Characterization of the Alfvén Transition in the Young
Solar Wind using Parker Solar Probe Observations
Approaching Solar Maximum

Subash Adhikari, Riddhi Bandyopadhyay, Joshua Goodwill, William H.

Matthaeus, David Ruffolo, Panisara Thepthong, Peera Pongkitiwanichakul,
Sohom Roy, Francesco Pecora, Rohit Chhiber, et al.

► To cite this version:

Subash Adhikari, Riddhi Bandyopadhyay, Joshua Goodwill, William H. Matthaeus, David Ruffolo, et al.. Characterization of the Alfvén Transition in the Young Solar Wind using Parker Solar Probe Observations Approaching Solar Maximum. The Astrophysical Journal, 2026, 997, 10.3847/1538-4357/ae2c78 . insu-05524759

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# Characterization of the Alfvén Transition in the Young Solar Wind using Parker Solar Probe Observations Approaching Solar Maximum

Subash Adhikari¹ ID, Riddhi Bandyopadhyay¹ ID, Joshua Goodwill¹ ID, William H. Matthaeus¹ ID, David Ruffolo² ID,
Panisara Thephthong³ ID, Peera Pongkitiwanichakul⁴ ID, Sohom Roy⁵ ID, Francesco Pecora¹ ID, Rohit Chhiber¹,⁶ ID,
Rayta Pradata¹ ID, Arcadi Usmanov¹,⁶ ID, Michael Stevens⁷ ID, Samuel Badman⁷ ID, Orlando Romeo⁸ ID, Jiaming Wang¹ ID, and
Melvyn L. Goldstein⁹ ID

¹Department of Physics and Astronomy, University of Delaware, Newark, DE 19716, USA; subash@udel.edu

²Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand

³LPC2E, OSUC, CNRS, University of Orléans, CNES, Orléans F-45071, France

⁴Department of Physics, Faculty of Science, Kasetsart University, Bangkok 10900, Thailand

⁵Space Research Institute, Austrian Academy of Sciences, Schmiedlstraße 6, 8042 Graz, Austria

⁶Heliophysics Science Division, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA

⁷Center for Astrophysics, Harvard & Smithsonian, Cambridge, MA 02138, USA

⁸Department of Earth & Planetary Science and Space Sciences Laboratory, University of California at Berkeley, Berkeley, CA 94720, USA

⁹Space Science Institute, Boulder, CO 80301, USA

Received 2025 August 29; revised 2025 December 1; accepted 2025 December 13; published 2026 January 28

## Abstract

Close to Earth, the solar wind is usually super-Alfvénic, i.e., the speed of the solar wind is much larger than the Alfvén speed. However, in the lower coronal regions, the solar wind is mostly sub-Alfvénic. With the Parker Solar Probe (PSP) crossing the boundary between the sub- and super-Alfvénic flow, R. Bandyopadhyay et al. performed a turbulence characterization of the sub-Alfvénic solar wind with initial data from encounters 8 and 9. In this study, we reexamine the turbulence properties such as turbulence amplitude, anisotropy of the magnetic field variance, intermittency, and switchback strength using PSP data from encounters 8–19. The later orbits probe lower altitudes and experience sub-Alfvénic conditions more frequently, providing a greater statistical coverage to contrast sub- and super-Alfvénic solar wind. These later orbits also extend the observations from near solar minimum at launch to near solar maximum conditions. Also, by isolating the intervals where the solar wind speed is approximately equal to the Alfvén speed, we explore the transition in more detail. We show that the amplitude of the normalized magnetic field fluctuation is smaller for the sub-Alfvénic samples. While solar wind turbulence in general is shown to be anisotropic, the sub-Alfvénic samples are more anisotropic than the super-Alfvénic samples, in general. Further, we show that the sub- and super-Alfvénic samples do not show much distinction in terms of intermittency strength. Finally, consistent with prior results, we find no evidence for polarity reversing >90° switchbacks in the sub-Alfvénic solar wind.

