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Structure and dynamics of erupting solar prominences using the Rolling Hough Transform: Toward a feature-oriented classification

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The Rolling Hough Transform sorts solar prominences by eruption phase in plane-of-sky images.

desk verdict A promising proof-of-concept for RHT-based prominence classification, but the polar-coordinate transform has a sign error that inverts the central radial/tangential labels as written. read the letter →

arxiv 2505.24515 v1 pith:AMVV3WMJ submitted 2025-05-30 astro-ph.SR

Harry Birch , Stéphane Régnier This is my paper
classification astro-ph.SR
keywords solarprominencesRollingHoughTransformprominenceeruptionsactivationplane-of-skystructureradial-tangentialclassificationSDO/AIA304Åfeature-oriented
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 aims to show that the Rolling Hough Transform (RHT), an image-processing algorithm that measures fine-scale linear orientation, can be used on solar prominence movies to recover structural information in the plane of the sky. It proposes a simple classification rule based on how much of a prominence's fine structure is radial (perpendicular to the limb) versus tangential (parallel to the limb), and claims that this ratio tracks the dynamical phase of an eruption or activation. Using two contrasting SDO/AIA 304 Å events—a quiet-Sun eruption and a polar-crown swirl activation—it finds that the classification flips in the expected sense: the eruption starts tangential and turns radial during the fast rise, while the swirl starts radial and becomes more tangential as plasma moves laterally. If correct, this offers a quantitative, automated descriptor of prominence morphology and dynamics that could support systematic event classification.

What carries the argument

The central object is the spatial Rolling Hough Transform (RHT), adapted for solar applications by Schad (2017) from the algorithm of Clark et al. (2014), which measures the orientation of curvilinear features by counting linearly aligned pixels in a circular kernel of width wlen = 51 around each pixel, applying an adaptive threshold of f = 0.25, and outputting a mean axial angle θ̄xy and a mean resultant length R̄, with only pixels satisfying R̄ ≥ 0.75 retained. The key modification for prominences is a polar conversion, θ_polar = α − θ̄xy, where α is the angle between the line from the pixel to the solar center and the eastward axis, which turns the classification into one about radial versus tangential alignment relative to the limb. The classification itself is a weighted sum of the angle histogram: bins at ±90° count fully as radial, bins at 0° count fully as tangential, and intermediate bins count in proportion, giving a ratio R/T whose crossing of 1 defines the label.

What would settle it

Recompute the classification for both events across a grid of cadences (1–10 minutes), kernel widths (21–101), adaptive thresholds f (0.10–0.50), and R̄ cutoffs (0.5–0.9); if any phase label changes between radial and tangential under small parameter changes, the claim that the classification tracks the dynamical phase is not robust.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the spatial RHT, applied over time to 304 Å images, returns per-pixel mean orientation angles for fine-scale prominence structures, and that these angles can be converted into a polar frame so that ±90° marks radial structure and 0° marks tangential structure. Weighted histograms of these angles yield a radial-to-tangential ratio R/T, with R/T ≥ 1 classifying a phase as radial and R/T < 1 as tangential, plus a strength |R/T − 1|. In the quiet-Sun eruption, the slow-rise phase is 64.93% tangential and the fast-rise phase is 58.26% radial; in the polar-swirl activation, the rise phase is 56.96% radial and the swirl phase is 50.59% tangential, with the two full-event classifications being tangential (54.95%) and radial (53.18%) respectively. The paper interprets this as evidence that the structural orientation carries dynamical information distinct from, but consistent with, motion measurements.

Load-bearing premise

The load-bearing premise is that the radial-versus-tangential percentages produced by the RHT are stable under the analyst's choices of time cadence, kernel width, and thresholds; if those choices shift, the phase labels could flip.

