arxiv: 2604.13590 · v1 · submitted 2026-04-15 · 🌌 astro-ph.SR · astro-ph.IM· physics.space-ph

Recognition: unknown

SJET: An Interactive Solar Jet Extraction Tool

Song Tan , Alexander Warmuth , Fr\'ed\'eric Schuller , Yuandeng Shen , Yue Fang , Jake A. J. Mitchell , Zedong Liu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 12:49 UTC · model grok-4.3

classification 🌌 astro-ph.SR astro-ph.IMphysics.space-ph

keywords solar jetsfeature extractionimage processingsolar coronaEUV observationsthresholdingBézier curves

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The pith

SJET standardizes extraction of solar jet length, width, curvature and direction from EUV images.

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

Solar jets are fast-moving plasma flows whose shapes vary widely and whose measurements have differed across studies because observers choose different processing steps. The paper presents SJET as an interactive Python tool that applies five thresholding algorithms plus morphological cleaning, locates jet start and end points inside circular regions by using the jet's own asymmetry to fix propagation direction, and fits the axis with a quadratic Bézier curve. From this curve the tool computes length, width, curvature and deflection angle. Validation on Solar Orbiter and SDO images shows the derived numbers agree with conventional manual measurements when the user sets thresholds and regions of interest appropriately. The central purpose is to replace ad-hoc methods with a single documented workflow so that large numbers of jets can be compared on equal terms.

Core claim

SJET integrates multiple thresholding algorithms with morphological operations and introduces a circular-region method that identifies jet endpoints while determining propagation direction from morphological asymmetry; the jet axis is then represented by a quadratic Bézier curve from which length, width, curvature and deflection angles are extracted. Tests on high-resolution EUV images from Solar Orbiter/EUI and SDO/AIA show that the extracted parameters match those obtained by traditional analysis when user-selected thresholds and regions of interest are held fixed.

What carries the argument

The SJET workflow that combines multi-algorithm thresholding, circular-region asymmetry detection for start and end points, and quadratic Bézier curve fitting of the jet axis.

If this is right

Geometric parameters such as jet length, width, curvature and deflection angle can be obtained under a single documented procedure rather than varying manual choices.
Large-sample statistical studies of solar jets become practical because processing steps are standardized and reproducible.
The same workflow applies to both on-disk and limb jets once appropriate thresholds and regions of interest are selected for each dataset.
Results from SJET agree with conventional manual tracing on real Solar Orbiter and SDO observations under controlled user input.

Where Pith is reading between the lines

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

A shared extraction pipeline could allow direct comparison of jet statistics across different solar-cycle phases or instruments without method-induced scatter.
The interactive design opens a route to test how sensitive final jet statistics are to small changes in the initial threshold or region selection.
Consistent curvature and deflection measurements could be fed into models that link jet geometry to the acceleration of the solar wind.

Load-bearing premise

Different users will choose thresholds and regions of interest consistently enough on the same data to produce comparable geometric measurements.

What would settle it

Independent users applying SJET to the identical image sequence produce length or curvature values that differ by amounts larger than the measurement uncertainties stated in the validation tests.

Figures

Figures reproduced from arXiv: 2604.13590 by Alexander Warmuth, Fr\'ed\'eric Schuller, Jake A. J. Mitchell, Song Tan, Yuandeng Shen, Yue Fang, Zedong Liu.

**Figure 1.** Figure 1: SJET workflow illustrating the interactive jet extraction process. The workflow begins with event selection and data input through FITS files, followed by optional ROI definition. Users can apply one of five thresholding methods (manual, Otsu, adaptive, percentile, and log-enhanced) to generate binary masks, which then undergo morphological optimization and region filtering. The central user interaction co… view at source ↗

**Figure 2.** Figure 2: SJET interactive interface and functionality demonstration. The left panel displays, from top to bottom: control panel, method selection, manual adjustment, and morphological optimization options. The right visualization panel presents an example of HRIEUV analysis. When users achieve satisfactory results, they can click the save button in the lower left corner of the interface, which will save all FITS fi… view at source ↗

**Figure 4.** Figure 4: Example of filtering regions by maximum intensity. The panels show an extracted EUI-HRIEUV jet image and a further processed image (with a tick to keep only the region of maximum intensity), respectively. results, and ROI masks (if applied), all preserving complete observational metadata. PNG format: High-resolution (300 DPI) visualizations including colorized intensity maps and grayscale masks. ASCII tex… view at source ↗

**Figure 5.** Figure 5: Example of jet geometric parameter extraction. The upper panel displays the binary mask of the jet with annotations of the obtained geometric parameters and jet axis structures. The lower panels show the original HRIEUV jet image and the extracted HRIEUV jet image, respectively. estimate and allows the Bézier curve to be adjusted to better follow the physically meaningful jet axis. Both the automatic and m… view at source ↗

