pith. sign in

arxiv: 2605.16149 · v1 · pith:G2UK6BAQnew · submitted 2026-05-15 · ⚛️ physics.plasm-ph · cond-mat.stat-mech

Beyond Maxwell-Boltzmann: Transport in Quasiequilibrium Plasmas

Pith reviewed 2026-05-19 18:47 UTC · model grok-4.3

classification ⚛️ physics.plasm-ph cond-mat.stat-mech
keywords plasma transportsuperstatisticsnon-Maxwellian distributionssolar wind electronsquasiequilibrium stateselectrical conductivityviscositysuprathermal tails
0
0 comments X

The pith

Quasiequilibrium plasmas show systematically larger transport coefficients than Maxwellian plasmas because of extra energetic particles in the distribution tails.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Space plasmas often deviate from Maxwell-Boltzmann statistics through suprathermal populations that arise in quasiequilibrium states. These states are modeled by expressing the overall distribution as a continuous superposition of local Maxwellians, known as superstatistics. The paper derives the resulting macroscopic transport relations and finds that electric conductivity, thermal conductivity, mobility, diffusion, and both shear and bulk viscosity all increase relative to their Maxwellian values. The enhancement is quantified across the three main superstatistical universality classes after the distributions are matched to solar-wind electron data. A sympathetic reader cares because most existing plasma models rely on Maxwellian assumptions; if the enhancement holds, those models under-predict fluxes and stresses in natural and laboratory plasmas.

Core claim

When the velocity distribution of a plasma is written as a continuous superposition of Maxwellians according to the superstatistics framework, the transport coefficients that link fluxes to driving forces become larger than the corresponding Maxwell-Boltzmann expressions. The increase arises because the superposition populates the high-energy tails more heavily. After verifying that the resulting distributions reproduce observed solar-wind electron spectra, the paper computes the electric and thermal conductivities, mobility, diffusion coefficient, and the shear and bulk viscosities, then reports the numerical enhancement for the three principal universality classes of superstatistics.

What carries the argument

Superstatistics, the representation of a quasiequilibrium distribution as a continuous superposition of local Maxwellian distributions whose parameters fluctuate according to a weighting function.

If this is right

  • Electric and thermal conductivities rise above their Maxwellian values for any of the three main superstatistical classes.
  • Mobility and diffusion coefficients are likewise enlarged by the same tail population effect.
  • Shear and bulk viscosity coefficients increase, altering momentum transport in flowing quasiequilibrium plasmas.
  • The size of the enhancement scales with the width of the superstatistical weighting function and is therefore largest for the classes that produce the heaviest tails.
  • Existing fluid or kinetic models that assume a single Maxwellian will under-estimate all these coefficients when applied to solar-wind or similar plasmas.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Models of heat flux in the solar corona or solar wind that currently use Maxwellian closures could be revised upward, potentially changing predicted temperature profiles.
  • The same superstatistical correction might be tested in magnetically confined laboratory plasmas whose measured distributions show similar tails.
  • Extensions to other transport processes such as momentum transfer in shocks or particle acceleration rates would follow the same pattern of enhancement.
  • If the superstatistical weighting function itself evolves with time or position, the transport coefficients would become spatially or temporally variable even at fixed average density and temperature.

Load-bearing premise

The observed non-Maxwellian electron distributions in the solar wind can be accurately expressed as a continuous superposition of Maxwellian distributions.

What would settle it

A laboratory or in-situ measurement that extracts the electric conductivity or shear viscosity from a plasma whose velocity distribution is independently confirmed to be one of the three standard superstatistical forms, then compares the measured value directly against the Maxwellian prediction for the same average temperature and density.

Figures

Figures reproduced from arXiv: 2605.16149 by Kamel Ourabah.

