Intermittency in Voyager Magnetic Field Beyond the Heliosphere

D.J. McComas; G. Livadiotis; J.S. Rankin; L.Y. Khoo; M.E. Cuesta

REVIEW 4 major objections 4 minor 25 references

Intermittency in Voyager Magnetic Field Beyond the Heliosphere

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Magnetic field at Voyager 1 stays intermittent, not Gaussian

desk verdict A transparent, useful reanalysis showing kappa estimates for Voyager 1 increments are window-sensitive, but the headline kappa 3-7 rests on a 'plateau' that their own numbers contradict. read the letter →

arxiv 2507.10378 v1 pith:VB24W6XV submitted 2025-07-14 astro-ph.SR astro-ph.EPphysics.space-ph

L.Y. Khoo , G. Livadiotis , D.J. McComas , M.E. Cuesta , J.S. Rankin This is my paper · ORCID

classification astro-ph.SRastro-ph.EPphysics.space-ph

keywords intermittencykappadistributionsq-statisticsmagneticfieldincrementsverylocalinterstellarmediumVoyager1heliopauseturbulence

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper re-examines the statistical distribution of magnetic field increments measured by Voyager 1 during the first 271 days of 2023, the same data that led a 2024 study to conclude the increments had become Gaussian. The authors show that the fitted kappa value swings wildly with the chosen increment window, and that a weighted average over the stable 30 to 100 minute window range gives kappa values between 4.70 and 5.67 for all field components and magnitudes. Over non-overlapping 30-day periods, the weighted-average kappa stays within 3 to 7, firmly non-Gaussian. The paper concludes that Voyager 1 remains in an intermittent, correlated magnetic field environment in the very local interstellar medium, and that the earlier Gaussian result came from mixing different structures into one long statistical window.

What carries the argument

The central object is the kappa distribution, a family of non-Gaussian distributions with parameter $\kappa$ that approaches a Gaussian as $\kappa \to \infty$ and describes correlated fluctuations at low $\kappa$, tied to the Tsallis q-statistics by $q = 1 + 1/\kappa$. The analysis uses two independent estimators: a kappa-fitting technique that minimizes reduced chi-square in logarithmic space, so the tails of the increment distribution are weighted, and a kappa-moment technique that inverts the ratio $M_{1/2}/M_1$ of increment moments. The load-bearing device is a weighted average of kappa over increment windows from 30 to 100 minutes, chosen because both kappa and the variance $M_1$ stabilize to a plateau there; this removes the arbitrary choice of a single increment scale.

What would settle it

A decisive test is to synthesize surrogate data: generate a Gaussian time series with the same power spectrum, sampling cadence, data gaps, and noise level as the Voyager 1 2023 record, run it through the same resampling, log-space histogram fitting, kappa-moment estimation, and 30 to 100 minute weighted averaging; if the surrogates return weighted-average kappa values in the 3 to 7 range, the reported non-Gaussianity is a method artifact, whereas if they stay at high kappa the result is real.

Watch

Extended reading notes

Core claim

The central claim is that the magnetic field increments seen by Voyager 1 in the very local interstellar medium during days 1 through 271 of 2023 do not follow Gaussian statistics. A single one-hour increment window yields kappa values of about 9 to 16, which look near-Gaussian and match the prior result; however, the fitted kappa changes sharply with the increment window, and a weighted average over the plateau region of 30 to 100 minutes gives kappa values between 4.70 and 5.67 for the normal, tangential, radial, tangential-normal magnitude, and total magnitude increments. For non-overlapping 30-day statistical periods, the weighted-average kappa remains consistently in the 3 to 7 range. The authors attribute the earlier Gaussian appearance to statistically induced mixing of different structures when long periods are pooled, and conclude that Voyager 1 still travels through an intermittent, coherent magnetic field environment in the very local interstellar medium.

Load-bearing premise

The load-bearing premise is that the plateau in fitted kappa and variance across 30 to 100 minute increment windows marks a physically stable fluctuation regime, rather than an artifact of resampling, data gaps, or averaging over genuinely different fluctuation scales.

