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What Can DKIST/DL-NIRSP Tell Us About Quiet-Sun Magnetism?

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read The paper claims that emulated DKIST/DL-NIRSP observations recover only about 71.4% and 52.6% of the ground-truth unsigned vertical Poynting flux at two photospheric heights, and that the net flux is underestimated largely because…

desk verdict Solid forward-modeling assessment of DKIST/DL-NIRSP for quiet-Sun Poynting flux, but the headline recovery fractions don't match the paper's own Table 2. read the letter →

arxiv 2411.18735 v1 pith:DH54MOOC submitted 2024-11-27 astro-ph.SR

Jiayi Liu (1) , Xudong Sun (1) , Peter W. Schuck (2) , Sarah A. Jaeggli (3) ((1) Institute for Astronomy , University of Hawaii at Manoa , (2) Heliophysics Science Division , NASA Goddard Space Flight Center , (3) National Solar Observatory) This is my paper · ORCID
classification astro-ph.SR PACS 96.60.-j95.30.Qd
keywords quietSunDKISTDL-NIRSPPoyntingfluxspectropolarimetryStokesinversionflowtrackingsolarmagneticheating
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

The paper asks a practical question: once the 4-m DKIST telescope with its DL-NIRSP spectropolarimeter looks at the quiet Sun, how much of the magnetic energy flowing through the photosphere will the data actually show? To find out, the authors run a realistic MURaM magnetoconvection simulation through the full observational chain—synthesizing Fe I 630 nm Stokes profiles, degrading them to DL-NIRSP resolution, inverting them with SIR, and tracking velocities with DAVE4VMwDV—then compare the inferred vertical Poynting flux with the simulation's ground truth. Their headline result is that the unsigned vertical Poynting flux is recovered at about 71.4% at the photosphere ($\log\tau=0$) and 52.6% one scale height higher ($\log\tau=-1$), while the net flux is substantially underestimated and can even flip sign. The loss is traced mainly to the shearing term, which depends on horizontal magnetic-field components and horizontal velocities, and which the flow-tracking step systematically under-recovers. This matters because the quiet Sun covers most of the solar surface and its magnetic fields are a candidate energy source for heating the chromosphere and corona, so the answer determines what can be concluded from DL-NIRSP data about the Sun's energy budget.

What carries the argument

The machinery is a forward-model pipeline with a built-in ground truth: the MURaM magnetoconvection simulation provides the atmosphere, SIR is used twice (forward synthesis of Stokes profiles and inverse retrieval of vector fields), DL-NIRSP's point spread function and pixel scale degrade the spectra, and DAVE4VMwDV estimates the three-dimensional photospheric velocity by minimizing the residual of the ideal induction equation $\partial B_z/\partial t = -\nabla_h\cdot(B_z v_h - B_h v_z)$ together with a Doppler-velocity penalty. The vertical Poynting flux is then computed as $S_z = (1/4\pi)\int (B_h^2 v_z - (B_h\cdot v_h)B_z)\,dS$ and split into an emergence term and a shearing term. The shearing term is the key diagnostic: because it is bilinear in the horizontal magnetic field and horizontal velocity, it is the term most sensitive to azimuth-disambiguation errors and to the systematic underestimation of horizontal flows, and the paper isolates it as the main source of missing flux.

What would settle it

Rerun the same SIR plus DAVE4VMwDV pipeline on the same MURaM quiet-Sun frames after adding photon noise at the DL-NIRSP design level (e.g., $\sigma\approx 10^{-3}$ of continuum intensity) and compare the recovered unsigned and net Poynting flux fractions. If the fractions stay within a few percent of 71.4% and 52.6%, the missing flux is dominated by the underestimated horizontal velocity; if they drop substantially, the noiseless assumption is the controlling limitation.