Figure 1
Figure 1. Figure 1: The system undergoes four successive stages: an [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The top panels show the three universality classes [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Electron velocity distribution functions in the solar wind: (a) data from Feldman [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Electric conductivity ratio σ (q) /σ as a function of q := ⟨β 2 ⟩/β2 0 for the three universality classes of superstatis￾tics. is the standard Maxwellian-based electric conductivity [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Thermoelectric coefficient ratio α (q) /α as a function of q for the three universality classes of superstatistics. Comparing Eq. (35) with Eq. (22) under E = 0, and using σ (q) from Eq. (30), the thermoelectric coefficient for the three universality classes reads    α (q) 1 = 2 7 − 5q α (q < 7/5), α (q) 2 = 7q − 5 2q α, α (q) 3 = q 5/2 α, (36) where α = − 5 2e (37) is the standard Maxwellia… view at source ↗
Figure 6
Figure 6. Figure 6: Thermal conductivity ratio λ (q) /λ as a function of q := ⟨β 2 ⟩/β2 0 , for the three universality classes of superstatis￾tics [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: shows the normalized diffusion coefficient D(q)/D as a function of q for the three universality classes. Temperature fluctuations clearly enhance parti￾cle diffusion, following the same trend observed for other transport coefficients: the χ 2 class produces the strongest increase, followed by the log-normal class, and finally the inverse-χ 2 class. This enhancement arises from the heavy tails of the distri… view at source ↗
Figure 8
Figure 8. Figure 8: shows this common ratio as a function of q := ⟨β 2 ⟩/β2 0 for the three universality classes. In all cases, the ratio is larger than unity, showing that superstatistical ef￾fects enhance both shear and bulk viscosities relative to the Maxwellian case. This enhancement reflects the high￾energy overpopulation induced by the non-Maxwellian tails, which increases momentum transport across neigh￾boring fluid la… view at source ↗
read the original abstract

Space plasmas are generally characterized by non-Maxwellian distributions with suprathermal populations, as routinely revealed by in situ observations. Such departures from standard Maxwellian distributions can be understood as signatures of quasiequilibrium states, in which the distribution of the medium can be expressed as a continuous superposition of Maxwellian distributions, namely through superstatistics. Here, we construct macroscopic relations linking fluxes to their associated driving forces in such plasmas, where superstatistical effects enter the picture through the transport coefficients. After comparing the resulting superstatistical distributions with observed electron distributions in the solar wind, we turn to the kinetic response of quasiequilibrium plasmas and derive the corresponding transport coefficients, including the electric and thermal conductivities, the mobility, and the diffusion coefficient. We further extend the analysis to viscous plasmas and compute the shear and bulk viscosity coefficients. Overall, quasiequilibrium effects are found to systematically enhance the transport coefficients relative to their Maxwellian values. We quantify this enhancement for the three main universality classes of superstatistics, which are the most commonly encountered in experimental and observational situations, and interpret it as a consequence of the increased population of energetic particles in the non-Maxwellian tails.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 1 minor

Summary. The manuscript develops a framework for transport in quasiequilibrium plasmas by modeling non-Maxwellian distributions as continuous superpositions of Maxwellians via superstatistics. It derives flux-force relations with modified transport coefficients (electric/thermal conductivity, mobility, diffusion, shear/bulk viscosity), shows systematic enhancement relative to Maxwellian values, quantifies the effect across the three main universality classes, and compares the resulting distributions to solar wind electron observations.

Significance. If the derivations hold, the work supplies a parameterized route to include suprathermal-tail effects in plasma transport calculations without full kinetic simulation. Explicit results for the three universality classes and the observational comparison constitute concrete strengths that could be useful for space-plasma modeling.

major comments (1)
  1. [Section on the kinetic response of quasiequilibrium plasmas] Section on the kinetic response of quasiequilibrium plasmas: the transport coefficients appear to be obtained by weighting the standard Maxwellian expressions with the superstatistical parameter distribution f(β) or f(σ). This procedure assumes the driving force and collision operator commute with the superposition; a proper linear-response treatment would instead solve the perturbed kinetic equation once on the composite f(v). The discrepancy is expected to be largest in the suprathermal tails that drive the claimed enhancement, so the central result is load-bearing on this point.
minor comments (1)
  1. [Abstract] The abstract states that the superstatistical distributions are compared with solar wind data but provides no quantitative fit metrics (e.g., parameter values or goodness-of-fit measures), which would help readers assess the practical applicability.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful reading of the manuscript and for identifying an important subtlety in the linear-response derivation. We address the major comment below and will revise the manuscript accordingly.

read point-by-point responses
  1. Referee: Section on the kinetic response of quasiequilibrium plasmas: the transport coefficients appear to be obtained by weighting the standard Maxwellian expressions with the superstatistical parameter distribution f(β) or f(σ). This procedure assumes the driving force and collision operator commute with the superposition; a proper linear-response treatment would instead solve the perturbed kinetic equation once on the composite f(v). The discrepancy is expected to be largest in the suprathermal tails that drive the claimed enhancement, so the central result is load-bearing on this point.

