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REVIEW 3 major objections 5 minor 76 references

Optimising reference library selection for reference-star differential imaging of discs with SPHERE/IRDIS

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read In reference-star differential imaging, blended reference libraries give the best disc signal and most consistent contrast, while pure correlation picks give the best average contrast.

desk verdict Solid, honest empirical comparison of RDI reference-selection metrics; the contrast analysis is the robust part, but the S/N ranking that favors mixed libraries is more fragile than the abstract lets on. read the letter →

arxiv 2509.03325 v1 pith:XQLC6Z25 submitted 2025-09-03 astro-ph.EP astro-ph.IMastro-ph.SR

S. Stasevic , J. Milli , J. Mazoyer , A.-M. Lagrange , S. Bergeon This is my paper · ORCID
classification astro-ph.EPastro-ph.IMastro-ph.SR
keywords reference-stardifferentialimagingRDIreferencelibraryselectionSPHERE/IRDIScircumstellardiscspole-onprincipalcomponentanalysisPearsoncorrelationcoefficient
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

This paper tests how the choice of reference frames affects reference-star differential imaging (RDI) of faint, face-on circumstellar discs with SPHERE/IRDIS. The authors compare libraries built by matching ten different observational, atmospheric, and stellar parameters, by frame-to-frame Pearson correlation alone, by mixing all ten parameter-matched subsets, and by random selection. Their central result is that diverse 'mixed' libraries give the best disc signal-to-noise and the most consistent contrast across targets, while pure correlation-based libraries give the best average contrast. They conclude that mixed libraries are the practical choice for the upcoming large-scale RDI reduction of archival SPHERE/IRDIS data in the search for new discs.

What carries the argument

The central object is the reference library construction scheme. For each science frame, the authors preselect the 10,000 master-library frames whose metadata—seeing, coherence time, 30m wind, 200mbar wind, elevation, epoch, detector integration time, G/H magnitudes, and spectral type—most closely match the science frame; the final 1000-frame library keeps the highest frame-to-frame Pearson correlation coefficient computed in the speckle-dominated annulus between 0.18 and 0.43 arcseconds. A 'mixed' library combines 100 such frames from each of the ten parameter preselects. These libraries feed a PCA-based RDI subtraction, and performance is judged by throughput-corrected contrast on syntheti

What would settle it

Run the same 20-target synthetic-disc experiment with the S/N measured by an automated aperture centered on the injected disc model, with no hand-drawn regions, and check whether the mixed library still ranks first; if the ordering changes, the reported S/N advantage is a measurement artifact.

Watch

Extended reading notes

Core claim

The paper's claim is that for PCA-based RDI reductions of pole-on discs, no single selection criterion is enough: a reference library assembled by taking equal numbers of high-correlation frames from sub-libraries preselected to match different noise-determining parameters outperforms libraries built from any one criterion. On 20 disc-free sequences with synthetic pole-on discs, mixed libraries always stayed within a factor of two of the best contrast, and on seven real disc targets they produced the highest mean disc S/N. Pure Pearson-correlation libraries produced the best mean contrast overall, but with a larger worst-case spread, while libraries selected by frames close in time performed

Load-bearing premise

The comparisons assume the hand-drawn regions used to measure disc S/N are unbiased; the paper itself reports that slightly changing these regions can reorder the selection metrics, so a biased region choice could make mixed libraries look best when they are not.

Editorial extensions

If this is right

  • If this preference is right, the coming large-scale reduction of archival SPHERE/IRDIS data should use mixed reference libraries as the default selection, gaining the most consistent contrast and best disc S/N at lower computational cost than pure correlation libraries.
  • Discs at small separations, around 20px, should additionally or alternatively be reduced with epoch-matched libraries; these were best for about 30% of configurations and reached normalized S/N of at least 0.95 for the real small discs in the sample.
  • Pure Pearson-correlation libraries remain the best choice when the goal is lowest average contrast, such as for individual targeted reductions where computational cost is not a concern.
  • Random selection is strongly suboptimal, and selection on spectral type or detector integration time performs little better than random, so these should not be used as primary selection metrics.
  • The same selection scheme should transfer to other ground-based high-contrast instruments with large archives and atmospheric metadata, such as the Gemini Planet Imager.