Editorial extensions

If this is right

The 2024 Gaussian conclusion for the very local interstellar medium is reinterpreted as a statistically induced artifact of pooling long periods and selecting a single increment window.
The argument that Voyager 1 crossed a new heliopause boundary during early 2023 loses its statistical support from magnetic field increments.
Magnetic field increments in the very local interstellar medium are better described as intermittent and correlated, with kappa values of 3 to 7.
Future intermittency analyses should report kappa as a function of increment window and statistical period, using weighted averages over plateau regions rather than a single window.
The two-technique kappa framework can be applied to magnetic field time series from other spacecraft and other plasma environments.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The 30 to 100 minute plateau may mark a physical coherence scale in the local interstellar medium, which could be tested by comparing it with spectral breaks or structure-function slopes in the same Voyager 1 record.
The mixing effect that inflates kappa for periods longer than about 110 days may also explain Gaussian-looking statistics in other astrophysical time series when multiple regimes are pooled; reanalyzing Voyager 2 and other intervals would show how general it is.
If the non-Gaussian claim holds, the heliopause search falls back to plasma, plasma-wave, and energetic-particle signatures, because the magnetic-field-increment argument for a new boundary is gone.
The analysis averages over resampling durations rather than using fixed high-resolution increments, so fine-scale intermittency below 30 minutes could be suppressed; a follow-up using increments larger than the resampling scale could test whether even lower kappa values appear.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Quick take: this is a careful reanalysis of Voyager 1 magnetic-field increments that makes a real point—kappa estimates depend strongly on increment window—but the headline kappa range 4.7-5.67 is built on a 'plateau' that their own data show is not actually flat. The sensitivity finding is solid; the averaged number is not yet established.

What's new: they reproduce Burlaga et al. (2024b)'s near-Gaussian result for 1-hour increments (kappa ~11), then systematically vary increment windows (1-200 min) and statistical periods. Fitting in log space is a sensible improvement, and the two estimators agree for the 1-hour case. The appendices checking bin-width and statistical-power effects are honest and useful. Showing that a single arbitrary window choice can produce near-Gaussian kappa while neighboring windows give kappa ~6 is a meaningful caution for the field.

Where it gets shaky: Section 5.2 defines the 30-100 min plateau, but the paper itself reports kappa ~15 at 60 min and ~6 at 62 min. That is not a plateau; it's a scatter that they average over. The weighted average is a smoothing of non-stationary estimates, not a scale-independent characteristic of the VLISM. The kappa-moment technique is only demonstrated for the 1-hour window, so the 'two independent techniques' claim doesn't actually cover the weighted-average result. And the assertion that mixing structures produces Gaussian-like results is plausible but not tested with synthetic or independent data.

None of this is fatal to the paper's main warning: kappa is sensitive to analysis choices, and a single window can mislead. But the specific claim that VLISM increments have kappa ~3-7 (or that Burlaga's conclusion is an artifact) needs stronger support. I'd want to see either a justification for why the 30-100 min range is physical, a robust estimator that handles the scatter, or a synthetic-data test of the mixing claim.

This paper deserves peer review—it challenges a high-profile result on public data with a transparent method. But it needs revision before acceptance. Expect the referees to push on the plateau.

Referee Report

4 major / 4 minor

Summary. This paper analyzes Voyager 1 magnetic field increments during the first 271 days of 2023 to test whether the very local interstellar medium (VLISM) magnetic field is Gaussian or intermittent. The authors fit kappa distributions to increment histograms in logarithmic space, introduce a kappa-moment estimator, and explore how the inferred kappa depends on the increment window and on the length of the statistical period. For the 1-hour / 271-day case they recover high kappa values (≳10) consistent with Burlaga et al. (2024b). However, they report that kappa is highly sensitive to the increment window, and they propose a variance-weighted average over a claimed 30–100 min plateau, obtaining kappa = 4.70–5.67. They also find that 30-day non-overlapping periods yield kappa in 3–7 and that longer periods increase kappa, which they interpret as mixing of distinct structures. The central claim is that VLISM magnetic field increments are non-Gaussian and intermittent, contradicting the Gaussian conclusion of Burlaga et al. (2024b).