    Authors: We thank the referee for highlighting this point. Our derivation proceeds from the physical premise that a quasiequilibrium plasma is a continuous superposition of Maxwellian subpopulations, each characterized by its own inverse temperature β drawn from f(β). Because the external driving forces (electric field, gradients) are identical for every subpopulation and the collision operator (taken in the relaxation-time approximation) is linear in the distribution, the flux contributed by each Maxwellian component can be computed separately and then averaged with weight f(β). This procedure is formally equivalent to solving the linearized kinetic equation for the composite distribution provided the perturbation does not induce transitions between subpopulations—an assumption that holds for sufficiently weak forces. We will revise the manuscript to state this assumption explicitly, to note that the suprathermal tails are precisely the components that receive the largest weight in the average, and to add a short paragraph comparing the present averaged result with the outcome of a direct solution of the perturbed equation on the composite f(v). revision: yes

Circularity Check

0 steps flagged

No circularity: standard kinetic derivation applied to superstatistical input distribution

full rationale

The paper takes the superstatistical distribution (continuous superposition of Maxwellians) as given input from prior literature and inserts it into conventional expressions for transport coefficients obtained from linear response or Chapman-Enskog expansion. No equation reduces the output coefficients back to a fitted parameter or to the input distribution by algebraic identity. No load-bearing self-citation chain or uniqueness theorem imported from the same author is required to close the derivation. The claimed enhancement follows directly from the heavier tails in the composite distribution and is therefore an independent consequence rather than a renaming or tautology. The derivation remains self-contained against external kinetic-theory benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the superstatistics representation of quasiequilibrium states, which is imported from prior literature rather than derived here. No new free parameters, invented entities, or ad-hoc axioms are introduced in the abstract.

axioms (1)
  • domain assumption Space plasmas are characterized by non-Maxwellian distributions that can be expressed as continuous superpositions of Maxwellian distributions via superstatistics
    Stated directly in the abstract as the basis for understanding observed suprathermal populations.

pith-pipeline@v0.9.0 · 5735 in / 1262 out tokens · 46426 ms · 2026-05-19T18:47:58.996867+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

82 extracted references · 82 canonical work pages

  1. [1]

    distribution function is decomposed into a dominant sta- tionary part and a small perturbation, f(r,v, t) =f 0(r,v) +δf(r,v, t),(15) with|δf| ≪f 0

    In the limitq→1, f(β) collapses to a Dirac delta function, and the associated velocity distribution reduces to the Maxwellian distribution. distribution function is decomposed into a dominant sta- tionary part and a small perturbation, f(r,v, t) =f 0(r,v) +δf(r,v, t),(15) with|δf| ≪f 0. In general, the collision termC(f) is a nonlinear integral operator, ...

  2. [2]

    Lutz, Anomalous diffusion and Tsallis statistics in an optical lattice, Phys

    E. Lutz, Anomalous diffusion and Tsallis statistics in an optical lattice, Phys. Rev. A.67, 051402(R) (2003)

  3. [3]

    Douglas, S

    P. Douglas, S. Bergamini, and F. Renzoni, Tunable Tsal- lis Distributions in Dissipative Optical Lattices, Phys. Rev. Lett.96, 110601 (2006)

  4. [4]

    Khachatryanet al.(CMS Collaboration), Transverse- Momentum and Pseudorapidity Distributions of Charged Hadrons inppCollisions at √s= 7 TeV, Phys

    V. Khachatryanet al.(CMS Collaboration), Transverse- Momentum and Pseudorapidity Distributions of Charged Hadrons inppCollisions at √s= 7 TeV, Phys. Rev. Lett. 105, 022002 (2010)

  5. [5]

    Adareet al.(PHENIX Collaboration), Measurement of neutral mesons inp+pcollisions at √s= 200 GeV and scaling properties of hadron production, Phys