*Unified Astronomy Thesaurus concepts: Space plasmas (1544); Solar wind (1534); Interplanetary magnetic fields (824); Alfvén waves (23)*

## 1. Introduction

Parker Solar Probe (PSP; N. Fox et al. 2016; N. E. Raouafi et al. 2023) explores the solar atmosphere more closely than any previous in situ mission and is therefore able to observe processes potentially responsible for heating and accelerating young solar wind plasma. This already iconic dataset finally enables probing of the origin of the solar wind—a problem that has challenged the community for more than half a century. It is now well understood that the plasma close to the Sun is magnetically controlled and at least approximately corotates with the Sun. At large distances, the solar magnetic field can no longer control the plasma as the energy density in the flow greatly exceeds the thermal and magnetic energy densities. Between these limits are several interesting transitions where, first, the flow energy density exceeds the magnetic energy density, and second, the thermal pressure equals or exceeds the magnetic pressure. The first of these is commonly called the Alfvén critical point (E. J. Weber & L. Davis 1967), now

thought to be a more complex region (R. Chhiber et al. 2024). Exploring this “Alfvén transition region” in PSP data provides a unique opportunity to understand the differences in physical properties and processes that operate in the corona (the region below the transition) and the extended solar wind above.

In regard to exploring the change in conditions across the trans-Alfvénic transition, R. Bandyopadhyay et al. (2022) contrasted the properties of sub-Alfvénic solar wind as measured by PSP during the eighth (E8) and ninth (E9) solar encounters. The sub-Alfvénic datasets sampled in these earlier encounters provided a first opportunity to examine the sub-Alfvénic properties of the upper corona or sub-Alfvénic solar wind.

Here, we extend that study to datasets from encounters E8 to E19, a significantly longer period that now spans from near solar minimum to near solar maximum. These later orbits probe lower altitudes and experience sub-Alfvénic conditions more frequently. With greater statistical coverage, we are able to better contrast the sub- and super-Alfvénic solar wind conditions. Furthermore, by isolating the intervals where the solar wind is trans-Alfvénic (i.e., Alfvén Mach number $M_A \simeq 1$), we explore the transition in more detail. In particular, we analyze solar wind in categories defined as sub-Alfvénic $M_A < 0.85$, trans-Alfvénic $0.85 \le M_A \le 1.15$, and super-

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Alfvénic $M_A > 1.15$ regimes and study their properties. Note that using a narrower range for the trans-Alfvénic regime, such as $0.9 \le M_A \le 1.1$, yields qualitatively similar results.

We note that other studies have also explored the trans-Alfvén transition (G. Zank et al. 2022; L.-L. Zhao et al. 2022; Y. Jiao et al. 2023), adopting strategies that are complementary to ours. The paper is organized along the lines of R. Bandyopadhyay et al. (2022). Section 2 describes the datasets employed. Section 3 provides details of the results on turbulence amplitudes (R. Bruno & V. Carbone 2013), variance anisotropy (S. Oughton et al. 2016), intermittency measured using partial variance of increment (PVI) methods (A. Greco et al. 2018), and occurrence rate of the switchback parameter (T. D. de Wit et al. 2020). Finally, in Section 4, we present our conclusions and discuss the implications.

## 2. Data During Encounters 8–19

PSP magnetic-field ($\mathbf{B}$) data are obtained from the flux-gate magnetometer on the FIELDS instrument suite (S. Bale et al. 2016, 2019, 2020). The proton radial velocities ($V_R$) in the inertial frame are obtained from the Solar Probe ANalyzer for Ions (SPAN-I) on the SWEAP instrument suite (J. C. Kasper et al. 2016, 2019; R. Livi et al. 2020). The FIELDS quasi-thermal noise (QTN) electron densities ($n_e$) are computed from the QTN spectrum (O. Romeo et al. 2023) measured by the Radio Frequency Spectrometer onboard PSP (M. Moncuquet et al. 2020). The use of QTN data is recommended due to potential problems with secondary electrons and photoelectrons in the measurement by the alternative instrument SPAN-E (O. Romeo et al. 2023). With these measurements, the local Alfvén speed is calculated as $V_A = |\mathbf{B}|/\sqrt{\mu_0 m_p n_e}$, where $\mu_0$ is the magnetic permeability of a vacuum and $m_p$ is the proton mass. Finally, the local Alfvén Mach number is estimated as $M_A = V_R/V_A$ with a resolution of 60 s.<fnref n="10" /> The data are then divided into sub-Alfvénic, trans-Alfvénic, and super-Alfvénic regions as intervals in which the sampled data satisfy the criteria $M_A < 1$, $0.85 \le M_A \le 1.15$, and $M_A > 1$, respectively, each lasting for a duration of 10 minutes. Further, all the calculations involving the magnetic field use a dataset of four samples per cycle, i.e., an approximate time resolution of ~0.22 s, which are later resampled to 60 s cadence to map with the Alfvén Mach number $M_A$ using a rolling average over a bin of one-minute width.