Reading between the lines

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

  • A likely reason mixed libraries win is that real speckle patterns combine several decorrelated noise sources—wind-driven halos, low-wind effect, seeing residuals—and no single parameter or correlation score captures all of them; a testable extension would be adding more noise-descriptive parameters, such as scintillation strength, and checking whether the mixed-library margin grows.
  • The manual S/N region selection is the fragile point: an automated, model-matched measurement could decide whether the mixed library's S/N advantage is real or a region-choice artifact.
  • The epoch result hints that for observing strategies with dense temporal sampling, such as star-hopping, time-nearest reference selection may be competitive with image-similarity metrics, a hypothesis that could be tested directly on star-hopping sequences.
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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. The paper addresses reference-library selection for PCA-based reference-star differential imaging (RDI) of circumstellar discs, with a focus on pole-on discs observed with SPHERE/IRDIS. The authors construct 13 library types: ten metadata-matched libraries, a mixed library combining subsets matched on different parameters, a pure Pearson-correlation (PCC) library, and a random library. These are tested on 20 disc-free targets with synthetic disc throughput corrections at four radii and two disc widths, and on seven known disc targets whose disc S/N is measured in visually defined regions. The main claims are that mixed libraries achieve the best disc S/N and the smallest deviation from the best contrast per target, while PCC-only libraries achieve the best mean throughput-corrected contrast; epoch-based libraries perform well for small-separation discs. The authors recommend mixed libraries for large-scale archival reductions.

Significance. If the conclusions are robust, this is a practical and timely contribution: it directly informs the planned large-scale reduction of archival SPHERE/IRDIS data for disc searches, and it compares physically motivated selection metrics under realistic observing conditions. The study is carefully designed, uses a moderate but well-characterized sample (20 disc-free + 7 disc targets), and is transparent about many limitations. The contrast analysis with synthetic disc throughput is a useful empirical benchmark, and the finding that the mixed library consistently stays within a factor of two of the best contrast is a concrete, falsifiable claim. However, the headline S/N result is supported by only seven targets and by mean differences of 0.01 in normalized S/N, with no significance testing or region-robustness analysis. The paper's own caveats indicate that the ranking is fragile, yet the abstract and conclusions present the mixed library as the unequivocally best S/N choice. Strengthening or softening this claim is essential before the recommendation is adopted.