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Extended reading notes

Core claim

Working on a quiet-Sun magnetoconvection simulation with a mean flux density of about 120 G, the authors synthesize Stokes profiles of the Fe I 630 nm lines, convolve them with the DL-NIRSP point spread function, rebin to 0.03 arcsec pixels, and invert with SIR to obtain vector magnetic fields at constant optical depth. Velocities come from DAVE4VMwDV, a flow-tracker that fits the ideal induction equation for the vertical field while constraining the line-of-sight component to the measured Doppler velocity. The paper claims that this pipeline recovers 71.4% and 52.6% of the ground-truth unsigned vertical Poynting flux at $\log\tau=0.0$ and $\log\tau=-1.0$, but that the net vertical Poynting flux is strongly underestimated; in the emulated case at $\log\tau=0$ the inferred net flux has the wrong sign ($-1.1\times10^7$ versus $+3.7\times10^6\,\mathrm{erg\,cm^{-2}\,s^{-1}}$). Decomposition into an emergence term ($B_h^2 v_z/4\pi$) and a shearing term ($-(B_h\cdot v_h)B_z/4\pi$) shows the emergence term is recovered almost exactly on simulation magnetograms (99.6% and 88.8% of the net values), whereas the shearing term is the weak link (72.0% and 55.5%), and on the emulated data the signed shearing term drops to roughly 17% of the reference at $\log\tau=0$. The horizontal velocity field is the main culprit: DAVE4VMwDV reproduces large-scale (above roughly 150–200 km) flows well, but systematically underestimates flow amplitudes, and the shortfall worsens as cadence degrades, with an effective constraint $\Delta x/\Delta t > \langle v\rangle$ for reliable velocity estimates. The authors emphasize that photon noise was deliberately omitted, so these fractions should be read as upper limits on what the instrument chain can deliver.

Load-bearing premise

The load-bearing premise is that photon-free synthetic Stokes profiles stand in for real DL-NIRSP observations; the paper explicitly omits noise, and quiet-Sun polarization signals are weak, so actual recovery will likely be worse than the quoted fractions.

Editorial extensions

If this is right

  • For DKIST/DL-NIRSP quiet-Sun campaigns, unsigned vertical Poynting flux measured with SIR plus DAVE4VMwDV should be treated as a lower limit, with the missing fraction concentrated in the shearing contribution from horizontal motions at scales below a few hundred kilometers.
  • Estimates of the net vertical Poynting flux, and any conclusion about net upward versus downward magnetic energy transport, are not reliable with current schemes: the sign itself can be wrong at photospheric heights.
  • Velocity products from DAVE4VMwDV are trustworthy only at scales larger than roughly 150–200 km; users should not interpret small-scale flow structure recovered by the algorithm as real.
  • Cadence planning matters: to keep velocity estimates accurate, magnetogram cadence should satisfy $\Delta x/\Delta t > \langle v\rangle$, corresponding to roughly 2.5 s or better for 16 km pixels, which DL-NIRSP's several-second cadence can approach but may not always reach.
  • Improvements that place inversions on constant geometric height, such as magnetohydrostatic inversions or deep-learning methods, can recover an additional 10–20 percentage points of the shearing term, so the missing flux is a recoverable systematic rather than an intrinsic ceiling.

Reading between the lines

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

  • Inference: because the pipeline is noiseless, the 71.4% and 52.6% recovery fractions are optimistic ceilings; real DL-NIRSP data with photon noise on weak quiet-Sun polarization will likely recover a smaller fraction, and the net flux sign may be even less trustworthy.
  • Inference: the paper's scale analysis implies that a correction function could be calibrated from simulation—recovery fraction as a function of pixel size, cadence, and noise level—and applied to real DL-NIRSP observations in future quiet-Sun energy-budget studies.
  • Inference: the specific failure mode, azimuth ambiguity corrupting $v_h\cdot B_h$, suggests that multi-height or multi-line inversions that resolve the 180-degree ambiguity without a smoothness prior would be the single highest-leverage upgrade over the current scheme.
  • Inference: the result generalizes beyond DKIST: any spectropolarimetric quiet-Sun Poynting-flux measurement using similar flow-tracking will be biased low, so comparisons between instruments such as Hinode, SUNRISE, and DKIST should be made on recovery-corrected quantities rather than raw fluxes.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. This paper assesses the diagnostic capability of DKIST/DL-NIRSP for quiet-Sun magnetism by constructing an end-to-end pipeline: synthetic Stokes profiles from a MURaM simulation are degraded to emulate DL-NIRSP observations, inverted with SIR to obtain vector magnetic fields, processed with the DAVE4VMwDV algorithm to estimate photospheric velocities, and then used to compute vertical Poynting flux. The authors validate the velocity inference on high-resolution simulation data, investigate dependence on cadence, and evaluate the recovery of Poynting flux from the emulated observations. The central quantitative claim is that the unsigned Poynting flux can be recovered at about 71.4% (log τ=0.0) and 52.6% (log τ=-1.0) of the ground truth, with the net flux strongly underestimated and the shearing term identified as the main source of error. The paper also provides scale-based guidance for required velocity resolution and discusses limitations due to the constant-τ surface, azimuthal disambiguation, and the absence of photon noise.