    A. Adareet al.(PHENIX Collaboration), Measurement of neutral mesons inp+pcollisions at √s= 200 GeV and scaling properties of hadron production, Phys. Rev. D83, 052004 (2011)

  6. [6]

    Olbert, inPhysics of the Magnetosphere, Astrophysics and Space Science Library, Vol

    S. Olbert, inPhysics of the Magnetosphere, Astrophysics and Space Science Library, Vol. 10, edited by R. D. L. Carovillano and J. F. McClay (D. Reidel, Dordrecht, 1968), p. 641

  7. [7]

    V. M. Vasyliunas, A survey of low-energy electrons in the evening sector of the magnetosphere with OGO 1 and OGO 3, J. Geophys. Res.73, 2839 (1968)

  8. [8]

    Maksimovic, V

    M. Maksimovic, V. Pierrard, and P. Riley, Ulysses elec- tron distributions fitted with Kappa functions, Geophys. Res. Let.24, 1151 (1997)

  9. [9]

    I. Zouganelis, Measuring suprathermal electron param- eters in space plasmas: Implementation of the quasi- thermal noise spectroscopy with kappa distributions us- ing in situ Ulysses/URAP radio measurements in the so- lar wind, J. Geophys. Res.113, A08111 (2008)

  10. [10]

    Livadiotis and D

    G. Livadiotis and D. J. McComas, Beyond kappa distri- butions: Exploiting Tsallis statistical mechanics in space plasmas, J. Geophys. Res.114, A11105 (2009)

  11. [11]

    J. C. Carvalho, J. D. do Nascimento, Jr., R. Silva, and J. R. De Medeiros, Non-Gaussian statistics and stellar rotational velocities of main-sequence field, Astrophys. J.696, L48 (2009)

  12. [12]

    B. B. Soares and J. R. P. Silva, On the rotation of ONC stars in the Tsallis formalism context, EPL96, 19001 (2011)

  13. [13]

    A. L. B. Ribeiro, P. A. A. Lopes, and M. Trevisan, Non- Gaussian velocity distributions — the effect on virial mass estimates of galaxy groups, MNRAS413, L81 (2011)

  14. [14]

    Cur´ e, D

    M. Cur´ e, D. F. Rial, A. Christen, and J. Cassetti, A method to deconvolve stellar rotational velocities, As- tronom. Astrophys.565, A85 (2014)

  15. [15]

    Livadiotis,Kappa Distributions: Theory and Applica- 14 B tions in Plasmas(Elsevier Science, New York, 2017)

    G. Livadiotis,Kappa Distributions: Theory and Applica- 14 B tions in Plasmas(Elsevier Science, New York, 2017)

  16. [16]

    A Cairns,et al., Electrostatic solitary structures in non-thermal plasmas, Geophys

    R. A Cairns,et al., Electrostatic solitary structures in non-thermal plasmas, Geophys. Res. Lett.22, 2709 (1995)

  17. [17]

    Tsallis,Introduction to Nonextensive Statistical Me- chanics: Approaching a Complex World(Springer, New York, 2009)

    C. Tsallis,Introduction to Nonextensive Statistical Me- chanics: Approaching a Complex World(Springer, New York, 2009)

  18. [18]

    B. I. Squarer, C. Presilla, and R. Onofrio, Enhancement of fusion reactivities using non-Maxwellian energy distri- butions, Phys. Rev. E109, 025207 (2024)

  19. [19]

    Ourabah, Reaction rates in quasiequilibrium states, Phys

    K. Ourabah, Reaction rates in quasiequilibrium states, Phys. Rev. E111, 034115 (2025)

  20. [20]

    Y. Ye, W. Zhang, and B. Wan, Enhancement of the fusion reactivity due to the D-T non-Maxwellian ion distribu- tion and its impact on Lawson criterion, Phys. Plasmas 32, 092504 (2025)

  21. [21]

    Tsallis, Possible generalization of Boltzmann-Gibbs statistics, J

    C. Tsallis, Possible generalization of Boltzmann-Gibbs statistics, J. Stat. Phys.52, 479 (1988)

  22. [22]

    Kaniadakis, Statistical mechanics in the context of special relativity, Phys

    G. Kaniadakis, Statistical mechanics in the context of special relativity, Phys. Rev. E66, 056125 (2002)

  23. [23]