major comments (3)
  1. [Section 5, Fig. 5] The headline result that the mixed library gives the best disc S/N is not statistically supported. The mean normalized S/N is 0.84 for mixed versus 0.83 for epoch and PCC over only seven targets, and the paper itself states that slight variations of the manually defined S/N regions (Sect. 3.4, Appendix B) can change the ordering. No bootstrap, permutation, or leave-one-target-out analysis is provided, and no test of sensitivity to the chosen S/N aperture is reported. Furthermore, the mixed library has a minimum normalized S/N of 0.55, worse than PCC's 0.66, so 'best' depends entirely on a small mean difference. Please add uncertainty estimates or a region-robustness test, and if the difference is not significant, revise the abstract and conclusion to say mixed is among the best rather than the best.
  2. [Section 4.2, Fig. 2] For non-wind-effect observing conditions the paper reports that the five best selection metrics fall within 10% of each other and that PCC, seeing, and mixed are within ~5%, then states that no meaningful ranking can be drawn. This caveat is not carried into the Conclusions, where mixed and PCC are described as giving the best mean contrast for LWE/WDH and PCC as best for the non-wind groups. With only about four science targets per observing-condition group (Table 2) and no significance tests, these subgroup rankings are not established. Please add confidence intervals or a by-target bootstrap, or explicitly label the condition-dependent analysis as exploratory.
  3. [Section 4.1, Fig. 1] The contrast comparison reports means such as PCC 1.16 and mixed 1.21 with no associated uncertainties. The 160 measurements (20 targets x 4 radii x 2 widths) are treated as independent, but they are not: the same target appears eight times, and measurements at different radii and widths for a given target are correlated. A difference of 0.05 in normalized contrast may or may not be meaningful, and the claim that PCC achieves the best mean contrast is therefore not quantitatively supported. A hierarchical or target-bootstrap resampling scheme should be used to attach uncertainties to the means and to the statement that PCC is best on average.
minor comments (5)
  1. [Abstract] The phrase 'single criteria' should be 'single criterion'.
  2. [Section 5] Typo: 'HD 1000453' should be 'HD 100453'.
  3. [Fig. 5 caption] The caption states the metrics are 'ordered from top to bottom by ascending mean', but the figure shows the best-performing mixed library at the top and the worst-performing random library at the bottom, which is descending mean. This is inconsistent with the other figure captions.
  4. [Section 3.2] The sentence 'For this study, we chose to build unique reference libraries for each science frame and wavelength channel, consisting of 1000 reference frames each' could be clarified to state explicitly whether 1000 frames are selected per wavelength channel or per cube.
  5. [Section 3.4] The small-sample penalty term from Mawet et al. (2014) is mentioned, but the reader must consult the reference to see the exact formula. Including the formula or a reference to the equation in that paper would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the library ranking is an empirical comparison with transparent normalization; self-citations are contextual and not load-bearing.

full rationale

This paper is an empirical benchmark comparison, not a derivation. The central claims—mixed libraries give the best disc S/N and smallest deviation from best contrast, while PCC-only libraries give the best mean contrast—are read directly from measured contrast and S/N distributions over 20 disc-free targets with injected synthetic discs and 7 real disc targets. No parameter in the comparison is fitted to the headline ranking; the per-target normalization by the best-performing library is a display convention, not an input that forces the ranking. The throughput correction is computed independently for each library by projecting synthetic discs onto that library's principal components, so it does not smuggle the real-disc S/N result into the contrast analysis. Citations to Juillard et al. (2024) and Romero et al. (2024), both with overlapping authorship, are contextual and do not carry the argument: the paper's ranking is measured from archival SPHERE/IRDIS data, not inherited from those papers. The paper itself flags a genuine robustness limitation in Sect. 5: 'We have found that slight variations of the measurement regions can change the ordering of selection metrics with similar mean S/N values.' This is an honest caveat about the visually chosen S/N apertures, but it is a measurement-robustness concern, not a definitional equivalence between the library construction and the measured S/N. Because no step in the claimed comparison reduces by construction to its own input, no circularity is exhibited.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The paper introduces no invented physical entities and fits no model constants. The load-bearing choices are methodological hyperparameters (library size, preselection size, PCC annulus, mixed composition) and domain assumptions about the validity of the similarity and forward-modelling procedures. All are transparently stated and tested to varying degrees.

