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arxiv: 2605.12979 · v1 · submitted 2026-05-13 · 🌌 astro-ph.SR · astro-ph.IM

Recognition: 2 theorem links

· Lean Theorem

Forward Modeling of Dust-Induced Stray Light in Ground-Based Coronagraphs: A Dual-Path Monitoring Approach for High-Precision Inner Corona Observations

Xiande Liu , Xuefei Zhang , Yu Liu , Tengfei Song , Mingyu Zhao , Mingzhe Sun , Feiyang Sha , Jun Fang
Authors on Pith no claims yet

Pith reviewed 2026-05-14 18:58 UTC · model grok-4.3

classification 🌌 astro-ph.SR astro-ph.IM
keywords stray light correctionground-based coronagraphdust scatteringinner coronaforward modelingpolar coronal holesradial intensity profileFe XIV line
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The pith

Dual-path lens monitoring and forward modeling remove dust stray light to restore true inner corona signals in ground-based observations.

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

The paper establishes a correction technique for stray light caused by dust accumulating on the objective lens of the Spectral Imaging Coronagraph. Real-time imaging of the lens surface supplies the dust distribution, which is then used to build a physical point-spread function and reconstruct the additive background through convolution. Model parameters are fitted using the uniform brightness of polar coronal holes as a constraint. After application, the method reduces rms noise in polar regions by about 67 percent on average and raises the signal-to-background ratio by up to a factor of 3.7 under heavy contamination, while recovering streamer shapes that match space-based data and restoring radial intensity profiles consistent with a 2.0 MK plasma temperature.

Core claim

By simultaneously imaging the objective lens to obtain the dust distribution and constructing a defocus-based point-spread function, the nonuniform scattering background is forward-modeled and subtracted from the coronal images. Parameters of the model are retrieved through inversion constrained by the brightness of polar coronal holes. The corrected images show restored morphological fidelity in streamers and radial profiles that follow the hydrostatic exponential decay expected for emission at the Fe XIV 530.3 nm formation temperature of approximately 2.0 MK.

What carries the argument

Dual-path real-time monitoring of the objective lens surface combined with convolution reconstruction of the dust-scattering background using an optical defocus point-spread function.

If this is right

  • Corrected images recover streamer morphologies that align closely with simultaneous space-based observations from SDO/AIA.
  • Radial intensity profiles in the inner corona exhibit the hydrostatic exponential decay corresponding to a plasma temperature of approximately 2.0 MK.
  • The method maintains performance across varying levels of dust contamination on the objective lens.
  • High-precision thermodynamic and dynamic studies of the inner corona become feasible with ground-based instruments after applying the correction.

Where Pith is reading between the lines

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

  • The same dual-path imaging and modeling strategy could be applied to other ground-based solar telescopes that suffer from surface contamination.
  • Automated, real-time parameter retrieval might support continuous long-term monitoring campaigns without manual intervention.
  • The forward-modeling approach may generalize to correcting additional instrumental backgrounds such as internal reflections in similar optical systems.

Load-bearing premise

Polar coronal holes supply an independent, uniform reference that determines the dust-scattering parameters without introducing bias into the corrected streamer structures.

What would settle it

If simultaneous space-based observations show that the corrected ground-based radial intensity profiles in streamers deviate from the exponential decay rate expected for a 2.0 MK plasma, or if streamer morphologies remain mismatched after correction under heavy dust conditions.

Figures

Figures reproduced from arXiv: 2605.12979 by Feiyang Sha, Jun Fang, Mingyu Zhao, Mingzhe Sun, Tengfei Song, Xiande Liu, Xuefei Zhang, Yu Liu.