Significance. If the central claim is correct, the paper has substantial significance: it challenges a recent high-profile conclusion about the heliopause location and the statistical character of the VLISM, and it demonstrates that binning choices and increment-window choices can materially change kappa-based intermittency conclusions. The paper has concrete strengths: the log-space fitting is a sensible improvement for tail sensitivity; the kappa-moment technique provides an independent estimator at the 1-hour scale; and the appendices address bin width and limited-event statistical effects. The sensitivity of kappa to increment window is convincingly demonstrated and is itself a useful cautionary result. However, the headline non-Gaussian conclusion depends on the claim that kappa and M1 stabilize to a plateau over 30–100 minutes, and the paper's own numbers undercut that claim, as detailed below. The cross-technique consistency applies only to the single 1-hour window and does not validate the weighted-average kappa values.

major comments (4)

[§5.2, Fig. 6] The claimed plateau in the 30–100 min increment-window range is contradicted by the numbers reported in the same section: a 60-minute window gives κ ≈ 15 while a 62-minute window gives κ ≈ 6, a factor-of-2.5 change between adjacent windows. Because the headline values 4.70–5.67 are variance-weighted averages over exactly this range, they are averages over non-stationary estimates rather than a scale-independent characteristic, so the central non-Gaussian conclusion rests on an unestablished premise. The authors need to either justify the plateau quantitatively (e.g., a stationarity test or block-bootstrap across windows) or reframe the claim as window-dependent sensitivity rather than a single kappa value for the VLISM.
[§5.2, §6] The claim of support from 'two independent techniques' applies only to the 1-hour single-window estimates in Fig. 5; the weighted-average 30–100 min kappa values that drive the headline result are produced by the kappa-fitting technique alone. The kappa-moment technique is not applied to the weighted averages, so the cross-technique agreement does not cover the central claim.
[§4.2.1, Fig. 7] There is an internal inconsistency between the text, which defines the weighted average over increment windows 30–100 min, and the Fig. 7 caption, which says the purple weighted-average line is derived from 1–200 min windows. Since the 1–200 min range includes the highly fluctuating, non-converging small-window estimates (assigned κ = 100 with uncertainty 10000 in §4.2.1), the choice matters and must be reconciled.
[§5.3] The choice of a 30-day statistical period is justified only by the observation that kappa is stable for periods of 30–110 days, but no quantitative criterion or uncertainty is given for selecting 30 days rather than, say, 60 or 90 days. The interpretation that longer periods mix structures and inflate kappa is plausible but not tested; a direct comparison of the increment distributions before and after day 110 would strengthen the claim.

minor comments (4)

[Throughout] The manuscript contains numerous typographical errors (e.g., 'Dield', 'Ditting', 'VLSIM' in the summary) that should be corrected in the published version.
[§4.2.2, Eq. (9)] Equation (9) is referenced but the explicit functional form of g(κ0) is not displayed; please include the full expression for reproducibility.
[§4.2.1, §5.2] The treatment of non-converged fits (κ = 100, uncertainty = 10000) should be reported as a count per increment window, since these entries affect the enriched histogram in Fig. 7 even if their weight in the variance-weighted average is small.
[Appendix A] Appendix A reports the bin-width check only for δBN (Fig. A2); please state whether the same conclusion was checked for the other magnetic field components and magnitudes.

Circularity Check

1 steps flagged · score 2.0 of 10

Central kappa measurement is empirical, not circular; one minor by-construction validation in the enriched-histogram representativeness check.

other [Section 5.2, paragraph after Figure 7]
"The enriched histogram is computed by randomly generating kappa values (in this case, 10 points for each increment window) from normal distributions. The mean of each distribution is the estimated kappa value of that window, and the standard deviation is the corresponding kappa uncertainty. Notably, the average kappa values (purple lines) for δBN, δBT, δBR, δBTN, and δB generally coincide with the peak of the two histograms, suggesting that the weighted-average kappa values are representative of the period we intended to characterize without biasing to a specific choice of increment window."