Editorial extensions

If this is right

  • If the central claim is correct, the RHT gives a quantitative, automated way to detect the structural transition from tangential to radial alignment that accompanies eruption onset.
  • The radial/tangential ratio can serve as a plane-of-sky structural complement to motion-based distinctions between eruptive and active prominences.
  • The same pipeline can be extended to the temporal RHT to estimate plasma velocities, adding a kinematic term to the classification.
  • Applied to a larger sample of events, the method could produce a feature-oriented catalogue of prominence dynamics that helps constrain eruption models.

Reading between the lines

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

  • The paper does not quantify how sensitive the radial/tangential percentages are to the cadence, kernel width, and thresholds; a sensitivity scan on these two events would show whether the phase labels are stable or artefacts of the chosen parameters.
  • Because RHT detects edges and boundaries, the classification may disproportionately reflect drainage paths and fine threads rather than bulk mass; relating the percentages to quantities like mass flux or twist would test this interpretation.
  • The method could be transferred to on-disk filament observations, although projection effects and foreground emission would likely require different filtering and a re-definition of the polar reference frame.
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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 / 4 minor

Summary. The paper presents a proof-of-concept application of the spatial Rolling Hough Transform (RHT) to SDO/AIA 304 Å movies of two limb prominences: a quiet-Sun eruption and a polar-crown swirl activation. The authors compute per-pixel orientation maps, convert the RHT line orientations into a polar frame relative to the solar limb, and introduce a radial/tangential classification based on weighted angle histograms (Eqs. 4-5). They report that the quiet-Sun event is predominantly tangential in the slow-rise phase and becomes more radial during/after eruption, while the polar-swirl event starts with a strong radial contribution and develops tangential structures during activation. The paper also provides timeslice kinematic measurements, a parameter table, and a cadence-sensitivity test for the detected-pixel histogram.

Significance. If the technical issues are resolved, this would be a useful proof-of-concept for a quantitative, feature-oriented descriptor of prominence morphology, complementing existing LOS and POS methods. Strengths of the manuscript include the validation of the RHT code against Schad's TRACE case, the deliberate choice of two contrasting dynamical events, and the explicit discussion of parameter choices and of the effect of cadence on detection. However, the central radial/tangential classification is not reproducible as written because of an angle-convention inconsistency between the RHT angle definition and the polar conversion, and the phase-level percentages are presented without uncertainty or sensitivity analysis. The paper's contribution is therefore potentially sound but requires corrective revision before the classification claims can be accepted.