free parameters (4)
  • reference library size = 1000 frames
    Chosen as a compromise between storage and contrast; the contrast vs library size dependence (Ruane et al. 2019; Xie et al. 2022) means this choice could affect the ranking.
  • preselection subset size = 10,000 frames (~15-18% of master library)
    Chosen to keep parameter-matched frames while maintaining enough well-correlated frames; arbitrary and could influence which metrics appear useful.
  • PCC annulus = 0.18 to 0.43 arcsec
    Selected as the speckle-dominated region; using a different annulus could change the similarity metric and the resulting libraries.
  • mixed library composition = 100 frames per parameter from 5000 preselected
    The 100-frame-per-parameter split is an ad hoc choice; other splits might change the mixed library performance.
assumptions (4)
  • domain assumption The chosen metadata parameters (seeing, tau0, wind speeds/directions, elevation, epoch, DIT, G/H magnitudes, SpT) are meaningful proxies for speckle-noise similarity between science and reference frames.
    This is the premise of the entire parameter-preselection approach; the paper itself shows SpT and DIT are not useful, indicating not all proxies hold.
  • domain assumption The Pearson correlation coefficient computed in the 0.18-0.43 arcsec annulus on mean-subtracted frames captures frame similarity relevant to PCA subtraction.
    The paper uses PCC as the final ranking inside all libraries; if PCC is not a good similarity measure for speckle patterns, the libraries would be misbuilt.
  • domain assumption Forward modelling with synthetic pole-on discs from VIP, convolved with the stellar PSF, realistically reproduces over-subtraction for real discs.
    The throughput correction used to compute contrast assumes these models represent signal loss; the paper acknowledges real disc morphology may not be preserved.
  • domain assumption The master reference library, after removing targets with known discs/planets/binaries and satellite-spot observations, contains enough frames with similar noise properties for all science targets.
    If the library is sparse for some targets, the selection metric becomes less relevant and the comparisons break down.

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Cite this review

Pith. "Pith review of Optimising reference library selection for reference-star differential imaging of discs with SPHERE/IRDIS." pith.science (2026). https://pith.science/paper/XQLC6Z25

@misc{pith2026250903325,
  author       = {Pith},
  title        = {Pith review of: Optimising reference library selection for reference-star differential imaging of discs with SPHERE/IRDIS},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XQLC6Z25}},
  note         = {Machine review of arXiv:2509.03325}
}
read the original abstract

The direct detection of circumstellar discs through high-contrast imaging provides key insights into the history and dynamics of planetary systems. Pole-on discs, especially faint debris discs, are difficult to detect and require careful consideration during post-processing to remove stellar residuals from the data while preserving the disc signal. Reference-star differential imaging (RDI) serves as one of the primary post-processing methods for disc observations. We aim to develop a method of reference frame selection that is optimised for the reduction of pole-on discs. Method: We performed principal component analysis based RDI on seven known disc targets and 20 disc-free targets with varying observational conditions, using reference libraries built from frames preselected to best match different observational, atmospheric, and stellar parameters of the science frames. The contrast of the disc-free reductions was measured, and forward modelling was used to estimate the signal loss from over-subtraction using synthetic pole-on discs with two different widths and four different radii. The signal-to-noise ratio (S/N) of the real disc targets was measured. Results: Diverse reference libraries built using subsets of frames that closely matched different parameters achieved the best disc S/N and smallest deviation from the best contrast of each target, outperforming libraries built using a single criterion as a selection metric. Libraries built using frame-to-frame Pearson correlation coefficient alone as a selection criterion achieved the best mean contrast overall. Both selection metrics performed consistently well for all disc radii and observational conditions. We also found that reference libraries built using frames observed close in time to the science frame performed well for discs at small separations, giving the best contrast for ~30% of the targets at a radius of 20px.

Figures

Figures reproduced from arXiv: 2509.03325 by the authors.

Figure 1
Figure 1. Violin plot showing the contrast distribution of each reference library selection metric for all science targets, disc radii, and widths. The contrast was normalised by that of the parameter library giving the best performance under the same measurement conditions. The verti￾cal lines of the violin show the minimum, mean, and maximum values. Reference library selection metrics are ordered from top to bottom by desce… view at source ↗
Figure 2
Figure 2. Violin plots showing the contrast distribution of each reference library selection metric for different observing conditions of the science targets. Plots show the distribution for the LWE (orange; top), WDH (purple), good seeing (green), average seeing (blue), and bad seeing (red; bottom) science targets, including all disc radii and widths. The contrast was normalised by that of the parameter library giving the be… view at source ↗
Figure 3
Figure 3. Violin plot showing the contrast distribution when throughput is calculated using a narrow (blue, upper side) and wide (red, lower side) synthetic disc for all science targets and disc radii. The contrast was normalised by that of the parameter library giving the best performance under the same measurement conditions. The vertical lines of the violin show the minimum, mean, and maximum values. Reference library se￾l… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Violin plots showing the contrast distributions measured at a 20px (top), 40px, 60px, and 80px (bottom) separation for all science targets and disc widths. The contrast was normalised by that of the parameter library giving the best performance under the same measureme…
Figure 5
Figure 5. Figure 5: Violin plot showing the distribution of disc S/N measured on RDI reductions using reference libraries built with different selection met￾rics. The S/N measurements were normalised by the best S/N achieved for that disc using any reference library. The reference library…