    R. C. Dewar, Information theory explanation of the fluc- tuation theorem, maximum entropy production and self- organized criticality in non-equilibrium stationary states, J. Phys. A36, 631 (2003)

  24. [25]

    B. D. Shizgal, Kappa and other nonequilibrium distribu- tions from the Fokker-Planck equation and the relation- ship to Tsallis entropy, Phys. Rev. E97, 052144 (2018)

  25. [26]

    A. D. Oylukan and B. D. Shizgal, Nonequilibrium distri- butions from the Fokker-Planck equation: Kappa distri- butions and Tsallis entropy, Phys. Rev. E108, 014111 (2023)

  26. [27]

    R. J. Ewartet al., PNAS122, 2417813122 (2025)

  27. [28]

    Beck and E

    C. Beck and E. G. D. Cohen, Superstatistics, Physica A 322, 267 (2003)

  28. [29]

    Beck, Statistics of Three-Dimensional Lagrangian Turbulence, Phys

    C. Beck, Statistics of Three-Dimensional Lagrangian Turbulence, Phys. Rev. Lett98, 064502 (2007)

  29. [30]

    Reynolds, Superstatistical Mechanics of Tracer- Par- ticle Motions in Turbulence, Phys

    A. Reynolds, Superstatistical Mechanics of Tracer- Par- ticle Motions in Turbulence, Phys. Rev. Lett.91, 084503 (2003)

  30. [31]

    Mehrafarin, Superstatistics as the statistics of quasiequilibrium states: Application to fully developed turbulence, Phys

    M. Mehrafarin, Superstatistics as the statistics of quasiequilibrium states: Application to fully developed turbulence, Phys. Rev. E84, 022102 (2011)

  31. [32]

    Jung and H

    S. Jung and H. L. Swinney, Velocity difference statistics in turbulence, Phys. Rev. E72, 026304 (2005)

  32. [33]

    Rouse and S

    I. Rouse and S. Willitsch, Superstatistical Energy Distri- butions of an Ion in an Ultracold Buffer Gas, Phys. Rev. Lett.118, 143401 (2017)

  33. [34]

    Ourabah, Quantum entanglement and temperature fluctuations, Phys

    K. Ourabah, Quantum entanglement and temperature fluctuations, Phys. Rev. E95, 042111 (2017)

  34. [35]

    Cheraghalizadeh, M

    J. Cheraghalizadeh, M. Seifi, Z. Ebadi, H. Moham- madzadeh, and M. N. Najaf, Superstatistical two- temperature Ising model, Phys. Rev. E103, 032104 (2021)

  35. [36]

    Ourabah, Quasiequilibrium self-gravitating systems, Phys

    K. Ourabah, Quasiequilibrium self-gravitating systems, Phys. Rev. D102, 043017 (2020)

  36. [37]

    Ourabah, Generalized statistical mechanics of stellar systems, Phys

    K. Ourabah, Generalized statistical mechanics of stellar systems, Phys. Rev. E105, 064108 (2022)

  37. [38]

    G. C. Yalcin and C. Beck, Generalized statistical me- chanics of cosmic rays: Application to positron-electron spectral indices, Sci. Rep.8, 1764 (2018)

  38. [39]

    Beck and C

    C. Beck and C. Tsallis, Anomalous velocity distributions in slow quantum-tunneling chemical reactions, Phys. Rev. Research7, L012081 (2025)

  39. [40]

    Großmannet al., Non-Gaussian Displacements in Ac- tive Transport on a Carpet of Motile Cells, Phys

    R. Großmannet al., Non-Gaussian Displacements in Ac- tive Transport on a Carpet of Motile Cells, Phys. Rev. Lett.132, 088301 (2024)

  40. [41]

    M. O. Costa, R. Silva, and D. H. A. L. Anselmo, Su- perstatistical and DNA sequence coding of the human genome, Phys. Rev. E106, 064407 (2022)

  41. [42]

    A. Y. Abul-Magd, Modeling highway-traffic headway dis- tributions using superstatistics, Phys. Rev. E76, 057101 (2007)

  42. [43]

    Van der Straeten and C

    E. Van der Straeten and C. Beck, Superstatistical fluctu- ations in time series: Applications to share-price dynam- ics and turbulence, Phys. Rev. E80, 036108 (2009)