The enriched histogram is synthesized from the same per-window kappa estimates and uncertainties that are combined into the variance-weighted average. Because each synthetic sample is drawn with mean equal to the fitted kappa and standard deviation equal to its uncertainty, the mode of the resulting mixture is forced, to first order, to sit at the inverse-variance-weighted mean. The 'agreement' between the weighted average and the histogram peak is therefore a property of how the histogram was generated, not an independent confirmation that the 30–100 min window range is representative of a scale-independent kappa. This is a minor circular validation; it does not by itself determine the kappa values, which come from the fits to Voyager data.

full rationale

The paper's central quantitative result—kappa values in the 3–7 range for Voyager 1 VLISM magnetic-field increments—is obtained by direct histogram fits (log-space chi-square minimization) and moment-ratio estimation from the measured 48-s data. These are parameter estimates, not predictions derived from premises that already contain the answer; the data are external to the kappa formalism. The main scientific dispute with Burlaga et al. (2024b) therefore rests on analysis choices (increment window, log vs linear fitting, statistical period) rather than on circular reasoning. The one identifiable by-construction step is the 'enriched histogram' validation in Section 5.2: the synthetic histogram is generated from the same per-window kappa means and uncertainties that define the variance-weighted average, so its peak coinciding with that average is a mathematical artifact of the construction, not an independent confirmation of representativeness. This is a minor circular validation and does not affect the fitted values themselves. In addition, the interpretation of low kappa as 'coherence' and 'intermittency' is imported from the q-statistics framework of Livadiotis and McComas, who are co-authors; this is a self-citation chain, but it is not an equation-level reduction and the underlying data analysis remains externally anchored to Voyager measurements. Accordingly, the paper merits a low circularity score rather than a charge of central circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central quantitative result rests on several free analytic choices, chiefly the post hoc increment-window plateau range and the 30-day statistical period, plus the interpretive framework that maps fitted kappa values to physical coherence and intermittency. No new physical entities are introduced. The main risk is not circularity in the fitting, but that the headline kappa range is conditioned on choices made after inspecting the data.

free parameters (4)

Increment-window plateau range (30-100 minutes)
Chosen post hoc in Section 5.2 as the range where kappa and M1 stabilize; the central kappa values 4.7-5.7 are weighted averages over this chosen range.
Histogram bin width (0.005 nT for most components, 0.002 nT for BR) = 0.005/0.002 nT
Chosen by hand in Section 5.1. Appendix A shows the finer bin gives slightly lower kappa values, so the bin choice has a small effect on the headline numbers.
Kappa fit range and non-convergence assignment = κ = 1.5-100; non-converged set to 100 with uncertainty 10000
The upper cap of 100 and the arbitrary non-convergence assignment, defined in Section 4.2.1, can bias estimates toward Gaussian; the moment technique partly mitigates this.
Statistical period of 30 days
Selected in Section 5.3 because kappa is stable from 30 to roughly 110 days; the headline claim of kappa 3-7 is tied to this specific choice of statistical window.

assumptions (5)

domain assumption Kappa distributions with a physical kappa parameter are the correct model for magnetic field increments, and kappa maps to Tsallis q via q = 1 + 1/κ.
Section 2 invokes Livadiotis & McComas 2009 to connect kappa and q-statistics. The conclusion that kappa 3-7 means non-Gaussian and intermittent depends on this model choice.
domain assumption Low kappa values correspond to coherent structures and intermittency, while Gaussian statistics correspond to decoherence.
Section 1 and Section 6 apply this physical interpretation directly. It is inherited from the q-statistics literature and is not independently tested in this paper.
domain assumption The Voyager 1 BR component, calibrated with less robust methods beyond the heliosphere, can be used at face value in the increments analysis.
Section 3 acknowledges the weaker BR calibration but still includes δBR, which gives the lowest average kappa around 4.7. Unmodeled calibration error could affect this component.
domain assumption Averaging kappa estimates over increment windows that are not independent yields a physically meaningful characteristic kappa.
The weighted-average procedure in Section 5.2 treats estimates from different increment windows as independent samples and weights by their uncertainties. This is an assumption about how to combine statistically dependent estimates.
standard math The generalized central limit theorem ensures convergence to kappa distributions for correlated fluctuations.
Section 2 cites Umarov et al. 2008 for this background. It is standard in the non-extensive statistics framework and is not central to the data fitting itself.