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Works this paper leans on

76 extracted references · 58 canonical work pages

  1. [1]

    & Quanz, S

    Amara, A. & Quanz, S. P. 2012, MNRAS, 427, 948 Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. 2022, ApJ, 935, 167

    work page 2012

  2. [2]

    C., Lagrange, A

    Augereau, J. C., Lagrange, A. M., Mouillet, D., Papaloizou, J. C. B., & Grorod, P. A. 1999, A&A, 348, 557

    1999

  3. [3]

    L., Mouillet, D., Lagrange, A

    Beuzit, J. L., Mouillet, D., Lagrange, A. M., & Paufique, J. 1997, A&AS, 125, 175

    work page 1997

  4. [4]

    2019, A&A, 631, A155

    Beuzit, J.-L., Vigan, A., Mouillet, D., et al. 2019, A&A, 631, A155

    work page 2019

  5. [5]

    Bowler, B. P. 2016, PASP, 128, 102001

    work page 2016

  6. [6]

    2022, astropy /photutils: 1.6.0, Zen- odo, DOI: 10.5281/zenodo.7419741

    Bradley, L., Sip˝ocz, B., Robitaille, T., et al. 2022, astropy /photutils: 1.6.0, Zen- odo, DOI: 10.5281/zenodo.7419741

    doi:10.5281/zenodo.7419741 2022

  7. [7]

    2000, Dr

    Bradski, G. 2000, Dr. Dobb’s Journal of Software Tools, 25, 120

    work page 2000

  8. [8]

    2019, The Mes- senger, 176, 25

    Cantalloube, F., Dohlen, K., Milli, J., Brandner, W., & Vigan, A. 2019, The Mes- senger, 176, 25

    work page 2019

Show all 76 references
  1. [9]

    Cantalloube, F., Farley, O. J. D., Milli, J., et al. 2020, A&A, 638, A98

    2020

  2. [10]

    H., Dohlen, K., et al

    Cantalloube, F., Por, E. H., Dohlen, K., et al. 2018, A&A, 620, L10

    2018

  3. [11]

    2023, The Journal of Open Source Software, 8, 4774 Copernicus Climate Change Service

    Christiaens, V ., Gonzalez, C., Farkas, R., et al. 2023, The Journal of Open Source Software, 8, 4774 Copernicus Climate Change Service. 2023, ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), DOI: 10.24...

    2023 doi

  4. [12]

    2019, ApJ, 877, L3

    Currie, T., Marois, C., Cieza, L., et al. 2019, ApJ, 877, L3

    2019

  5. [13]

    M., Skrutskie, M

    Cutri, R. M., Skrutskie, M. F., van Dyk, S., et al. 2003, VizieR Online Data

    2003

  6. [14]

    2017, in SF2A-2017: Proceedings of the Annual meeting of the French Society of Astronomy and Astrophysics, ed

    Delorme, P., Meunier, N., Albert, D., et al. 2017, in SF2A-2017: Proceedings of the Annual meeting of the French Society of Astronomy and Astrophysics, ed. C. Reylé, P. Di Matteo, F. Herpin, E. Lagadec, A. Lançon, Z. Meliani, & F. Royer, Di

    2017

  7. [15]