Figure 1
Figure 1. Figure 1: Schematic diagram of the optical layout of the SICG. The blue solid lines represent the coronal light path, while the red dotted lines indicate direct sunlight suppressed by the internal occulter (D1). Instrumental stray light is further mitigated by the Lyot stop (A3) and Lyot spot (D2). Key optical components are labeled as follows: O1, objective lens; A1, aperture stop; A2, field stop; O2, field lens; O… view at source ↗
Figure 2
Figure 2. Figure 2: Schematic diagram of the dual-path monitoring system. A beam-splitting prism divides the incident light into two channels: the science channel (green path), which is focused at infinity for coronal imaging, and the monitoring channel (red path), which is focused on the objective lens surface for dust monitoring. This configuration allows for the simultaneous acquisition of scientific data and the dust sour… view at source ↗
Figure 3
Figure 3. Figure 3: Flowchart of the comprehensive data processing pipeline, illustrating the strategic placement of the forward-modeling dust correction loop between the basic calibrations (Level 1) and the final scientific data products. Although the theoretical Mie scattering pattern of a single ideal spherical particle contains complex concentric diffraction rings, under the actual observational configuration of the SICG,… view at source ↗
Figure 4
Figure 4. Figure 4: Process of extracting objective lens dust parameters. (a) The original monitoring image, showing the dust distribution superimposed on a non-uniform illumination gradient background. (b) The high-pass enhanced image, where the low-frequency background is suppressed, isolating the high-frequency signal components representing the dust particles. (c) Final detection results overlaid on the original image. Re… view at source ↗
Figure 5
Figure 5. Figure 5: illustrates the variation of the residual RMS as a function of the parameter K. The curve exhibits a distinct concave structure, indicating the existence of a unique global minimum. The behavior of the RMS curve can be explained by the geometric properties of the convolution. Specifically, in the underestimation regime (K < 0.5), the kernel width is smaller than the actual dust halo, making the model profi… view at source ↗
Figure 6
Figure 6. Figure 6: Visual comparison of the stray light correction performance under different objective lens contamination levels. Left column (a, d, g): The Level 1 observed images (IL1) after flat-field correction, corresponding to three representative states: Low (Clean), Medium (Moderate), and High (Dirty) contamination. Middle column (b, e, h): The scattering background models (Bscat) constructed based on simultaneousl… view at source ↗
Figure 7
Figure 7. Figure 7: Radial intensity profiles and exponential fitting analysis in the equatorial streamer region (Position Angle 0◦ ± 5 ◦ ). The light blue solid line represents the flat-fielded Level 1 data, overlaid with its best-fit curve (dark blue dashed line). The green dotted line shows the derived scattering background model (Bscat). The red solid line denotes the dust-removed Level 2 data, which exhibits excellent ag… view at source ↗
Figure 8
Figure 8. Figure 8: Statistical distribution of the Contrast Improvement Factor over the extended radial range of 1.05–1.35 R⊙. The CIF is calculated as the ratio of SBRpost to SBRpre at discrete radial intervals with a step size of 0.05 R⊙. The boxplots illustrate the performance across Clean, Moderate, and Dirty contamination levels: the red lines indicate the median values, the colored boxes represent the interquartile ran… view at source ↗
Figure 9
Figure 9. Figure 9: Comparison of polar-unrolled maps (Cartesian projection) between SDO/AIA 211 ˚A and SICG 530.3 nm. The maps cover the radial range of 1.05–1.25 R⊙ and the full azimuth (0◦ –360◦ ). (a) Reference image from SDO/AIA 211 ˚A. (b) Corrected SICG image for the “High Contamination” (Dirty) case. Both images are displayed in a green colormap to highlight the structural correspondence. The vertical dashed lines mar… view at source ↗
Figure 10
Figure 10. Figure 10: Comparison of normalized angular intensity profiles averaged over the radial range of 1.05–1.25 R⊙ under different contamination levels. Panels (a)–(c) correspond to the Clean, Moderate, and Dirty cases, respectively. The gray dashed line represents the raw SICG data (Iraw), which exhibits a flattened profile due to the strong stray light background. The orange dotted line denotes the pre-processed Level … view at source ↗
read the original abstract

High-precision ground-based observations of the inner corona (1.05-2.0 R_sun) are fundamentally constrained by instrumental stray light, particularly the additive background from dynamic dust accumulation on the objective lens. To address this issue, we propose a correction method for the Spectral Imaging Coronagraph (SICG) based on dual-path real-time monitoring and forward physical modeling. By simultaneously imaging the objective lens surface, we obtain deterministic prior information on dust distribution. We construct a physical point-spread function using optical defocus parameters and reconstruct the nonuniform scattering background via convolution. Model parameters are retrieved through data-driven inversion constrained by polar coronal holes. The method demonstrates excellent robustness under varying contamination conditions. After correction, the rms noise in the polar background is reduced by approximately 67% on average, and the signal-to-background ratio improves by a factor of up to 3.7 under heavy contamination conditions. Comparisons with space-based Solar Dynamics Observatory/Atmospheric Imaging Assembly observations indicate that the corrected images recover the morphological structures of streamers with high fidelity. Further radial intensity analysis reveals that the correction process successfully restores the hydrostatic exponential decay characteristic of inner coronal radiation. The fitted decay coefficient corresponds to a plasma temperature of approximately 2.0 MK, consistent with the characteristic formation temperature of the Fe XIV 530.3 nm line. These results demonstrate that the method effectively eliminates the dominant systematic bias in ground-based observations, providing a reliable data foundation for high-precision coronal thermodynamic and dynamic research with the SICG.

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.