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Cite this review

Pith. "Pith review of Intermittency in Voyager Magnetic Field Beyond the Heliosphere." pith.science (2026). https://pith.science/paper/VB24W6XV

@misc{pith2026250710378,
  author       = {Pith},
  title        = {Pith review of: Intermittency in Voyager Magnetic Field Beyond the Heliosphere},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VB24W6XV}},
  note         = {Machine review of arXiv:2507.10378}
}

read the original abstract

As the two Voyager spacecraft traveled beyond the heliosphere, they encountered a magnetic field environment that had never been observed before. Studies have attempted to characterize this new regime by examining the magnetic field intermittency. This is typically done by fitting the optimal kappa distribution function and interpreting its so-called q-statistics to characterize the magnetic field increments. Using this approach, recent findings concluded that beyond a certain distance, the magnetic field increments in the very local interstellar medium (VLISM) follow Gaussian statistics, unlike those both inside the heliosphere and in the region just beyond the widely accepted heliopause location, raising questions about the heliopause identification. This study explores this issue in detail by (1) optimizing the derivation of the distribution function, (2) examining whether and how the results depend on increment windows and time periods, and (3) determining the statistical behavior of the examined time series. Using magnetic field measurements from Voyager 1, we present two independent techniques and introduce a statistical framework to systematically analyze the distributions of magnetic field increments. Contrary to previous findings, we find that magnetic field increments in the VLISM do not follow a Gaussian distribution (k to infinity) and instead are in the non-Gaussian range of kappa values (3-7, when analyzed on a 30-day statistical period). We further demonstrate how erroneous, statistically induced results can arise that mimic Gaussian-like results when mixing different structures in such analyses. Our results show that Voyager 1 still travels in the intermittent magnetic field environment of the VLISM.

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Reference graph

Works this paper leans on

25 extracted references · 25 canonical work pages

[1]

Dra$ version July 4, 2025 Typeset using LATEX default style in AASTeX631 Intermittency in Voyager’s Magnetic Field Beyond the Heliosphere L. Y . Khoo ,1 G. Livadio.s ,1 D. J. McComas ,1 M. E. Cuesta ,1 and J. S. Rankin 1 1Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA ABSTRACT As the two Voyager spacecraft traveled be...

work page 2025
[2]

Analysis of these unprecedented measurements has led to a variety of interpretations of this unique data set

INTRODUCTION As the Voyagers explore the Very Local Interstellar Medium, they continue to uncover unexpected behaviors in particles, plasma and magnetic Dields in the region beyond the heliopause, which is modiDied by the presence of the heliosphere. Analysis of these unprecedented measurements has led to a variety of interpretations of this unique data s...

work page 2024
[3]

By maximizing the generalized non-extensive entropy consistent with thermodynamics (Tsallis 1988; Livadiotis & McComas

BACKGROUND: Q-STATISTICS AND KAPPA DISTRIBUTION Non-extensive statistical mechanics has provided a very successful generalization of the Boltzmann-Gibbs statistical mechanics, which describes a system in the classical adaptation of thermal equilibrium where there are no correlations among the particles. By maximizing the generalized non-extensive entropy ...

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[4]

2006a,b, 2024a)

METHODOLOGY: DETERMINING KAPPA PARAMETERS OF MAGNETIC FIELD INCREMENTS Magnetic Dield increments and their statistical distribution are useful tools to examine the intermittency in various space environments, including the heliosheath and VLISM (Burlaga et al. 2006a,b, 2024a). Past studies have demonstrated the use of q-index to characterize the statistic...

work page 2024
[5]

RESUL TS This section investigates the kappa values characteristic of magnetic Dield increments during the Dirst 271 days of 2023 using the approaches described in Section

work page 2023
[6]

calibrated

while the R component appears to have a larger discrepancy with the models. The T and N vectors were calibrated using inputs from spacecraft spinning maneuvers such as magnetometer roll maneuvers (also known as MAGROLs), which cannot be used to calibrate the radial vector. Therefore, BR was “calibrated” using the average expected value based on the Parker...

work page 2009
[7]

(Right) Histogram and enriched histogram of the kappa values from the left panels for 𝛿𝐵%, 𝛿𝐵&, 𝛿𝐵', 𝛿𝐵&%, and 𝛿𝐵

(Left) Similar to Figure 5, but for the kappa values and its corresponding uncertainty at each increment window, ranging from 1 minutes to 200 minutes. (Right) Histogram and enriched histogram of the kappa values from the left panels for 𝛿𝐵%, 𝛿𝐵&, 𝛿𝐵', 𝛿𝐵&%, and 𝛿𝐵. 5.3. Average kappa estimates with varying statistical windows We further investigate how t...