major comments (3)
  1. [§3, §4.1, Eq. (3), Section 7] The polar-angle conversion in Eq. (3) is inconsistent with the RHT angle convention defined in Section 3. Section 3 states that the RHT output θ_xy is the line orientation, with 0° horizontal and ±90° vertical. Section 4.1 then defines θ_polar = α − θ_xy and states that θ_polar = 0° is tangential and θ_polar = ±90° is radial. For a pixel on the east limb (α = 0°), a horizontal feature is radial, yet Eq. (3) gives θ_polar = 0°, which the text defines as tangential; a vertical feature is tangential, yet Eq. (3) gives θ_polar = ±90°, which the text defines as radial. The correct limb-aligned conversion from line orientation φ is θ_polar = (φ − α − 90°) mod 180 (or an equivalent form), not α − φ. As written, a reader following the text would invert every radial/tangential label, and the values in Tables 3 and 4 and the narrative in Sections 5 and 7 would be reversed relative to the stated convention. Section 7 contains a related error: it says 'Radial (parallel to the solar limb) and tangential (perpendicular to the solar limb)', which is the reverse of the standard geometry and also contradicts Section 4.1's own wording. Please correct the equation and the wording, state explicitly which angle convention the code actually outputs (line orientation or normal angle), and verify the corrected conversion against a known feature, for example a radial drainage column on the east limb.
  2. [§6, Tables 3-4, Figure 12] The central classification percentages are reported to two decimal places without uncertainties, and the only sensitivity test provided shows that the detected-pixel histogram changes substantially with cadence (Figure 12). Several phase classifications sit very close to the 50/50 boundary: the swirl phase has R = 49.41% / T = 50.59%, the drainage phase R = 50.72% / T = 49.28%, and the plasma-ejection and collapse phases differ by only 3-4 percentage points. No analysis is given for how the R/T ratio or the resulting labels change under the choices of cadence, wlen = 51, f = 0.25, Rbar ≥ 0.75, smr_xy, med_xy, nlev, the limb-mask threshold, or the manually selected phase boundaries. Because the phase labels are the paper's central claim, please provide a sensitivity analysis for the classification percentages and the phase-boundary choices, or at minimum report the margins by which each classification exceeds the threshold and state how those margins are affected by reasonable parameter variation.
  3. [§3, Eq. (1)] Equation (1), written as ¯θxy = 0.5 arctan(y/x), is not a valid expression for the directional-statistics mean of an axial distribution; x and y are image coordinates, not the RHT histogram components. This appears to be a typographical compression of the standard circular mean, e.g., ¯θxy = 0.5 atan2(Σ w_i sin 2θ_i, Σ w_i cos 2θ_i), but as written the equation is not reproducible and cannot be the operation applied to the RHT angle bins. Please replace it with the explicit formula used in the code, including the handling of the 180° axial ambiguity, so that the central measurement is unambiguously defined.
minor comments (4)
  1. [§4.1] The description of the modulo/offset step after Eq. (3) is ambiguous: please specify the exact mapping to the interval [-90°, 90°] (for example, θ_polar = ((θ_polar + 90°) mod 180°) − 90°), since this affects the visualizations and the histograms.
  2. [§2.2, §2.3] The phase names are used inconsistently: Section 2.2 refers to a 'pre-eruptive phase' while the later analysis and Table 3 use 'slow-rise'; the polar-swirl event is labeled 'Rise' in Figure 2 and 'slow-rise' in the text. Please harmonize the terminology.
  3. [§6, Figure 12] The cadence test in Figure 12 shows only detected-pixel histograms, not the resulting angle distributions; showing how the RHT angle histograms and classification percentages change with cadence would make the test directly relevant to the paper's main claim.
  4. [General] There are several typographical and formatting issues, including 'T able' in the table captions, the inconsistent spelling 'an prominence activation', and duplicated words such as 'the the' in the text; a careful proofread is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the RHT measurement is an independent image-processing output, the radial/tangential classifier is a threshold applied after the fact, and the only self-citation is ancillary.

full rationale

The derivation chain is self-contained. The spatial RHT angles are an independent image measurement produced by the published RHT code (Clark et al. 2014; Schad 2017), and the paper validates that code by reproducing Schad's TRACE result before applying it to the two prominence events. The polar-coordinate conversion in Equation (3) is a fixed coordinate convention, not a parameter fitted to the subsequent classification, and the radial/tangential classifier in Equations (4)-(5) is a deterministic threshold applied to the measured angle histograms. Phase boundaries are selected from light-curve intensity gradients before the RHT percentages are computed, so the phase-dependent classifications are not constructed so as to reproduce themselves. The only author self-citation, Régnier et al. (2011), appears in an ancillary kinematic comparison and is not load-bearing for the RHT-based structural claim. A possible sign inconsistency in Equation (3) would be a reproducibility or correctness concern rather than a circularity: it does not make the output equivalent to its input. The acknowledged dependence on user-chosen parameters (cadence, kernel width, thresholds) and the lack of a sensitivity analysis for the classification percentages are soundness limitations, not circular steps.