  43. [44]

    Sch¨ afer, C

    B. Sch¨ afer, C. Beck, K. Aihara, D. Witthaut, and M. Timme, Non-Gaussian power grid frequency fluctuations characterized by L´ evy-stable laws and superstatistics, Nat. Energy3, 119 (2018)

  44. [45]

    L. R. Gorj˜ aoet al., Open database analysis of scaling and spatio-temporal properties of power grid frequencies, Nat. Communications11, 6362 (2020)

  45. [46]

    Williams, B

    G. Williams, B. Sch¨ afer, and C. Beck, Superstatistical approach to air pollution statistics, Phys. Rev. Research 2, 013019 (2020)

  46. [47]

    Ourabah, L

    K. Ourabah, L. A¨ ıt Gougam, and M. Tribeche, Nonther- mal and suprathermal distributions as a consequence of superstatistics, Phys. Rev. E91, 012133 (2015)

  47. [48]

    Sattin and L

    F. Sattin and L. Salasnich, Multiparameter generaliza- tion of nonextensive statistical mechanics, Phys. Rev. E 65, 035106(R) (2002)

  48. [49]

    Ourabah, Demystifying the success of empirical distri- butions in space plasmas, Phys

    K. Ourabah, Demystifying the success of empirical distri- butions in space plasmas, Phys. Rev. Research2, 023121 (2020)

  49. [50]

    Ourabah, Superstatistics in the context of relativity, Phys

    K. Ourabah, Superstatistics in the context of relativity, Phys. Rev. Research7, 033190 (2025)

  50. [51]

    Ourabah, Fingerprints of nonequilibrium stationary distributions in dispersion relations, Sci

    K. Ourabah, Fingerprints of nonequilibrium stationary distributions in dispersion relations, Sci. Rep.11, 12103 (2021)

  51. [52]

    Bouzit and K

    O. Bouzit and K. Ourabah, Nonlinear structures in a nonequilibrium plasma: impact of small fluctuations, Eur. Phys. J. Plus139, 175 (2024)

  52. [53]

    Daviset al., Single-particle velocity distributions of collisionless, steady-state plasmas must follow super- statistics, Phys

    S. Daviset al., Single-particle velocity distributions of collisionless, steady-state plasmas must follow super- statistics, Phys. Rev. E100, 023205 (2019)

  53. [54]

    Daviset al., Kappa distribution from particle correla- tions in nonequilibrium, steady-state plasmas, Phys

    S. Daviset al., Kappa distribution from particle correla- tions in nonequilibrium, steady-state plasmas, Phys. Rev. E108, 065207 (2023)

  54. [55]

    S. I. Braginskii, Transport processes in a plasma, Rev. Plasma Phys.1, 205 (1965)

  55. [56]

    Balescu,Transport Processes in Plasmas, Vol

    R. Balescu,Transport Processes in Plasmas, Vol. 1: Classical Transport Theory(North-Holland, Amsterdam, 1988)

  56. [57]

    C. T. Dum, inPhysical Processes in Hot Cosmic Plas- mas, edited by W. Brinkmann, A. C. Fabian, and F. Gio- vanelli (Kluwer Academic, Dordrecht/Boston/London, 1990), pp. 157–180

  57. [58]

    G. J. M. Hagelaar and L. C. Pitchford, Solving the Boltz- mann equation to obtain electron transport coefficients and rate coefficients for fluid models, Plasma Sources Sci. Technol.14, 722 (2005)

  58. [59]

    Lvet al., Electronic transport of Lorentz plasma with collision and magnetic field effects, Chin

    C. Lvet al., Electronic transport of Lorentz plasma with collision and magnetic field effects, Chin. Phys. B25, 105201 (2016)

  59. [60]

    Du, Transport coefficients in Lorentz plasmas with the 15 B power-law kappa-distribution, Phys

    J. Du, Transport coefficients in Lorentz plasmas with the 15 B power-law kappa-distribution, Phys. Plasmas20, 092901 (2013)

  60. [61]

    Wang and J

    L. Wang and J. Du, The diffusion of charged particles in the weakly ionized plasma with power-law kappa- distributions, Phys. Plasmas24, 102305 (2017)

  61. [62]