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)&'/"∙)(+!,-,

Comparison of the observations and its <it presented in linear (left) and logarithmic (right) scales. The discrepancy between the observation and the <it (from the kappa <itting technique) outside of the peak region is more apparent when presenting the result in logarithmic scale. There are no counts in bins beyond -0.02 and 0.02 nT, and thus the data poi...

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[9]

We compute the optimal kappa by binning the values of kappa (with a width of κ-bin equal to ∆κ = 0.1) and Dinding the bin for which the reduced χ2 is minimized

Example of how the optimal kappa value and the corresponding uncertainty are determined using the kappa <itting technique. We compute the optimal kappa by binning the values of kappa (with a width of κ-bin equal to ∆κ = 0.1) and Dinding the bin for which the reduced χ2 is mini...

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[10]

The Ditting result for the hourly-averaged δBN from the Dirst 271 days of 2023 is shown in Figure

In the case of no convergence, the ‘optimal’ kappa value is assigned to the highest kappa value (100, in this case), and its uncertainty is set to an arbitrarily large value (10000). The Ditting result for the hourly-averaged δBN from the Dirst 271 days of 2023 is shown in Figure

2023
[11]

Notably, for a given distribution that has sufDicient statistics and is well-deDined, the discrepancy between the data and the Dit beyond the core population (near the peak) only becomes more apparent when presented in log scale. In other words, when the Dit was done in linear...

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An example of how the kappa moment technique is used to estimate the parameter kappa using the hourly-averaged/hourly increment scale data for 𝐵% during the <irst 271 days of 2023 as shown in Figure

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[14]

Importantly, it explores how the kappa estimates vary for different increment windows and statistical periods. 5.1. Averaged kappa values for the ;irst 271 days of 2023 Figure 5 shows the 1-hour magnetic Dield increments during the Dirst 271 days of 2023 (consistent with Burla...

2023
[15]

The kappa value listed on Figure 5 is the average of kappa estimates derived from the two techniques discussed in Section 4, and the uncertainty is the difference between the two kappa estimates. We determine that the average kappa values for all three Dield components are >10...

2024
[16]

(Right) Histogram of the magnetic <ield increments from the left panels for (f-j) 𝛿𝐵%, 𝛿𝐵&, 𝛿𝐵', 𝛿𝐵&%, and 𝛿𝐵, respectively

(Left) Increments of magnetic <ield in units of nT for (a) the normal component, 𝛿𝐵%, (b) the tangential component, 𝛿𝐵&, (c) the radial component, 𝛿𝐵', (d) the magnitude from the tangential and normal components, 𝛿𝐵&% (e) the magnitude of all three <ield components, 𝛿𝐵. (Right...

2023
[17]

The statistical period is still the <irst 271 days of 2023 for consistency with Section 5.1

Kappa values, M1, and the reduced 𝜒( values for the histogram of the magnetic <ield increments,𝛿𝐵%, as a function of increment windows. The statistical period is still the <irst 271 days of 2023 for consistency with Section 5.1. The bottom panel provides an illustration of the...

2023
[20]

The Ditting was performed in logarithmic space, which improves the inclusion of the distributions’ tails

DISCUSSION AND SUMMARY This study investigates how the choice of increment windows and the statistical period affect the Ditted distribution of magnetic Dield increments. The Ditting was performed in logarithmic space, which improves the inclusion of the distributions’ tails. ...

2024
[21]

Particularly, δBN and δBT have very similar average kappa values, ∼5.25

Overall, we determine that the average kappa values for δBN, δBT, δBR, δBTN, and δB are between 4.70 and 5.67 for the same statistical period (the Dirst 271 days of 2023). Particularly, δBN and δBT have very similar average kappa values, ∼5.25. Similarly, δBTN and δB have thei...

2023
[23]

the presence of intermittency and scaling laws are key ingredients of turbulence

0 2 4 6 8 10Average Kappa 13 resampling window to remain consistent with the method used by Burlaga et al. (2024b); this allows us to focus on understanding the effect of varying resampling window and statistical window on the result. We note that resampling could reduce the e...

