the domain of time series to its Visibility Graph. Let be the point of the time series. and are the two vertices (nodes) of the graph that are connected via a bidirectional edge if and only if the following equation is valid, where and : As shown in Figure 1, and can see each other, if the above is satisfied. With this logic two sequential points of the time series can always see each other; hence all sequential nodes are connected together. The time series should be converted to positive planes as the above algorithm is valid only for positive values in the time series.

Figure 1: Visibility Graph for time series .
2.1.1. Power of Scale-Freeness of VG-PSVG

As per the graph theory, the definition of the degree of a node is the number of connections or edges that the node has with other nodes. The degree distribution, say , of a network formed from the time series is defined as the fraction of nodes with degree in the network. Thus if there are nodes in total in a network and of them have degree , then .

The scale-freeness property of Visibility Graph states that the degree distribution of its nodes satisfies a power law, that is, , where is a constant and it is known as Power of the Scale-Freeness in Visibility Graph (PSVG), which is denoted by and is calculated as the gradient of versus plot. corresponds to the amount of complexity and fractal nature of the time series indicating the fractal dimension of the signal [27, 28, 34]. It is also proved that there exists a linear relationship between and of the associated time series [28].

3. Experimental Details

3.1. Data Description

For this experiment, the data were acquired by exposing Illford G5 emulsion plates to a 32S-beam of 200 GeV incident energy per nucleon, from CERN. The details of the data including scanning, measurement, and resolution were given in some of our previous works.

In this experiment, the emission angle, , and azimuthal angle, , are measured for each track with respect to the beam directions. The readings are taken for the coordinates   of the interaction point, coordinates   of the end of the linear portion of every secondary track, and coordinates   of a point on the incident beam. The coordinates are measured by semiautomatic measurement system with precision of 1 μm along -axis and -axis. The corresponding precision in -direction is 0.5 μm. In spite of its several limitations, nuclear emulsion experiments are superior to many experiments because they offer a very high angular resolution (around 1 mrad). This advantage is relevant where the distribution of particles is confined into a small phase space region. As detector it also has the ability to register all the charged particles produced or emitted in the -geometry space.

3.2. Our Method of Analysis

To analyze the void probability distribution data of produced pions, we have divided the overall -distribution of singly charged particles produced in 32S-Ag/Br interaction at 200 AGeV into overlapping pseudorapidity (denoted by ) windows around the central rapidity region. Below are the details of the analysis.(i)Eight pseudorapidity windows (denoted by ) of pseudorapidity values around the central rapidity regions were obtained. Hence -values in each dataset range from () to (), where is the central rapidity for the whole dataset. ranges from which is the narrowest region around central rapidity, to the full phase space, .(ii)Each dataset of -values are sorted in ascending order, and this way series of data points are formed. Then for each data series gaps or voids between the -values are noted. Then, starting from minimum -value to maximum -values, the series is divided into a number of windows with a specific width. Then, for each window, we calculate the number of voids and divide it by the total number of voids for the whole data series. Thus we get the void probability distribution for the particular data series. Each dataset of void probability distribution contains number of points equal to the number of windows that could be made from the data series of -values. Visibility Graph Analysis is done on these datasets of void probability distribution.(iii)Then Visibility Graph is constructed following the method in Section 2, for each of the datasets (consisting of void probability values) generated out of overlapping rapidity intervals around the central rapidity region. For each Visibility Graph the values of versus are calculated. versus plot dataset for void probability distribution for one of the overlapping windows of -values for S-Ag/Br interaction is shown in Figure 2, and the power-law relationship is obvious here.(iv)As per the method described in Section 2, is deduced from the slope of versus for each of the -versus- datasets.Figure 3 shows the plot of versus for the same dataset for which the -versus- plot is shown. It is evident from Figure 3 that the value of , calculated for this dataset, is .(v)Table 1 lists -values (along with the statistical errors), denoted by , for sets of void probability dataset for the 32S-Ag/Br interaction. Similar analysis using randomized data is also included. Further for the sake of comparison we have included another Table 2 where similar analysis was performed on pseudorapidity () values.

Table 1: Values of for experiment data and for randomized data, calculated for void probability distribution for different -range around the central rapidity regions.
Table 2: Values of calculated for multipion production in S-Ag/Br interactions, -range wise around the central rapidity regions.
Figure 2: versus plot for the Visibility Graph created for a test dataset of void probability distribution.
Figure 3: Slope of versus for the Visibility Graph created for the same dataset in Figure 2.