**Figure 6.** Figure 6: SJET-based HRIEUV jet analysis. The left panel presents the complete HRIEUV image, while the right panels show the analysis results. Notably, the time-space plot, constructed using slices positioned along the fitted Bézier curve, reveals the jet propagation velocity through linear fitting. average width is calculated through arithmetic averaging of all valid measurement points: 𝑊¯ = 1 𝑁 ∑︁𝑁 𝑖=1 𝑊𝑖 (7) wher… view at source ↗

**Figure 7.** Figure 7: SJET-based AIA 304 Å jet analysis. Similar to [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗

**Figure 8.** Figure 8: Gaussian FWHM width measurement by SJET for a jet observed in the AIA 304 Å channel (2021 September 27, 11:42:53 UT). Left: binary mask with the fitted Bézier axis (red) and mask boundary widths (cyan lines) at ten equally spaced locations along the axis. It is worth noting that, when no manual control points are entered, the control points (automatic) and control points (manual) coincide; please refer to … view at source ↗

read the original abstract

Solar jets are dynamic collimated plasma flows in the solar atmosphere that play crucial roles in coronal heating and solar wind acceleration. Their complex and diverse morphologies pose significant challenges for developing universal algorithms for automatic identification and extraction, particularly for on-disk jets affected by projection effects and background contamination. We present SJET, an interactive tool for solar jet feature extraction using multiple algorithms developed in Python that integrates five thresholding algorithms with morphological operations. SJET implements a novel method for identifying start and end points based on circular regions that objectively determines jet propagation direction by exploiting morphological asymmetry, combined with modeling the axis using quadratic B\'ezier curves for accurate extraction of geometric parameters including length, width, curvature, and deflection angles. Validation analyses using Solar Orbiter/EUI high-resolution image and SDO/AIA observations demonstrate SJET's effectiveness across different observational conditions, with good agreement compared to traditional analysis methods, though the tool's accuracy remains dependent on user-defined threshold parameters and region of interest selection. SJET provides a solution to method inconsistency in solar jet research through standardized processing workflows, establishing a technical foundation for large-sample statistical studies.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note private letter to a colleague

SJET packages a new asymmetry test and Bezier axis fit into an interactive Python tool for solar jets, but its user-dependent thresholds prevent any strong claim of solving method inconsistency.

read the letter

SJET is a new interactive Python tool that extracts length, width, curvature, and deflection from solar jet images. It combines five thresholding algorithms with morphological operations, adds a circular-region asymmetry check to set propagation direction, and fits the axis with quadratic Bezier curves. That specific combination is not described in the papers it cites, so the implementation is the main novelty here. The code is aimed at Solar Orbiter/EUI and SDO/AIA data and gives users a single interface instead of stitching separate scripts together. Validation on a handful of events shows qualitative agreement with manual tracing, which is the expected baseline for a tool paper. The authors are straightforward that results still depend on the thresholds and region-of-interest choices the user makes. That dependence is the central limitation. No numbers are given on how much different observers differ when they process the same image, and no blinded test or error bars appear in the description. Solar jets change appearance with projection and background, so subjective parameter choices can easily reintroduce the very inconsistency the tool is meant to reduce. Without evidence that inter-user scatter is small, the pitch for large-sample statistical studies rests on an untested assumption. This paper is for solar physicists who already analyze jets and want a ready-made extraction aid rather than a fully automatic pipeline. A reader who processes dozens of events might save time and keep their own measurements more uniform, but anyone expecting field-wide standardization will be disappointed. The work is coherent on its own terms and shows honest engagement with the practical difficulties, so it deserves a serious referee. I would send it for review with the request that the authors add a short section quantifying reproducibility across users on a small test set.

Referee Report

2 major / 1 minor

Summary. The manuscript introduces SJET, an interactive Python tool for extracting solar jet features from EUV images. It integrates five thresholding algorithms with morphological operations, employs a novel circular-region approach to identify start and end points and determine propagation direction via morphological asymmetry, and models the jet axis with quadratic Bézier curves to derive geometric parameters including length, width, curvature, and deflection angles. Validation on selected Solar Orbiter/EUI high-resolution and SDO/AIA images is reported to show good agreement with traditional manual methods, although accuracy depends on user-defined thresholds and ROI selection. The tool is positioned as providing standardized workflows to reduce method inconsistency and enable large-sample statistical studies of solar jets.