    Guo and J

    R. Guo and J. Du, Transport coefficients of the fully ion- ized plasma with kappa-distribution and in strong mag- netic field, Physica A523, 156 (2019)

  62. [63]

    Husidic, M

    E. Husidic, M. Lazar, H. Fichtner, K. Scherer, and S. Poedts, Transport coefficients enhanced by suprathermal particles in nonequilibrium heliospheric plasmas, Astron. Astrophys.654, A99 (2021)

  63. [64]

    Husidic, K

    E. Husidic, K. Scherer, M. Lazar, H. Fichtner, and S. Poedts, Toward a Realistic Evaluation of Transport Co- efficients in Non-equilibrium Space Plasmas, Astrophys. J.927, 159 (2022)

  64. [65]

    R. A. Treumann, Kinetic Theoretical Foundation of Lorentzian Statistical Mechanics, Phys. Scr.59, 19 (1999)

  65. [66]

    C. Beck, E. G. D. Cohen, and H. L. Swinney, From time series to superstatistics, Phys. Rev. E72, 056133 (2005)

  66. [67]

    Liu and J

    B. Liu and J. Goree, Superdiffusion and Non-Gaussian Statistics in a Driven-Dissipative 2D Dusty Plasma, Phys. Rev. Lett.100, 055003 (2008)

  67. [68]

    Iranmanesh, H

    M. Iranmanesh, H. Arjomand Kermani, and K. Ourabah, Superstatistics and stellar rotation: Modeling velocity distributions in six stellar groups, New Astronomy119, 102408 (2025)

  68. [69]

    Brito, I

    C. Brito, I. S. Aranson, and H. Chat´ e, Vortex Glass and Vortex Liquid in Oscillatory Media, Phys. Rev. Lett.90, 068301 (2003)

  69. [70]

    P. D. Dixit, A maximum entropy thermodynamics of small systems, J. Chem. Phys.138, 184111 (2013)

  70. [71]

    Xu and C

    D. Xu and C. Beck, Transition from lognormal toχ 2- superstatistics for financial time series, Physica A453, 173 (2016)

  71. [72]

    Jizba, J

    P. Jizba, J. Korbel, H. Laviˇ cka, M. Prokˇ s, V. Svo- boda, and C. Beck, Transitions between superstatistical regimes: Validity, breakdown and applications, Physica A493, 29 (2018)

  72. [73]

    W. C. Feldman, J. R. Asbridge, S. J. Bame, M. D. Mont- gomery, S. P. Gary, Solar wind electrons, J. Geophys. Res.80, 4181 (1975)

  73. [74]

    C. S. Salem, M. Pulupa, S. D. Bale, and D. Verscharen, Precision electron measurements in the solar wind at 1 au from NASA’s Wind spacecraft, Astron. Astrophys.675, A162 (2023)

  74. [75]

    P. L. Bhatnagar, E. P. Gross, and M. Krook, A model for collision processes in gases. I. Small amplitude processes in charged and neutral one-component systems, Phys. Rev.94, 511 (1954)

  75. [76]

    Helander and D

    P. Helander and D. J. Sigmar,Collisional Transport in Magnetized Plasmas(Cambridge University Press, Cam- bridge, 2005)

  76. [77]

    T. J. M. Boyd and J. J. Sanderson,The Physics of Plas- mas(Cambridge University Press, Cambridge, 2003)

  77. [78]

    Wang and J

    Y. Wang and J. Du, The viscosity of charged particles in the weakly ionized plasma with power-law distributions, Phys. Plasmas25, 062309 (2018)

  78. [79]

    G. J. M. Hagelaar, Modelling electron transport in magnetized low-temperature discharge plasmas, Plasma Sources Sci. Technol.16, S57 (2007)

  79. [80]

    Risken,The Fokker–Planck Equation: Methods of So- lution and Applications, 2nd ed

    H. Risken,The Fokker–Planck Equation: Methods of So- lution and Applications, 2nd ed. (Springer, Heidelberg, 1996)

  80. [81]

    Andries, P

    P. Andries, P. Le Tallec, J.-Ph. Perlat, and B. Perthame, The Gaussian-BGK model of Boltzmann equation with small Prandtl number, Eur. J. Mech. B Fluids19, 813 (2000)

Showing first 80 references.