    2008, in SPIE Proceedings, ed

    Dohlen, K., Langlois, M., Saisse, M., et al. 2008, in SPIE Proceedings, ed. I. S. McLean & M. M. Casali (SPIE)

    2008

  8. [16]

    R., & Brittain, S

    Dong, R., Najita, J. R., & Brittain, S. 2018, ApJ, 862, 103

    2018

  9. [17]

    2014, in Protostars and Planets VI, ed

    Espaillat, C., Muzerolle, J., Najita, J., et al. 2014, in Protostars and Planets VI, ed. H. Beuther, R. S. Klessen, C. P. Dullemond, & T. Henning, 497–520

    2014

  10. [18]

    M., Fitzgerald, M

    Esposito, T. M., Fitzgerald, M. P., Graham, J. R., & Kalas, P. 2014, ApJ, 780, 25

    2014

  11. [19]

    M., Kalas, P., Fitzgerald, M

    Esposito, T. M., Kalas, P., Fitzgerald, M. P., et al. 2020, AJ, 160, 24

    2020

  12. [20]

    Follette, K. B. 2023, PASP, 135, 093001 Gaia Collaboration. 2020, VizieR Online Data Catalog, I/350

    2023

  13. [21]

    Gonzalez, C. A. G., Wertz, O., Absil, O., et al. 2017, AJ, 154, 7

    2017

  14. [22]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357

    2020

  15. [23]

    2020, Quarterly Journal of the Royal Meteorological Society, 146, 1999

    Hersbach, H., Bell, B., Berrisford, P., et al. 2020, Quarterly Journal of the Royal Meteorological Society, 146, 1999

    2020

  16. [24]

    M., Duchêne, G., & Matthews, B

    Hughes, A. M., Duchêne, G., & Matthews, B. C. 2018, ARA&A, 56, 541

    2018

  17. [25]

    Hunter, J. D. 2007, Computing in Science & Engineering, 9, 90

    2007

  18. [26]

    Joye, W. A. & Mandel, E. 2003, in Astronomical Society of the Pacific Con- ference Series, V ol. 295, Astronomical Data Analysis Software and Systems XII, ed. H. E. Payne, R. I. Jedrzejewski, & R. N. Hook, 489

    2003

  19. [27]

    2024, A&A, 688, A185

    Juillard, S., Christiaens, V ., Absil, O., Stasevic, S., & Milli, J. 2024, A&A, 688, A185

    2024

  20. [28]

    & Jewitt, D

    Kalas, P. & Jewitt, D. 1995, AJ, 110, 794

    1995

  21. [29]

    2012, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, V ol

    Kasper, M. 2012, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, V ol. 8447, Adaptive Optics Systems III, ed. B. L. Eller- broek, E. Marchetti, & J.-P. Véran, 84470B

    2012

  22. [30]

    Kennedy, G. M. 2020, Royal Society Open Science, 7, 200063

    2020

  23. [31]

    2007, MNRAS, 382, 1268

    Kornilov, V ., Tokovinin, A., Shatsky, N., et al. 2007, MNRAS, 382, 1268

    2007

  24. [32]

    E., Stapelfeldt, K

    Krist, J. E., Stapelfeldt, K. R., Ménard, F., Padgett, D. L., & Burrows, C. J. 2000, ApJ, 538, 793

    2000

  25. [33]

    2007, ApJ, 660, 770

    Lafreniere, D., Marois, C., Doyon, R., Nadeau, D., & Artigau, E. 2007, ApJ, 660, 770

    2007

  26. [34]

    P., et al

    Lawson, K., Currie, T., Wisniewski, J. P., et al. 2022, ApJ, 935, L25 Lecavelier Des Etangs, A., Perrin, G., Ferlet, R., et al. 1993, A&A, 274, 877

    2022

  27. [35]

    Lee, E. J. & Chiang, E. 2016, ApJ, 827, 125

    2016

  28. [36]

    Lin, J. W. & Chiang, E. 2019, ApJ, 883, 68

    2019

  29. [37]