Significance. If the quantitative results were internally consistent, this would be a valuable end-to-end assessment of what DKIST/DL-NIRSP can deliver for quiet-Sun energy transport studies. The pipeline uses an independent MHD simulation as ground truth, explicitly analyzes the limitations of constant-τ magnetograms for flow tracking, and offers concrete guidance on required spatial and temporal scales for recovering the emergence and shearing terms of the Poynting flux. The paper is also transparent about many known limitations, such as the noiseless assumption and the poor performance of the ME0 disambiguation. However, the central recovery percentages are mutually inconsistent across the abstract, main text, and conclusion, and they do not follow from Table 2. Until these numbers are reconciled, the paper's headline quantitative conclusion is not reproducible from the presented evidence.

major comments (3)
  1. [Abstract, Section 4.2, Section 6, Table 2] The net Poynting flux values quoted in the conclusion differ from Table 2: the conclusion lists -1.3×10^7 and -8.5×10^6 erg cm^-2 s^-1, while Table 2 gives -10.5×10^6 and -8.6×10^6. This is another instance of the same numerical inconsistency.
  2. [Section 2.2, Section 5.4] The noiseless emulation is an acknowledged but understated limitation. Section 2.2 states 'We did not add noise to the synthetic spectra for this study,' and Section 5.4 defers noise effects to future work. For quiet-Sun Stokes Q, U, and V signals, photon noise is expected to be a dominant error source, so the quoted recovery percentages are optimistic upper limits rather than realistic capability estimates. The abstract and conclusion present these numbers without this caveat. The authors should either add a noise sensitivity analysis (e.g., a single representative noise level) or explicitly qualify the headline numbers as noiseless, idealized recoveries.
  3. [Appendix A.2] The DAVE4VMwDV free parameters (λ, d, dr, w) are optimized on the same emulated observation data that are then used to evaluate the Poynting flux recovery. This in-sample tuning means the reported recovery fractions are not out-of-sample estimates; the L-curve and L1-minimization procedures in Appendix A select parameters that best fit the specific realization. The paper should demonstrate robustness to parameter choices, for example by showing the range of recovery fractions across a plausible parameter grid or by fixing the parameters a priori based on the simulation-data experiments in Appendix A.1.
minor comments (5)
  1. [Equation (9)] The definition of E_rel appears malformed: the numerator should be the Euclidean norm of the difference vector and the denominator should be the product of the vector magnitudes, not the dot product as written. As printed, the denominator can be zero or negative, making the metric ill-defined.
  2. [Section 1] In the first paragraph, 'Goiet al. 2014, 2016' is missing a space; it should read 'Goi et al. 2014, 2016'.
  3. [Section 4.2] The text states 'The inferred unsigned Poynting flux is 2.0 × 10^8 ergs cm^-2 s^-1', but Table 2 gives 150.5 × 10^6 = 1.505 × 10^8 erg cm^-2 s^-1. The value 2.0 × 10^8 is inconsistent by about 33%.
  4. [Section 4.2] The percentage for the unsigned shearing term is quoted as 44.6% of the ground truth, while Table 2 implies 95.2/201.1 = 47.3%. Please check the calculation.
  5. [Section 3.1 and Figure 4 caption] The phrase 'Here and after' should be 'Hereafter' for clarity.

Circularity Check

1 steps flagged · score 2.0 of 10

No constructional circularity: recovery fractions are benchmarked against independent MURaM truth; the only circularity-adjacent burden is a co-author's in-preparation flow-tracking code with in-sample parameter tuning, plus a separate internal numerical inconsistency in the headline percentages.

  1. other [Section 2.4 (Eq. 6) and Appendix A]
    "Termed DA VE4VMwDV (with Doppler Velocity; P. W. Schuck 2024, in preparation), the global loss function L now reads ... In this work, we optimize these parameters using the methods described in Liu et al. (2023)."