2024
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0 2 4 6 8 10Average Kappa for δBN 7 days10 days 20 days 30 days45 days 16 REFERENCES Behannon, K. W ., A. M. H. B. L. F . L. R. P . N. N. F . N. 1977, 1977SSRv...21..235B Page

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that are available in the radial, tangential and normal (R, T , and N) magnetic Dield vectors as well as its magnitude from three Dield components. R refers to the sun-to-spacecraft vector , T is the cross product of the solar rotation axis (northward) and R, and N completes t...

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but are not close to being a Gaussian distribution as suggested by Burlaga et al. (2024b). The kappa value also varies signiDicantly as a function of increment windows. Smaller increment windows correspond to larger estimation of kappa, such that the distributions appear nearl...

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Tsallis 2009; Livadiotis 2014)

(also known as Tsallis entropy), one can derive the Tsallis q-exponential distribution, a canonical distribution that is parameterized by q-index and describes the average behavior of a particle system that may or may not be at thermal equilibrium (e.g. Tsallis 2009; Livadioti...

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

Dra$ version July 4, 2025 Typeset using LATEX default style in AASTeX631 Intermittency in Voyager’s Magnetic Field Beyond the Heliosphere L. Y . Khoo ,1 G. Livadio.s ,1 D. J. McComas ,1 M. E. Cuesta ,1 and J. S. Rankin 1 1Department of Astrophysical Sciences, Princeton University, Princeton, NJ 08544, USA ABSTRACT As the two Voyager spacecraft traveled be...

work page 2025

[2] [2]

Analysis of these unprecedented measurements has led to a variety of interpretations of this unique data set

INTRODUCTION As the Voyagers explore the Very Local Interstellar Medium, they continue to uncover unexpected behaviors in particles, plasma and magnetic Dields in the region beyond the heliopause, which is modiDied by the presence of the heliosphere. Analysis of these unprecedented measurements has led to a variety of interpretations of this unique data s...

work page 2024

[3] [3]

By maximizing the generalized non-extensive entropy consistent with thermodynamics (Tsallis 1988; Livadiotis & McComas

BACKGROUND: Q-STATISTICS AND KAPPA DISTRIBUTION Non-extensive statistical mechanics has provided a very successful generalization of the Boltzmann-Gibbs statistical mechanics, which describes a system in the classical adaptation of thermal equilibrium where there are no correlations among the particles. By maximizing the generalized non-extensive entropy ...

work page 1988

[4] [4]

2006a,b, 2024a)

METHODOLOGY: DETERMINING KAPPA PARAMETERS OF MAGNETIC FIELD INCREMENTS Magnetic Dield increments and their statistical distribution are useful tools to examine the intermittency in various space environments, including the heliosheath and VLISM (Burlaga et al. 2006a,b, 2024a). Past studies have demonstrated the use of q-index to characterize the statistic...

work page 2024

[5] [5]

RESUL TS This section investigates the kappa values characteristic of magnetic Dield increments during the Dirst 271 days of 2023 using the approaches described in Section

work page 2023

[6] [6]

calibrated

while the R component appears to have a larger discrepancy with the models. The T and N vectors were calibrated using inputs from spacecraft spinning maneuvers such as magnetometer roll maneuvers (also known as MAGROLs), which cannot be used to calibrate the radial vector. Therefore, BR was “calibrated” using the average expected value based on the Parker...

work page 2009

[7] [7]

(Right) Histogram and enriched histogram of the kappa values from the left panels for 𝛿𝐵%, 𝛿𝐵&, 𝛿𝐵', 𝛿𝐵&%, and 𝛿𝐵

(Left) Similar to Figure 5, but for the kappa values and its corresponding uncertainty at each increment window, ranging from 1 minutes to 200 minutes. (Right) Histogram and enriched histogram of the kappa values from the left panels for 𝛿𝐵%, 𝛿𝐵&, 𝛿𝐵', 𝛿𝐵&%, and 𝛿𝐵. 5.3. Average kappa estimates with varying statistical windows We further investigate how t...

work page 2023

[8] [8]

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Comparison of the observations and its <it presented in linear (left) and logarithmic (right) scales. The discrepancy between the observation and the <it (from the kappa <itting technique) outside of the peak region is more apparent when presenting the result in logarithmic scale. There are no counts in bins beyond -0.02 and 0.02 nT, and thus the data poi...

work page 2013