    2019, Journal of Astronomical

    Madurowicz, A., Macintosh, B., Chilcote, J., et al. 2019, Journal of Astronomical

    2019

  30. [38]

    2021, Journal of Astronomical

    Maire, A.-L., Langlois, M., Delorme, P., et al. 2021, Journal of Astronomical

    2021

  31. [39]

    2016, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, V ol

    Maire, A.-L., Langlois, M., Dohlen, K., et al. 2016, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, V ol. 9908, Ground- based and Airborne Instrumentation for Astronomy VI, ed. C. J. Evans, L. Simard, & H. Takami, 990834

    2016

  32. [40]

    2006, ApJ, 641, 556 Matrà, L., Marino, S., Wilner, D

    Marois, C., Lafreniere, D., Doyon, R., Macintosh, B., & Nadeau, D. 2006, ApJ, 641, 556 Matrà, L., Marino, S., Wilner, D. J., et al. 2025, A&A, 693, A151

    2006

  33. [41]

    2014, ApJ, 792, 97

    Mawet, D., Milli, J., Wahhaj, Z., et al. 2014, ApJ, 792, 97

    2014

  34. [42]

    2012, ApJ, 760, L26

    Mayama, S., Hashimoto, J., Muto, T., et al. 2012, ApJ, 760, L26

    2012

  35. [43]

    2020, in Society of Photo-Optical Instru- mentation Engineers (SPIE) Conference Series, V ol

    Mazoyer, J., Arriaga, P., Hom, J., et al. 2020, in Society of Photo-Optical Instru- mentation Engineers (SPIE) Conference Series, V ol. 11447, Ground-based and Airborne Instrumentation for Astronomy VIII, ed. C. J. Evans, J. J. Bryant, & K. Motohara, 1144759

    2020

  36. [44]

    A., & Watson, R

    McDonald, I., Zijlstra, A. A., & Watson, R. A. 2017, MNRAS, 471, 770

    2017

  37. [45]

    2010, in Proceedings of the 9th Python in Science Conference, ed

    McKinney, W. 2010, in Proceedings of the 9th Python in Science Conference, ed. Stéfan van der Walt & Jarrod Millman, 56–61

    2010

  38. [46]

    2016, in Society of Photo-Optical Instru- mentation Engineers (SPIE) Conference Series, V ol

    Milli, J., Banas, T., Mouillet, D., et al. 2016, in Society of Photo-Optical Instru- mentation Engineers (SPIE) Conference Series, V ol. 9909, Adaptive Optics Systems V , ed. E. Marchetti, L. M. Close, & J.-P. Véran, 99094Z

    2016

  39. [47]

    2018, in Adaptive Optics Systems VI, ed

    Milli, J., Mouillet, D., Beuzit, J.-L., et al. 2018, in Adaptive Optics Systems VI, ed. D. Schmidt, L. Schreiber, & L. M. Close (SPIE)

    2018

  40. [48]

    M., et al

    Milli, J., Mouillet, D., Lagrange, A. M., et al. 2012, A&A, 545, A111

    2012

  41. [49]

    M., Beuzit, J

    Mouillet, D., Lagrange, A. M., Beuzit, J. L., & Renaud, N. 1997, A&A, 324, 1083

    1997

  42. [50]

    & Stapelfeldt, K

    Padgett, D. & Stapelfeldt, K. 2016, in IAU Symposium, V ol. 314, Young Stars & Planets Near the Sun, ed. J. H. Kastner, B. Stelzer, & S. A. Metchev, 175–178

    2016

  43. [51]

    2008, in Astronomical Society of the Pa- cific Conference Series, V ol

    Pavlov, A., Feldt, M., & Henning, T. 2008, in Astronomical Society of the Pa- cific Conference Series, V ol. 394, Astronomical Data Analysis Software and Systems XVII, ed. R. W. Argyle, P. S. Bunclark, & J. R. Lewis, 581