    The flow-tracking step that supplies the velocities for every downstream Poynting-flux number is an unpublished algorithm attributed to a co-author, and its free parameters are selected by minimizing the loss on the same emulated data used for the capability assessment. This makes the reported recovery fractions in-sample and potentially optimistic, and it places a self-citation burden on the method's provenance. It is not a full constructional circularity because the comparison ground truth comes from the independent MURaM simulation, so the recovery fractions are not equated to a fitted input by definition.

full rationale

The derivation chain is self-contained against an external benchmark. MURaM provides ground-truth B and v; SIR synthesizes and inverts the Stokes profiles; DAVE4VMwDV estimates the velocity from the magnetogram/Dopplergram sequence; Equation (8) computes the Poynting flux; and every estimate is compared with the simulation truth. The induction equation is used as a physical constraint rather than as a fit to the Poynting-flux target, so the central result does not reduce to its inputs by construction. The noiseless-emulation caveat is explicitly stated in Section 2.2 ('We did not add noise to the synthetic spectra for this study'), making the numbers optimistic limits rather than full capability bounds; this is a limitation, not a circular step. The minor circularity-adjacent concern is the in-preparation, co-authored DAVE4VMwDV and in-sample hyperparameter selection, which is why the score is not 0. Separately, the paper has an internal numerical inconsistency: the abstract's 71.4%/52.6% unsigned-Poynting recovery fractions do not match Table 2's emulation rows (150.5/278.9 = 53.9% and 36.2/75.7 = 47.8%), while the conclusion reports 72.5%/61.3%; this is a reproducibility/correctness defect that should be fixed but is not itself circularity.

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

The central claim rests on a chain of modeling choices: the MURaM run as ground truth, SIR inversion assumptions (LTE, hydrostatic equilibrium, node interpolation), the ideal induction equation for velocity tracking on constant-tau surfaces, ME0 disambiguation, and the decision to omit noise. The quantitative recovery fractions also depend on algorithm hyperparameters (window size, Legendre degrees, Doppler weighting, ME0 lambda, SIR node grids) that were tuned on the same data. No new physical entities are introduced.

free parameters (5)
  • DAVE4VMwDV window size w = 15 pixels for all runs
    Window size for local velocity estimation, selected via L-curve optimization in Appendix A.
  • DAVE4VMwDV Legendre degrees (d, dr) = (5,7) at log tau=0 and (3,5) at log tau=-1 for MHD data; (3,5) and (1,5) for emulated data
    Degrees of polynomial expansion for horizontal and vertical velocity, optimized by minimizing the induction-equation loss on the same data.
  • DAVE4VMwDV Doppler weighting lambda = 1.0 for MHD data, 0.35 for emulated data
    Controls trade-off between induction-equation residual and Doppler velocity constraint; chosen from L-curves.
  • ME0 disambiguation weighting lambda = 0.5
    Relative weight of vertical current in the minimum-energy azimuth disambiguation; chosen because it gives a smooth solution and affects the shearing Poynting flux sign.
  • SIR node configuration = Table 1: 5 nodes for magnetic field, inclination, azimuth in final cycles; experiment two uses 14, 14, 5
    The node grid controls how faithfully the inversion can represent the atmospheric stratification; the optimal number of nodes is not known a priori and affects recovered magnetic fields.
assumptions (5)
  • domain assumption MURaM O16bM simulation is a realistic representation of quiet-Sun magnetoconvection
    Ground truth for all comparisons; if the simulation's small-scale field and flow structure is wrong, the assessed capability does not transfer to the Sun. Used throughout Section 2.1.
  • domain assumption SIR inversion under LTE and hydrostatic equilibrium recovers depth-dependent atmospheric parameters
    SIR assumes LTE and hydrostatic equilibrium and lacks an absolute geometric height scale; constant-tau maps are treated as constant-height slices. Acknowledged in Section 2.2.
  • domain assumption The ideal induction equation holds on constant-tau surfaces for velocity inference
    Central to DAVE4VMwDV; the paper shows the two sides of the induction equation disagree below roughly 120 to 150 km because tau surfaces are corrugated and numerical diffusion is present (Section 5.1, Figure 15).
  • domain assumption ME0 minimum-energy disambiguation selects correct azimuths in the quiet Sun
    Used to compute the shearing Poynting flux; the paper itself shows poor cos(phi) agreement with ground truth (Figure 11) and notes similar conclusions in earlier work.
  • ad hoc to paper Noise-free synthetic Stokes profiles are sufficient to assess DL-NIRSP diagnostic capability
    Section 2.2: 'We did not add noise to the synthetic spectra for this study', deferring noise to future work. Quiet-Sun polarization is weak, so noise is likely to dominate the error budget.