A graphical overview of the datasets collected in each encounter is given in the Appendix, with the start and end times for each encounter listed in Table 2 (see Appendix). In Figure 1, we compare the total datasets available in these encounters with the subset chosen for the analysis. For the purpose of this study, we use a total of four days of data for each encounter around the perihelion (two days on either side) to create a comparable sample size for both sub- and super-Alfvénic datasets and avoid statistical bias. Note that almost all of the sub-Alfvénic periods sampled by the PSP during these encounters have been included, as shown in the bar chart embedded in Figure 1. Many super-Alfvénic periods distant from the Sun are excluded from our analysis.

<figure><img src="image_4.png" /><figcaption>**Figure 1.** Comparison of distributions of the Mach number $M_A$ for the analyzed dataset shown in Figure 6, with the total datasets in encounters 8–19. The dataset is divided into sub-Alfvénic ($M_A < 0.85$), trans-Alfvénic ($0.85 \le M_A \le 1.15$), and super-Alfvénic ($M_A > 1.15$) samples by the red vertical dashed lines. Embedded: comparison of the number of datasets in each category (sub-Alfvénic, trans-Alfvénic, and super-Alfvénic).</figcaption></figure>

## 3. Results

### 3.1. Turbulence Amplitude

First, we examine the amplitude of the magnetic-field fluctuations. In Figure 2(a), we show the probability distribution function (PDF) of the turbulence amplitude, calculated as $\delta B = \sqrt{\langle |\mathbf{B}(t) - \langle \mathbf{B} \rangle|^2 \rangle}$, where $\langle \cdots \rangle$ is a time average, over some chosen time range, usually several correlation times. Throughout this paper, we choose averaging intervals of 10 minutes (on the order of a few correlation times; see T. Parashar et al. (2020)) and evaluate the turbulence amplitude in each interval. Then, we separately accumulate the intervals classified as sub-Alfvénic, trans-Alfvénic, or super-Alfvénic. In Figure 2(a), we show the frequency of occurrence of values of $\delta B$ (in nT) for super-Alfvénic and sub-Alfvénic intervals. We exclude the PDF of $\delta B$ near unit Alfvén Mach number to clearly distinguish the two populations. This result differs from that of G. Zank et al. (2022), R. Bandyopadhyay et al. (2022). In particular, we cannot conclude that the sub-Alfvénic period has systematically lower $\delta B$ as was reported in that earlier study. In fact, there is a suggestion of a second population of sub-Alfvénic contributions with corresponding $\text{Log}_{10}(\delta B) \sim 2.2$. Inspection of the results from each encounter (not shown) indicates that this feature is mainly due to several encounters, such as E12, E17, E18, and E19. Therefore, this feature is likely mainly due to the influence of the solar maximum. Most of the other encounters are more consistent with the conclusions in R. Bandyopadhyay et al. (2022), which occurred near solar minimum and showed no secondary peak. Fluctuations in the radial magnetic field ($B_r$) are also small for sub-Alfvénic intervals and follow R. Bandyopadhyay et al. (2022). But the fluctuations in the normal ($B_n$) and tangential ($B_t$) magnetic fields are larger in the sub-Alfvénic intervals. Since the contribution to this secondary peak is mainly due to later encounters, we may hypothesize that larger amplitude transverse fluctuations are to be found in regions closer to the Sun and perhaps inside the region of complex trans-Alfvénic transitions (R. Bandyopadhyay et al. 2022). Because of this potential complication, we examine now the turbulence amplitude parameter $\delta B/B$, where $B$ is the average of the