4. Conclusion

(i)This analysis with chaos-based complex network analysis clearly manifests that the void probability distribution in multiparticle production in relativistic nuclear collision obeys scaling behavior.(ii)Further compared to randomized data it is observed that this scaling behavior depends on the rapidity window size in and around the central pseudorapidity region, although the dependency becomes weak when statistical errors are considered. However the overall picture speaks in favor of interval size dependence of void probability distribution which is a new finding obtained from a rigorous, complex, and nonlinear technique.(iii)This sort of analysis has a potential for assessment of approaching criticality for phase transition in terms of PSVG values, as also discussed in [32, 33].

Competing Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.


The authors thank the Department of Higher Education, Government of West Bengal, India, for logistics support of computational analysis.


  1. A. Bialas and B. Ziaja, “Intermittency in a single event,” Physics Letters B, vol. 378, no. 1–4, pp. 319–322, 1996. View at Google Scholar · View at Scopus
  2. W. Kittel and E. A. De Wolf, Soft Multihadron Dynamics, World Scientific, River Edge, NJ, USA, 2005. View at Publisher · View at Google Scholar
  3. E. A. De Wolf, I. M. Dremin, and W. Kittel, “Scaling laws for density correlations and fluctuations in multiparticle dynamics,” Physics Report, vol. 270, no. 3, pp. 1–141, 1996. View at Publisher · View at Google Scholar · View at Scopus
  4. A. Bialas and R. Peschanski, “Moments of rapidity distributions as a measure of short-range fluctuations in high-energy collisions,” Nuclear Physics B, vol. 273, no. 3-4, pp. 703–718, 1986. View at Publisher · View at Google Scholar · View at Scopus
  5. E. K. G. Sarkisyan, “Description of local multiplicity fluctuations and genuine multiparticle correlations,” Physics Letters B, vol. 477, no. 1–3, pp. 1–12, 2000. View at Publisher · View at Google Scholar · View at Scopus
  6. G. Abbiendi, K. Ackerstaff, G. Alexander et al., “Intermittency and correlations in hadronic Z0 decays,” The European Physical Journal C, vol. 11, no. 2, pp. 239–250, 1999. View at Publisher · View at Google Scholar
  7. R. C. Hwa and Q.-H. Zhang, “Erraticity of rapidity gaps,” Physical Review D, vol. 62, no. 1, Article ID 014003, pp. 1–6, 2000. View at Google Scholar · View at Scopus
  8. R. C. Hwa, “Fractal measures in multiparticle production,” Physical Review D, vol. 41, no. 5, pp. 1456–1462, 1990. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Paladin and A. Vulpiani, “Anomalous scaling laws in multifractal objects,” Physics Reports, vol. 156, no. 4, pp. 147–225, 1987. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. P. Grassberger and I. Procaccia, “Dimensions and entropies of strange attractors from a fluctuating dynamics approach,” Physica D. Nonlinear Phenomena, vol. 13, no. 1-2, pp. 34–54, 1984. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  11. T. C. Halsey, M. H. Jensen, L. P. Kadanoff, I. Procaccia, and B. I. Shraiman, “Fractal measures and their singularities: the characterization of strange sets,” Physical Review A, vol. 33, no. 2, pp. 1141–1151, 1986. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  12. F. Takagi, “Multifractal structure of multiplicity distributions in particle collisions at high energies,” Physical Review Letters, vol. 72, no. 1, pp. 32–35, 1994. View at Publisher · View at Google Scholar · View at Scopus
  13. D. Ghosh, M. Lahiri, A. Deb et al., “Factorial correlator study in Ag32/Br interaction at 200A GeV,” Physical Review C, vol. 52, no. 4, pp. 2092–2096, 1995. View at Publisher · View at Google Scholar · View at Scopus
  14. D. Ghosh, A. Deb, M. Mondal, and J. Ghosh, “Erraticity analysis of multipion data in 32S-AgBr interactions at 200A GeV,” Physical Review C, vol. 68, no. 2, Article ID 024908, 2003. View at Publisher · View at Google Scholar
  15. D. Ghosh, A. Deb, M. Mondal, and J. Ghosh, “Analysis of fluctuation of fluctuations in 32S-AgBr interactions at 200 AGeV,” Physics Letters, Section B: Nuclear, Elementary Particle and High-Energy Physics, vol. 540, no. 1-2, pp. 52–61, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. D. Ghosh, A. Deb, M. Mondal, and J. Ghosh, “Multiplicity-dependent chaotic pionization in ultra-relativistic nuclear interactions,” Journal of Physics G: Nuclear and Particle Physics, vol. 29, no. 9, pp. 2087–2098, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. D. Ghosh, A. Deb, and M. Lahiri, “Factorial and fractal analysis of the multipion production process at 350 GeV/c,” Physical Review D, vol. 51, no. 7, pp. 3298–3304, 1995. View at Publisher · View at Google Scholar · View at Scopus