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Cite this review

Pith. "Pith review of What Can DKIST/DL-NIRSP Tell Us About Quiet-Sun Magnetism?." pith.science (2026). https://pith.science/paper/DH54MOOC

@misc{pith2026241118735,
  author       = {Pith},
  title        = {Pith review of: What Can DKIST/DL-NIRSP Tell Us About Quiet-Sun Magnetism?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DH54MOOC}},
  note         = {Machine review of arXiv:2411.18735}
}
abstract

Quiet-Sun regions cover most of the Sun's surface; its magnetic fields contribute significantly to the solar chromospheric and coronal heating. However, characterizing the magnetic fields of the quiet Sun is challenging due to their weak polarization signal. The 4-m \textit{Daniel K. Inouye Solar Telescope} (\textit{DKIST}) is expected to improve our understanding of the quiet-Sun magnetism. In this paper, we assess the diagnostic capability of the Diffraction-Limited Near Infrared Spectropolarimeter (DL-NIRSP) instrument on \textit{DKIST} on the energy transport processes in the quiet-Sun photosphere. To this end, we synthesize high-resolution, high-cadence Stokes profiles of the \ion{Fe}{1} 630~nm lines using a realistic magnetohydrodynamic simulation, degrade them to emulate the \textit{DKIST}/DL-NIRSP observations, and subsequently infer the vector magnetic and velocity fields. For the assessment, we first verify that a widely used flow-tracking algorithm, Differential Affine Velocity Estimator for Vector Magnetograms, works well for estimating the large-scale ($> 200$ km) photospheric velocity fields with these high-resolution data. We then examine how the accuracy of inferred velocity depends on the temporal resolution. Finally, we investigate the reliability of the Poynting flux estimate and its dependence on the model assumptions. The results suggest that the unsigned Poynting flux, estimated with existing schemes, can account for about $71.4\%$ and $52.6\%$ of the reference ground truth at $\log \tau =0.0$ and $\log \tau = -1$. However, the net Poynting flux tends to be significantly underestimated. The error mainly arises from the underestimated contribution of the horizontal motion. We discuss the implications on \textit{DKIST} observations.

Figures

Figures reproduced from arXiv: 2411.18735 by the authors.