<footnote n="10">¹⁰ The use of radial velocity in Mach number calculations is due to the fact that (i) the propagation of the solar wind is mostly in the radial direction, (ii) the limited field of view of the SPAN-I instrument provides incomplete sampling of proton velocity distributions, making the velocity measurements in the other direction not reliable, (iii) the radial velocity is often the dominant contribution, such that $V_A \approx |V|$.</footnote>
<figure><img src="image_5.png" /><figcaption>**Figure 2.** PDF of (a) the magnetic-field turbulence amplitude (in nT) and (b) the normalized magnetic-field turbulence amplitude in the sub-Alfvénic and super-Alfvénic solar wind intervals as observed by the PSP in encounters 8–19.</figcaption></figure>

<table><caption>**Table 1**
Summary of Average (Blue) and Most Probable (Black) Values of the Parameters Described in the Results Section</caption><thead><tr><th></th><th></th><th>Sub-Alfvénic</th><th>Trans-Alfvénic</th><th>Super-Alfvénic</th></tr></thead><tbody><tr><td>Turbulence</td><td>B/B</td><td>0.18</td><td>0.22</td><td>0.30</td></tr><tr><td>Amplitude</td><td>B/B</td><td>0.20</td><td>0.24</td><td>0.38</td></tr><tr><td>Variance</td><td>A&#773;<sub>b</sub></td><td>51.09</td><td>31.33</td><td>20.16</td></tr><tr><td>Anisotropy</td><td>A&#773;<sub>b</sub></td><td>56.23</td><td>28.18</td><td>13.18</td></tr><tr><td>Partial Variance of Increment</td><td>PVI/PVI&#773;</td><td>0.75<br>0.62</td><td>0.75<br>0.48</td><td>0.75<br>0.40</td></tr><tr><td>Switchback Parameter</td><td>z&#773;<br>z&#773;</td><td>0.012<br>0.004</td><td>0.015<br>0.008</td><td>0.027<br>0.012</td></tr></tbody></table>

**Note.** We show the sub-Alfvénic ($M_A < 0.85$), trans-Alfvénic (0.85≤$M_A$ ≤ 1.15), and super-Alfvénic ($M_A > 1.15$) solar wind samples.

magnitude of the magnetic field, i.e., $B = \langle |B| \rangle$ over the same 10 minute interval as Figure 2(a).

In Figure 2(b), we show the frequency of occurrence of the normalized turbulence level (in logarithm), for the sub- and super-Alfvénic intervals. Clearly, the normalized turbulence amplitudes for the super-Alfvénic samples are larger relative to the sub-Alfvénic intervals. The most probable values of $\delta B/B$ for the sub-Alfvénic and super-Alfvénic wind are 0.20 and 0.38, respectively, indicating stronger turbulence in super-Alfvénic samples compared with sub-Alfvénic samples. Similarly, the average value of the normalized turbulence amplitude for the sub-Alfvénic solar wind is 0.18 with a standard deviation $\sigma = 0.10$. For the super-Alfvénic case, the average value of the normalized turbulence amplitude is 0.30 with $\sigma = 0.17$. While the trans-Alfvénic datasets are not included in these plots, the average and most probable values lie between the two regimes and are listed in Table 1 for comparison.

## 3.2. Variance Anisotropy

In this subsection, we examine the variance anisotropy (S. Oughton et al. 2016; T. N. Parashar et al. 2016) of the

magnetic field. Variance anisotropy measures the departure from equipartition of the magnetic-field fluctuation energies in each Cartesian component. For variance isotropy, $\langle b_x^2 \rangle = \langle b_y^2 \rangle = \langle b_z^2 \rangle$. In a Cartesian coordinate system, a measure of variance anisotropy is often defined as

$$A_b = \frac{\langle b_x^2 + b_y^2 \rangle}{\langle b_z^2 \rangle}, \qquad (1)$$

where the mean magnetic field is chosen to be along the z-axis and the magnetic fluctuations are $b = (b_x, b_y, b_z)$. For an isotropic distribution of magnetic-field components, the variance anisotropy takes on a value of $A_b = 2$.

The variance anisotropy $A_b$ is calculated in each interval of 10 minutes duration for the PSP datasets described in Section 2. The histograms of these magnetic-field anisotropies for the sub-Alfvénic and super-Alfvénic periods are shown in Figure 3. Again, the trans-Alfvénic intervals are excluded from the histograms for clarity, with the average and most probable values shown in Table 1.