  18. C.-K. Peng, S. V. Buldyrev, S. Havlin, M. Simons, H. E. Stanley, and A. L. Goldberger, “Mosaic organization of DNA nucleotides,” Physical Review E, vol. 49, no. 2, pp. 1685–1689, 1994. View at Publisher · View at Google Scholar · View at Scopus
  19. M. S. Taqqu and V. Teverovsky, “Estimations for long-range dependence: an empirical study,” Fractals, vol. 3, no. 4, pp. 785–788, 1995. View at Publisher · View at Google Scholar
  20. Z. Chen, P. C. H. Ivanov, K. Hu, and H. E. Stanley, “Effect of nonstationarities on detrended uctuation analysis,” Physical Review E, vol. 65, no. 4, part 1, Article ID 041107, 2002. View at Publisher · View at Google Scholar
  21. J. W. Kantelhardt, S. A. Zschiegner, E. Koscielny-Bunde, S. Havlin, A. Bunde, and H. E. Stanley, “Multifractal detrended fluctuation analysis of nonstationary time series,” Physica A: Statistical Mechanics and its Applications, vol. 316, no. 1–4, pp. 87–114, 2002. View at Publisher · View at Google Scholar · View at Scopus
  22. J. W. Kantelhardta, E. Koscielny-Bundea, H. H. A. Rego, S. Havlinb, and A. Bundea, “Detecting longrange correlations with detrended uctuation analysis,” Physica A, vol. 295, no. 3, pp. 441–454, 2001. View at Google Scholar
  23. Y. X. Zhang, W. Y. Qian, and C. B. Yang, “Multifractal structure of pseudorapidity and azimuthal distributions of the shower particles in Au+Au collisions at 200 A GeV,” International Journal of Modern Physics A, vol. 23, no. 18, pp. 2809–2816, 2008. View at Publisher · View at Google Scholar · View at Scopus
  24. C. Albajar, O. C. Allkofer, R. J. Apsimon et al., “Multifractal analysis of minimum bias events in s=630 GeVanti-p p collisions,” Zeitschrift für Physik C Particles and Fields, vol. 56, no. 1, pp. 37–46, 1992. View at Publisher · View at Google Scholar
  25. M. K. Suleymanov, M. Sumbera, and I. Zborovsky, “Entropy and multifractal analysis of multiplicity distributions from pp simulated events up to LHC energies,”
  26. E. G. Ferreiro and C. Pajares, “High multiplicity pp events and J/ψ production at energies available at the CERN large hadron collider,” Physical Review C, vol. 86, Article ID 034903, 2012. View at Publisher · View at Google Scholar
  27. L. Lacasa, B. Luque, F. Ballesteros, J. Luque, and J. C. Nuño, “From time series to complex networks: the visibility graph,” Proceedings of the National Academy of Sciences of the United States of America, vol. 105, no. 13, pp. 4972–4975, 2008. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  28. L. Lacasa, B. Luque, J. Luque, and J. C. Nuño, “The visibility graph: a new method for estimating the hurst exponent of fractional brownian motion,” Europhysics Letters, vol. 86, no. 3, Article ID 30001, 2009. View at Publisher · View at Google Scholar · View at Scopus
  29. S. Jiang, C. Bian, X. Ning, and Q. D. Y. Ma, “Visibility graph analysis on heartbeat dynamics of meditation training,” Applied Physics Letters, vol. 102, no. 25, Article ID 253702, 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. S. Bhaduri and D. Ghosh, “Electroencephalographic data analysis with visibility graph technique for quantitative assessment of brain dysfunction,” Clinical EEG and Neuroscience, vol. 46, no. 3, pp. 218–223, 2015. View at Publisher · View at Google Scholar · View at Scopus
  31. A. Bhaduri and D. Ghosh, “Quantitative assessment of heart rate dynamics during meditation: an ECG Based study with multi-fractality and visibility graph,” Frontiers in Physiology, vol. 7, article 44, 2016. View at Publisher · View at Google Scholar
  32. G. F. Zebende, M. V. S. Da Silva, A. C. P. Rosa Jr., A. S. Alves, J. C. O. De Jesus, and M. A. Moret, “Studying long-range correlations in a liquid-vapor-phase transition,” Physica A: Statistical Mechanics and Its Applications, vol. 342, no. 1-2, pp. 322–328, 2004. View at Publisher · View at Google Scholar · View at Scopus
  33. L. Zhao, W. Li, C. Yang et al., “Multifractal and network analysis of phase transition,”
  34. M. Ahmadlou, H. Adeli, and A. Adeli, “Improved visibility graph fractality with application for the diagnosis of Autism Spectrum Disorder,” Physica A: Statistical Mechanics and Its Applications, vol. 391, no. 20, pp. 4720–4726, 2012. View at Publisher · View at Google Scholar · View at Scopus