Assumptions & free parameters 9 free parameters · 6 assumptions · 0 invented entities

The central claim relies on user-chosen RHT parameters, manually selected phase boundaries and slits, and two domain assumptions (304 Å traces cool prominence plasma; plane-of-sky orientation is a valid structural descriptor). No new physical entities are introduced. The paper is transparent about several of these choices, which keeps the circularity burden low but raises sensitivity concerns.

free parameters (9)
  • wlen (kernel diameter) = 51
    Sets the circular window diameter for Hough transform; tailored to prominence feature width.
  • f (adaptive threshold) = 0.25
    Fraction of max H(theta) below which angle bins are zeroed; inherited from Schad (2017).
  • Rbar threshold = 0.75
    Minimum mean resultant length for accepting a pixel angle; inherited from Schad (2017).
  • smr_xy (spatial filter) = 30
    Spatial smoothing scale for the bitmap; set during tuning.
  • med_xy (median filter) = 3
    Median filter width applied to the bitmap.
  • nlev (noise level) = 10
    Noise threshold for background subtraction in bitmap creation.
  • Timeseries cadence = 1 min (quiet-Sun), 5 min (polar full), 10 min (polar phases)
    Controls temporal smoothing kernel; Figure 12 shows RHT angle output changes with cadence.
  • Phase boundary times = 16:30/17:45/18:00 UT (quiet-Sun); 12:15/20:30/23:45 UT (polar)
    Chosen by visual inspection of light curve gradients; classifications depend on these partitions.
  • Limb mask threshold = not specified numerically
    Threshold that varies the solar radius to remove the detected limb edge; not quantified in the paper.
assumptions (6)
  • standard math The Rolling Hough Transform and directional statistics formulas from Schad (2017) and Mardia & Jupp (1999) correctly compute mean orientation and coherence.
    Invoked in Section 3 as the algorithmic foundation; validated by reproducing Schad's TRACE result.
  • domain assumption 304 Å He ii emission traces cool prominence plasma, so RHT orientation in this channel reflects prominence structure.
    Section 2.1; standard usage in the field.
  • domain assumption Plane-of-sky projection preserves enough structural orientation for classification despite projection effects.
    Section 6 acknowledges projection can distort orientation; not corrected.
  • ad hoc to paper Linear weighting of 5-degree angle bins and the R/T >= 1 threshold provide a meaningful radial/tangential classification.
    Section 5, Equations (4) and (5); the weighting is a modeling choice without external validation.
  • domain assumption Phase boundaries derived from light-curve intensity gradients correspond to distinct dynamical phases.
    Sections 2.2 and 2.3; boundaries are hand-selected where intensity slope changes.
  • ad hoc to paper The limb-edge masking threshold removes the detected solar limb without losing significant prominence structure.
    Section 4.1; threshold varied manually, no quantitative loss assessment.

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Pith. "Pith review of Structure and dynamics of erupting solar prominences using the Rolling Hough Transform: Toward a feature-oriented classification." pith.science (2026). https://pith.science/paper/AMVV3WMJ

@misc{pith2026250524515,
  author       = {Pith},
  title        = {Pith review of: Structure and dynamics of erupting solar prominences using the Rolling Hough Transform: Toward a feature-oriented classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AMVV3WMJ}},
  note         = {Machine review of arXiv:2505.24515}
}
read the original abstract

The classification of solar prominences has proven to be challenging due to their diverse morphologies and dynamical behaviour. Complexity is heightened when considering eruptive prominences, where the dynamics demand methods capable of capturing detailed structural information. While there exists a range of line-of-sight (LOS) and plane-of-sky (POS) techniques which have advanced our understanding of prominence motions, they are subject to limitations, emphasising the need for effective methods of extracting structural information from prominence dynamics. We present a proof-ofconcept for the spatial Rolling Hough Transform (RHT) algorithm, which identifies finescale structural orientation in the POS, applied to prominence structure and dynamics. We demonstrate the RHT approach using two contrasting prominence dynamics events using SDO/AIA 304 \r{A} observations: (1) a quiet-Sun eruption, (2) activation (swirl) of a polar-crown prominence. By analysing the light curves and movies from each event, we divide the events into distinct dynamical phases: from slow rise to drainage. The spatial RHT method enables us to extract structural information and localised dynamics for both events and the different evolution phases. We develop a classification to label the prominences as either radially or tangentially oriented structures. The quiet-Sun eruption has a predominately tangential structure in the slow-rise phase, but displays greater radial features during/after the eruption. The polar-swirl activation initially shows a strong radial contribution, which diminishes as more tangential structures appear during/after the activation. Our results demonstrate the successful application of the spatial RHT to prominences, leading to the classification of individual prominences and an insight into their dynamics.