Significance. If the interactive workflows can be shown to yield reproducible geometric measurements across users and datasets, SJET would address a recognized source of inconsistency in solar jet studies, supporting more reliable statistical analyses of jet properties relevant to coronal heating and solar wind acceleration. The combination of multiple algorithms and Bézier modeling offers flexibility for diverse morphologies, but the lack of demonstrated control over user variability limits the immediate impact on standardization claims.

major comments (2)

[Abstract] Abstract: The central claim that SJET 'provides a solution to method inconsistency in solar jet research through standardized processing workflows' is not supported by evidence; the text explicitly states that accuracy 'remains dependent on user-defined threshold parameters and region of interest selection' yet provides no quantification (e.g., inter-user standard deviation or repeatability metrics) of variability in extracted parameters such as length, width, curvature, or deflection angles.
[Validation analyses] Validation analyses: The reported 'good agreement compared to traditional analysis methods' on Solar Orbiter/EUI and SDO/AIA data is described only qualitatively with no error bars, no blinded inter-observer tests, and no assessment of how different users' threshold/ROI choices affect the same jet measurements, which is required to substantiate the foundation for large-sample studies.

minor comments (1)

[Abstract] The abstract lists integration of 'five thresholding algorithms' but does not name them; including the specific algorithms (e.g., Otsu, adaptive, etc.) would improve clarity for readers evaluating the tool's implementation.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their constructive comments, which help clarify the scope and limitations of our work on the SJET tool. We respond point by point to the major comments and outline specific revisions.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim that SJET 'provides a solution to method inconsistency in solar jet research through standardized processing workflows' is not supported by evidence; the text explicitly states that accuracy 'remains dependent on user-defined threshold parameters and region of interest selection' yet provides no quantification (e.g., inter-user standard deviation or repeatability metrics) of variability in extracted parameters such as length, width, curvature, or deflection angles.

Authors: We agree that the abstract claim is not quantitatively supported. The standardization in SJET refers to the fixed sequence of algorithms (thresholding options, morphological operations, circular-region start/end detection, and quadratic Bézier fitting), which replaces ad-hoc manual tracing. However, we acknowledge that user choices for thresholds and ROIs still affect outputs. We will revise the abstract to state that SJET provides standardized processing workflows that can help mitigate method inconsistency, while explicitly noting the dependence on user-defined parameters. We will also add a new paragraph in the Discussion section describing potential variability sources and recommending future inter-user reproducibility assessments. revision: yes
Referee: [Validation analyses] Validation analyses: The reported 'good agreement compared to traditional analysis methods' on Solar Orbiter/EUI and SDO/AIA data is described only qualitatively with no error bars, no blinded inter-observer tests, and no assessment of how different users' threshold/ROI choices affect the same jet measurements, which is required to substantiate the foundation for large-sample studies.

Authors: The validation in the manuscript is qualitative, relying on visual comparison and agreement with manual measurements on selected high-resolution images. No quantitative error bars, statistical metrics, or blinded tests were included. We will revise the validation section to explicitly characterize the comparisons as qualitative, provide any available parameter differences from the example cases, and discuss how threshold and ROI selections influence results. We will also add a limitations subsection noting that full quantification of user variability lies beyond the present tool-description paper. revision: partial

standing simulated objections not resolved

Performing new blinded inter-observer tests or computing inter-user standard deviations on extracted parameters, as these require additional data collection and analysis not present in the current study.

Circularity Check

0 steps flagged

No circularity: SJET is an algorithmic implementation without derivations or predictions

full rationale

The paper presents an interactive Python tool integrating thresholding algorithms, morphological operations, circular-region start/end detection, and quadratic Bézier curve axis modeling for jet parameter extraction. No equations, first-principles derivations, or statistical predictions appear that could reduce to fitted inputs or self-definitions by construction. The central claim of standardized workflows solving method inconsistency is an implementation statement, not a result derived from prior outputs; the abstract explicitly notes remaining dependence on user thresholds and ROIs, and validation is described only as qualitative agreement with traditional methods on selected images. No self-citations, uniqueness theorems, or ansatzes are load-bearing for any claimed result, leaving the work self-contained as software description.

Axiom & Free-Parameter Ledger

2 free parameters · 0 axioms · 0 invented entities

The central claim rests on the assumption that user-selected thresholds and ROIs can be applied reproducibly; no new physical constants, particles, or axioms are introduced.

free parameters (2)

threshold parameters
User-chosen brightness cutoffs for each image; directly affect extracted jet boundaries and are not derived from data.
region of interest selection
Manual or semi-manual choice of image sub-region; required for the circular-asymmetry and Bezier steps.

pith-pipeline@v0.9.0 · 5517 in / 1152 out tokens · 28200 ms · 2026-05-10T12:49:26.352807+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

2 extracted references · 1 canonical work pages

[1]

BarnesW.T.,etal.,2020,aiapy:APythonPackageforAnalyzingSolarEUV Image Data from AIA, doi:10.5281/zenodo.4274931 Bradski G., 2000, Dr. Dobb’s Journal: Software Tools for the Professional Programmer, 25, 120 Chen H.-D., Zhang J., Ma S.-L., 2012, Research in Astronomy and Astro- physics, 12, 573 Chierichini S., Bourgeois S., Soós S., Liu J., Korsós M. B., Del...

work page doi:10.5281/zenodo.4274931 2020
[2]

to (163, 124)). This stability reflects the fact that the extremal point pair identification is based on the global geometry of the mask rather than its precise boundary, making it relatively robust to moderate threshold variations. Thearea-basedwidthshowsastrongersensitivityacrossthethree levels (71.44, 50.17, and 31.42 pixels), as it integrates over the...

2026