Referee Report

2 major / 1 minor

Summary. The manuscript describes a forward modeling approach for correcting dust-induced stray light in ground-based coronagraphs using dual-path monitoring. It involves real-time imaging of the objective lens to determine dust distribution, construction of a physical point-spread function based on optical defocus, and reconstruction of the nonuniform scattering background. Dust-scattering model parameters are retrieved via data-driven inversion constrained by polar coronal holes. The method is claimed to reduce the rms noise in the polar background by approximately 67% on average and improve the signal-to-background ratio by up to a factor of 3.7 under heavy contamination. Corrected images are shown to recover streamer morphologies consistent with SDO/AIA data, and radial intensity profiles are restored to show hydrostatic exponential decay corresponding to a plasma temperature of about 2.0 MK, matching the Fe XIV 530.3 nm line.

Significance. If the central claims hold, this method offers a valuable tool for improving the quality of ground-based inner corona observations by mitigating a significant source of systematic error from dust accumulation. The reported quantitative improvements and consistency with independent space-based observations suggest potential for enabling high-precision thermodynamic and dynamic studies of the corona that were previously limited by stray light.

major comments (2)
  1. The description of the data-driven inversion constrained by polar coronal holes does not include details on the inversion conditioning, potential degeneracies, or validation against independent regions, which is necessary to confirm that the reported 67% rms noise reduction and 3.7x signal-to-background improvement are not affected by over-fitting or bias in streamer structures.
  2. No specific equations, error budgets, or procedural details are provided for the forward modeling, PSF construction, or the radial profile fitting that yields the 2.0 MK temperature, making it challenging to assess the robustness of the hydrostatic decay restoration claim.
minor comments (1)
  1. The abstract mentions 'excellent robustness under varying contamination conditions' without quantifying the range of conditions tested.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thorough review and insightful comments on our manuscript. We address each of the major comments below, providing clarifications and indicating where revisions will be made to enhance the manuscript's clarity and completeness.

read point-by-point responses

  1. Referee: The description of the data-driven inversion constrained by polar coronal holes does not include details on the inversion conditioning, potential degeneracies, or validation against independent regions, which is necessary to confirm that the reported 67% rms noise reduction and 3.7x signal-to-background improvement are not affected by over-fitting or bias in streamer structures.

    Authors: We agree that additional details on the inversion process are warranted to strengthen the claims. In the revised manuscript, we will include a dedicated subsection describing the inversion conditioning, such as the regularization techniques employed and the choice of constraints from polar coronal holes. We will discuss potential degeneracies, for example between dust particle size distribution and scattering cross-section, and how they are mitigated. Furthermore, we will validate the method against independent regions, such as streamer belts, by comparing corrected images with simultaneous SDO/AIA observations to demonstrate that the noise reduction is not biased by the choice of constraint regions. revision: yes

  2. Referee: No specific equations, error budgets, or procedural details are provided for the forward modeling, PSF construction, or the radial profile fitting that yields the 2.0 MK temperature, making it challenging to assess the robustness of the hydrostatic decay restoration claim.

    Authors: We acknowledge the need for more explicit technical details. We will revise the methods section to provide the specific equations governing the forward modeling of dust scattering, including the construction of the point-spread function based on optical defocus parameters. An error budget will be added, outlining the contributions from measurement noise, model assumptions, and inversion uncertainties. For the radial profile fitting, we will detail the exponential decay model used, the fitting procedure, and how the scale height is converted to the plasma temperature of approximately 2.0 MK, consistent with the Fe XIV line formation temperature. These additions will allow for a better assessment of the robustness. revision: yes

Circularity Check

0 steps flagged

No significant circularity; derivation uses external monitoring data and independent validation

full rationale

The paper's chain proceeds from dual-path lens imaging (providing measured dust distribution) to explicit PSF construction via defocus parameters, convolution for background model, and data-driven inversion. Parameters are retrieved under polar-hole constraints, but the quantitative claims (67% rms reduction, 3.7x S/B gain, restored 2.0 MK hydrostatic profile) are demonstrated via direct comparison to independent SDO/AIA space-based images and physical consistency checks, not by re-deriving the inputs. No step reduces by construction to a self-fit, self-citation, or renamed ansatz; the polar-hole constraint functions as an external prior rather than an internal tautology. The method remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The method rests on a physical PSF model whose parameters are fitted via inversion; the only explicit external constraint is the assumption that polar coronal holes supply a reliable reference background.

free parameters (1)
  • dust-scattering model parameters
    Retrieved through data-driven inversion constrained by polar coronal holes
axioms (1)
  • domain assumption Polar coronal holes provide a spatially uniform, known-intensity reference region suitable for constraining the stray-light model
    Used to retrieve model parameters via inversion

pith-pipeline@v0.9.0 · 5606 in / 1206 out tokens · 43397 ms · 2026-05-14T18:58:20.856833+00:00 · methodology

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