Figure 1
Figure 1. Overview of MURaM simulation at log τ = 0 (top) and log τ = −1 (bottom) at t = 2 s. From left to right, the maps are for temperature T, vertical magnetic field Bz, and vertical velocity vl. The black dashed box marks the region of interest in this work. In the synthesis mode, SIR numerically solves the polarized radiative transfer equation in magnetically sensitive lines dI(τ ) dτ = K(τ ) [I(τ ) − S(τ )] , (1) where… view at source ↗
Figure 2
Figure 2. The inferred velocity field at log τ = 0 (left) and log τ = −1 (right) with input temporal resolution of ∆t = 2 s. The horizontal arrows indicate the direction and amplitude of horizontal velocity. The vertical velocities are plotted as the background. 3.1. Performance of DAVE4VMwDV at Different Optical Depths In this section, we investigate the performance of DAVE4VMwDV at different optical depths, similar to the a… view at source ↗
Figure 3
Figure 3. 2D histograms of the inferred velocity field and the reference velocity field at log τ = 0 (top) and log τ = −1 (bottom). From left to right, we show the histograms for vx, vy, and vz, respectively. The Spearman coefficient (ρ), Pearson coefficient (r), and slope (S) are also shown on the plots. The maps of the inferred velocity field are shown in [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (19 more)
Figure 4
Figure 4. Figure 4: Histograms of the magnitude of the ground-truth velocity (blue) and inferred velocity (orange) from magnetograms with different temporal resolution. Top: histograms for log τ = 0. Bottom: histograms for log τ = −1. From left to right, we show the results with ∆t = [2, …
Figure 5
Figure 5. Figure 5: Variation of metrics Erel, C and A (from top to bottom) with respect to cadence ∆t. The black lines represent to the metrics at log τ = 0.0, and the red lines represent to the metrics at log τ = −1.0. While Poynting flux of both signs exists near the intergranular lane…
Figure 6
Figure 6. Figure 6: Comparison between Poynting flux from MHD simulation (left) and DAVE4VMwDV (middle) at log τ = 0 (top) and log τ = −1 (bottom). The right column shows the 2D histogram between reference and estimate Poynting flux. The Spearman coefficient (ρ), Pearson coefficient (r), …
Figure 7
Figure 7. Figure 7: Original (left) and degraded (right) synthetic Stokes profiles. Upper panels show continuum intensity I/Ic, and lower panels show the circular polarization V /Ic in the wing of Fe I 630.15 nm line. continuum image decreases by 9%. In the Stokes V map, the original mixe…
Figure 8
Figure 8. Figure 8: Comparison between the ground-truth atmosphere (left) and that inferred with SIR (right) at log τ = 0. From top to bottom, we show temperature, LOS velocity, magnetic field strength, and inclination, respectively. An example of a mixed-polarity region is marked with bl…
Figure 9
Figure 9. Figure 9: Same as [PITH_FULL_IMAGE:figures/full_fig_p013_9.png]
Figure 10
Figure 10. Figure 10: Comparison of the distributions of the ground-truth (black) and inverted magnetic field (red). Left: Histograms of quantities at log τ = 0. Right: Histograms of quantities at log τ = −1. The figure shows the distribution of magnetic field strength |B|, vertical magnet…
Figure 11
Figure 11. Figure 11: Comparison of the cosine of azimuth between ground truth (upper row) and the one inferred with SIR after disambiguation (bottom row). From left to right we show the comparison at log τ = 0 and log τ = −1. Two dashed black boxes mark regions discussed Section 4.1 5.1. …
Figure 12
Figure 12. Figure 12: Velocities inferred from inverted magnetograms at log τ = 0.0 (left) and log τ = −1.0 (right). Top: The inferred velocity field. The horizontal arrows indicate the direction and amplitude of horizontal velocity. The vertical velocities are plotted as the background. B…
Figure 13
Figure 13. Figure 13: Comparison between MURaM and estimated Poynting flux at log τ = 0. Top: Ground truth value of the Poynting flux. Middle: Estimated Poynting flux with inferred magnetic and velocity field. Bottom: Histograms of Poynting flux from ground truth (black) and estimate (red)…
Figure 14
Figure 14. Figure 14: Same as [PITH_FULL_IMAGE:figures/full_fig_p018_14.png]
Figure 15
Figure 15. Figure 15: Power spectral density for selective inferred variables as a function of scale d at optical depth log τ = 0 (left) and log τ = −1 (right). From top to bottom: power spectra density of two terms in the induction equation, vx, vy, and vz. In the top panel, the black lin…
Figure 16
Figure 16. Figure 16: The response of emergence term S em z and shearing term S sh z of Poynting flux to the scale of velocity at log τ = 0 (left) and log τ = −1 (right). Top: the response of the absolute values to scale. Bottom: the response of the ratio of the value to ground truth. The …
Figure 17
Figure 17. Figure 17: Comparison of the distributions of quantities from reference magnetic field (black), inverted magnetic field with configuration in [PITH_FULL_IMAGE:figures/full_fig_p023_17.png]
Figure 18
Figure 18. Figure 18: Comparison of the power spectrum density (PSD) of magnetic field strength from reference magnetic field (black), inversion from emulated observation (green), inverted magnetic field with configuration in [PITH_FULL_IMAGE:figures/full_fig_p024_18.png]
Figure 19
Figure 19. Figure 19: Top: Plasma β in the region of interest with respect to height. The black, blue, and red lines represent the plasma β averaged among all the pixels, pixels with B < 3σB and pixels with B > 3σB in the region of interest, respectively. Bottom: Fraction of energy transpo…
Figure 20
Figure 20. Figure 20: Parameter selection for DAVE4VMwDV for MHD data at log τ = 0 (left) and log τ = −1 (right). Top: L-curve displaying the tradeoff between the two loss function terms L1 and L2 in DAVE4VMwDV. The values next to the black dots represent the weighting λ of each test. Bott…
Figure 21
Figure 21. Figure 21: Similar to [PITH_FULL_IMAGE:figures/full_fig_p027_21.png]
Figure 22
Figure 22. Figure 22: 2D histograms of the inferred velocity field and the reference velocity field at log τ = 0 (top) and log τ = −1 (bottom). From left to right, we show the histograms for vx, vy, and vz, respectively. The Spearman coefficient (ρ), Pearson coefficient (r), and slope (S) …

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Pith tools

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