Clearly, all these three selected regimes of the solar wind are highly anisotropic compared to isotropy, for which one would expect $A_b \sim 2$, and $\text{Log}_{10}(A_b) \sim 0.3$. However, the sub-Alfvénic solar wind periods display the strongest variance anisotropy of the three groups. The super-Alfvénic samples show the least variance anisotropy. From these distributions, one can also find the most probable value of the variance anisotropy $\tilde{A}_b = 56.23$ for the sub-Alfvénic case and $\tilde{A}_b = 13.18$ for the super-Alfvénic case. Similarly, the average value of the variance anisotropy for the sub-Alfvénic case is $\tilde{A}_b = 51.09$ with a standard deviation of $\sigma_{\tilde{A}_b} = 65.50$. For the super-Alfvénic samples, $\tilde{A}_b = 20.16$ with $\sigma_{\tilde{A}_b} = 30.33$. The degree of variance anisotropy is clearly a distinguishing feature of the sub-Alfvénic corona as explored by PSP.

## 3.3. Intermittency

Intermittent signatures can be partially quantified by the PVI method, a measure that detects the occurrence of sharp gradients in quantities such as the magnetic field (A. Greco et al. 2008, 2018). Note that the PVI is sensitive to discontinuities in both the magnitude and vector rotation.
<figure><img src="image_6.png" /><figcaption>**Figure 3.** PDF of variance anisotropy in the sub-Alfvénic and super-Alfvénic solar wind intervals observed by PSP in encounters 8–19. The vertical solid (black) line indicates the value $A_b = 2$, which corresponds to an isotropic distribution.</figcaption></figure>

The PVI of the magnetic field **B** for a time lag of $\tau$ at a time $t$ is defined as

$$ \text{PVI}_{t,\tau} = \frac{|\Delta\mathbf{B}(t, \tau)|}{\sqrt{\langle |\Delta\mathbf{B}(t, \tau)|^2 \rangle}}, \qquad (2) $$

where the temporal increment of the magnetic field **B** is defined as $\Delta\mathbf{B}(t, \tau) = \mathbf{B}(t+\tau) - \mathbf{B}(t)$.

Here, PVI is computed using a moving average of 10 minutes, denoted by $\langle \dots \rangle$, with a time lag of $\tau \approx 0.87$ s. Figure 4 shows the distribution of values of PVI for the selected samples of sub- and super-Alfvénic solar wind. While the turbulence amplitude and variance anisotropy for the sub- and super-Alfvénic samples, as shown above, have distinctly different features, the PVI distributions separated in these two categories are very similar and display no distinct trends, consistent with the findings of R. Bandyopadhyay et al. (2022). The average value of PVI for both the sub- and super-Alfvénic cases is 0.75. Likewise, the most probable values of PVI for both samples are approximately 0.5, suggesting the close similarities between these samples with regard to PVI values.

### 3.4. Magnetic Switchbacks

Deflections of the magnetic-field vector, including the phenomenon of “switchbacks,” may be quantified following T. D. de Wit et al. (2020) by the parameter

$$ z = \frac{1}{2}(1 - \cos \alpha), \qquad (3) $$

where

$$ \cos \alpha = \frac{\mathbf{B} \cdot \langle \mathbf{B} \rangle}{|\mathbf{B}| \langle \mathbf{B} \rangle|} $$

and the brackets denote a suitable local or regional average. Here, we again use an averaging interval of 10 minutes. The $z$ variable admits values between 0 and 1. Values of $z > 1/2$ indicate that the field is in a polarity-reversed state, and lower values correspond to “background” magnetic polarity. In the present analysis, the term “switchback” refers exclusively to deflections that reverse the polarity, i.e., $z > 1/2$.