Figures

Figures reproduced from arXiv: 2505.24515 by the authors.

Figure 1
Figure 1. Top: SDO/AIA 304 ˚A images of the quiet-Sun eruption across each of the four phases: (a) Pre￾eruption, (b) Eruption, (c) Plasma Ejection, and (d) Collapse. Bottom: (e) Light curve for the quiet-Sun eruption generated across the full timeseries, coloured according to each phase. The positions in time of the above SDO/AIA images are marked with an asterisk. An animation (Movie1) of the SDO/AIA 304 ˚A observations for … view at source ↗
Figure 2
Figure 2. Top: SDO/AIA 304 ˚A images of the polar-swirl activation across each of the four phases: (a) Rise, (b) Swirl, (c) Drainage, and (d) Residual Phase. Bottom: (e) Light curve for the polar-swirl activation generated across the full timeseries, coloured according to each phase. The positions in time of the above SDO/AIA images are marked with an asterisk. An animation (Movie2) of the SDO/AIA 304 ˚A observations for the … view at source ↗
Figure 3
Figure 3. Timeslice for the quiet-Sun eruption. Top: timeslice taken along slice s1, used to extract the speed of the slow-rise and fast-rise phases. Bot￾tom: timeslice taken along slice s2, used to extract the drainage speed. adapted for solar physics by Schad (2017), al￾ [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (8 more)
Figure 5
Figure 5. Figure 5: (a) Bitmap array with a line at 35 degrees from the horizontal axis and random noise. The kernel is shown centered on pixel (x, y) covered by the blue circular region. For each angle bin, the number of pixels which are linearly aligned is counted and normalised. This i…
Figure 6
Figure 6. Figure 6: shows the spatial RHT mean angle result applied to the full timeseries for the quiet￾Sun eruption (left), along with the extracted distribution (right). The cadence used for these results was 1 minute. The spatially RHT shows the tangentially oriented plasma body (in g…
Figure 7
Figure 7. Figure 7: Top: Spatial RHT mean angles for the four phases of the quiet-Sun eruption: slow-rise (a), fast-rise (b), plasma ejection (c), and collapse (d). Bottom: (e) Angle distributions for each phase in the quiet-Sun eruption. Angles of ±90 and 0 degrees represent radial and t…
Figure 8
Figure 8. Figure 8: Spatial RHT mean angle for the polar￾swirl activation (left) with the distribution of angles (right). Angles of ±90 and 0 degrees represent ra￾dial and tangential features, respectively. We introduce a classification system based on the structural information provided …
Figure 9
Figure 9. Figure 9: Top: Spatial RHT mean angles for each of the four phases: fast-rise (a), swirl (b), drainage (c), and the residual phase (d). (e) Angle distributions for each phase in the polar-swirl activation. Angles of ±90 and 0 degrees represent radial and tangential features, res…
Figure 10
Figure 10. Figure 10: Histograms for the percentage occu￾pation of spatial RHT mean angles across different timeseries’ in the quiet-Sun event, organised into 5 degree bins, showing: (a) full event, slow-rise (b), fast-rise (c), plasma ejection (d), and collapse (e). with a radial classifi…
Figure 11
Figure 11. Figure 11: Histograms for the percentage occu￾pation of spatial RHT mean angles across different timeseries in the polar-swirl activation, organised into 5 degree bins, showing: (a) full event, slow￾rise (b), swirl (c), drainage (d), and residual phase (e). 6. ASSESSMENT OF THE …
Figure 12
Figure 12. Figure 12: Histogram of detected pixels for the first image of the quiet-Sun eruption, taken at time 14:00:05.13 UT, using different cadence values. The cadence value determines the temporal dimension of the data and hence the value of temporal filter kernel. Accuracy of the ass…

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.