<figure><img src="image_7.png" /><figcaption>**Figure 4.** PDF of PVI in the sub-Alfvénic and super-Alfvénic solar wind intervals observed by PSP in encounters 8–19. Values of PVI > 2.5 represent the non-Gaussian and coherent structures, such as current sheets.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 5.** PDF of switchback parameter ($z$) in the sub- and super-Alfvénic solar wind intervals observed by PSP in encounters 8–19. The vertical solid (black) line indicates the value $z = 1/2$, which corresponds to a marginal reversal of polarity of the magnetic field. Higher $z$ values represent a stronger switchback.</figcaption></figure>

Figure 5 shows the histograms of the switchback parameter $z$ (in logarithm) for the sub- and super-Alfvénic periods. Clearly, the magnetic deflections, as characterized by the $z$ parameter, are smaller in the sub-Alfvénic periods than in the super-Alfvénic parts, and the deflections in the trans-Alfvénic samples lie between these. (As found by R. Bandyopadhyay et al. (2022); see also V. K. Jagarlamudi et al. (2023) for similar results.) The most probable value of the switchback parameter $\bar{z}$ for the sub-Alfvénic sample is 0.004, while for the Alfvénic and super-Alfvénic cases, the most probable values $\bar{z}$ are 0.008 and 0.012, respectively. Similarly, the mean value of the switchback parameter $\bar{z}$ for sub-Alfvénic solar wind is 0.012 with a standard deviation $\sigma_z = 0.035$. For the super-Alfvénic solar wind $\bar{z} = 0.027$ with $\sigma_z = 0.061$.

### 4. Discussions and Conclusions

As PSP orbits descended more deeply into the solar atmosphere, the occurrence rate of sub-Alfvénic intervals has sharply increased, being less than 10% at 30–40 $R_s$ and increasing to >70% at 14 $R_s$ (R. Chhiber et al. 2024). Indeed, in more recent orbits, PSP has sampled extended periods of
sub-Alfvénic solar wind. Comparing the sub-Alfvénic periods with the trans-Alfvénic and super-Alfvénic periods (see Table 1), we find that in the sub-Alfvénic solar wind: (i) the normalized turbulence amplitude decreases; (ii) variance anisotropy increases; (iii) intermittency as measured by PVI is almost unchanged; and (iv) the angular deflections of the magnetic field are, on average, weakening, indicating the lack of switchbacks (D. Ruffolo et al. 2020). Overall, our results are consistent with the initial study by R. Bandyopadhyay et al. (2022). We note, however, that the present study incorporates much better statistical coverage of the sub-Alfvénic samples. The present study also consistently extends the prior findings to periods of higher solar activity.

Once again, we can see that the sub-Alfvénic wind more closely resembles properties that one might associate with coronal conditions. Often, the corona is modeled based on properties expected for low beta, highly anisotropic plasma, which may be dominated by incompressive fluctuations such as Alfvénic fluctuations (or waves). A typical coronal model of this type is based on reduced magnetohydrodynamics (RMHD) as employed in, e.g., G. Einaudi & M. Velli (1999), D. O. Gomez et al. (2000), and S. Oughton et al. (2001). In contrast, the super-Alfvénic solar wind is much less anisotropic and is typically modeled in the fluid regime by compressible MHD models without the reduction in dimensionality leading to RMHD. In this regard, it has been shown that the validity of the RMHD approximation is particularly sensitive to the presence of component variance in the direction parallel to the regional mean magnetic field (P. Dmitruk et al. 2005). From this perspective it appears that, as it approaches the Sun, PSP is sampling not only a complex transition from super-Alfvénic to sub-Alfvénic wind (R. Chhiber et al. 2024), but also a transition from a large plasma beta, more fully three-dimensional compressible MHD plasma (at scales much larger than the ion inertial scale) to a plasma better described as highly anisotropic,

less compressible and described by Reduced MHD. We note that the anisotropy measure shown in Figure 3 is essentially the reciprocal of the quantity sometimes called magnetic compressibility (K. H. Kiyani et al. 2012); its behavior is consistent with greater compressibility in the super-Alfvénic regime. Other signatures of this transition, such as the presence of corotation as a signature of magnetic dominance in the corona, are anticipated (E. J. Weber & L. Davis 1967; J. C. Kasper et al. 2019; R. Chhiber et al. 2025).

## Acknowledgments

This work is supported at the University of Delaware in part by the PSP/ISOIS project through subcontract SUB0000165 from Princeton to the University of Delaware, and by the PUNCH project under subcontract N99054DS. J.G. is supported by the Delaware NASA Space Grant program grant number 80NSSC20M0045 at the University of Delaware. A.U., R.C., and S.A. are partially supported in part by NASA under grant No. 80NSSC22K1639. R.C. is also supported by NASA grant No. 80NSSC22K1020. This research is also supported in Thailand by the National Science and Technology Development Agency (NSTDA) and the National Research Council of Thailand (NRCT) through the High-Potential Research Team Grant Program (N42A650868).

## Appendix

Here, we provide an overview of the PSP datasets from encounters 8–19. In Figure 6, the datasets are shown characterized based on Alféven Mach number. Sub- and super-Alfvénic data samples are represented by the shaded regions with lighter shades of blue and red, respectively, with an additional gray region separating the trans-Alfvénic dataset ($0.85 \le M_A \le 1.15$). The dates of the intervals from each encounter used in this study are given in Table 2.

<figure><img src="image_9.png" /><figcaption>**Figure 6.** An overview of the Alfvén Mach number $M_A = V_R/V_A$, where $V_A$ is the Alfvén speed, as observed by the PSP for encounters 8–19. Sub- and super-Alfvénic intervals are shown by the shaded regions colored light blue and light red, respectively. In addition, the shaded gray region extending $0.85 \le M_A \le 1.15$ represents what we have termed the “trans-Alfvénic dataset” isolated to contrast the behavior between sub- and super-Alfvénic solar wind.</figcaption></figure>
<caption>Table 2</caption>

Start and End Times of the Four Days of the Dataset (Centered at Perihelion)
Analyzed for PSP Encounters 8–19

<table><thead><tr><th>Enc</th><th>Start (UTC)</th><th>End (UTC)</th></tr></thead><tbody><tr><td>8</td><td>2021-04-27 06:00:30</td><td>2021-05-01 06:00:30</td></tr><tr><td>9</td><td>2021-08-07 18:00:30</td><td>2021-08-11 18:00:30</td></tr><tr><td>10</td><td>2021-11-19 09:00:30</td><td>2021-11-23 09:00:30</td></tr><tr><td>11</td><td>2022-02-23 12:00:30</td><td>2022-02-27 12:00:30</td></tr><tr><td>12</td><td>2022-05-31 00:00:30</td><td>2022-06-04 00:00:30</td></tr><tr><td>13</td><td>2022-09-04 06:00:30</td><td>2022-09-08 06:00:30</td></tr><tr><td>14</td><td>2022-12-09 12:00:30</td><td>2022-12-13 12:00:30</td></tr><tr><td>15</td><td>2023-03-15 21:00:30</td><td>2023-03-19 21:00:30</td></tr><tr><td>16</td><td>2023-06-20 00:00:30</td><td>2023-06-24 00:00:30</td></tr><tr><td>17</td><td>2023-09-26 00:00:30</td><td>2023-09-30 00:00:30</td></tr><tr><td>18</td><td>2023-12-27 00:00:30</td><td>2023-12-31 00:00:30</td></tr><tr><td>19</td><td>2024-03-28 00:00:30</td><td>2024-04-01 00:00:30</td></tr></tbody></table>

ORCID iDs

Subash Adhikari https://orcid.org/0000-0003-2160-7066

Riddhi Bandyopadhyay https://orcid.org/0000-0002-6962-0959

Joshua Goodwill https://orcid.org/0000-0002-5354-1164

William H. Matthaeus https://orcid.org/0000-0001-7224-6024

David Ruffolo https://orcid.org/0000-0003-3414-9666

Panisara Thephthong https://orcid.org/0000-0001-9597-1448

Peera Pongkitiwanichakul https://orcid.org/0000-0002-6609-1422

Sohom Roy https://orcid.org/0000-0003-3891-5495

Francesco Pecora https://orcid.org/0000-0003-4168-590X

Rohit Chhiber https://orcid.org/0000-0002-7174-6948

Rayta Pradata https://orcid.org/0009-0005-9366-6163

Arcadi Usmanov https://orcid.org/0000-0002-0209-152X

Michael Stevens https://orcid.org/0000-0002-7728-0085

Samuel Badman https://orcid.org/0000-0002-6145-436X

Orlando Romeo https://orcid.org/0000-0002-4559-2199

Jiaming Wang https://orcid.org/0009-0008-8723-610X

Melvyn L. Goldstein https://orcid.org/0000-0002-5317-988X

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