    2008

  44. [52]

    Pearce, T. D. 2024, arXiv e-prints, arXiv:2403.11804

    2024 arXiv

  45. [53]

    D., Krivov, A

    Pearce, T. D., Krivov, A. V ., Sefilian, A. A., et al. 2024, MNRAS, 527, 3876 Pourré, N., Le Bouquin, J. B., Milli, J., et al. 2022, A&A, 665, A158

    2024

  46. [54]

    Racine, R., Walker, G. A. H., Nadeau, D., Doyon, R., & Marois, C. 1999, PASP, 111, 587

    1999

  47. [55]

    B., Debes, J., & Duchêne, G

    Ren, B., Pueyo, L., Zhu, G. B., Debes, J., & Duchêne, G. 2018, ApJ, 852, 104

    2018

  48. [56]

    Ren, B. B. 2023, A&A, 679, A18

    2023

  49. [57]

    1981, Progress in Optics, 19, 281

    Roddier, F. 1981, Progress in Optics, 19, 281

    1981

  50. [58]

    1999, Adaptive Optics in Astronomy (Cambridge University Press), google-Books-ID: 4n5tBN21LRsC

    Roddier, F. 1999, Adaptive Optics in Astronomy (Cambridge University Press), google-Books-ID: 4n5tBN21LRsC

    1999

  51. [59]

    M., et al

    Romero, C., Milli, J., Lagrange, A. M., et al. 2024, A&A, 691, A267

    2024

  52. [60]

    2019, AJ, 157, 118

    Ruane, G., Ngo, H., Mawet, D., et al. 2019, AJ, 157, 118

    2019

  53. [61]

    W., Wang, J

    Sanghi, A., Xuan, J. W., Wang, J. J., et al. 2024, AJ, 168, 215

    2024

  54. [62]

    & Roddier, F

    Sarazin, M. & Roddier, F. 1990, A&A, 227, 294

    1990

  55. [63]

    2015, in Adaptive Optics for Ex- tremely Large Telescopes IV (AO4ELT4), E9

    Sauvage, J.-F., Fusco, T., Guesalaga, A., et al. 2015, in Adaptive Optics for Ex- tremely Large Telescopes IV (AO4ELT4), E9

    2015

  56. [64]

    D., Hines, D

    Schneider, G., Silverstone, M. D., Hines, D. C., et al. 2006, ApJ, 650, 414

    2006

  57. [65]

    A., Becklin, E

    Schneider, G., Smith, B. A., Becklin, E. E., et al. 1999, ApJ, 513, L127

    1999

  58. [66]

    Smith, B. A. & Terrile, R. J. 1984, Science, 226, 1421 Smithsonian Astrophysical Observatory. 2000, SAOImage DS9: A utility for dis- playing astronomical images in the X11 window environment, Astrophysics Source Code Library, record ascl:0003.002

    1984

  59. [67]

    2012, ApJ, 755, L28

    Soummer, R., Pueyo, L., & Larkin, J. 2012, ApJ, 755, L28

    2012

  60. [68]

    A., et al

    Terrill, J., Marino, S., Booth, R. A., et al. 2023, MNRAS, 524, 1229 The Pandas Development Team. 2024, pandas-dev /pandas: Pandas, Zenodo, DOI: 10.5281/zenodo.3509134

    2023 doi

  61. [69]

    2010, MNRAS, 407, 71

    Vigan, A., Moutou, C., Langlois, M., et al. 2010, MNRAS, 407, 71

    2010

  62. [70]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261

    2020

  63. [71]

    2015, ApJ, 813, L2

    Wagner, K., Apai, D., Kasper, M., & Robberto, M. 2015, ApJ, 813, L2

    2015

  64. [72]

    2021, A&A, 648, A26

    Wahhaj, Z., Milli, J., Romero, C., et al. 2021, A&A, 648, A26

    2021

  65. [73]

    J., Becklin, E

    Weinberger, A. J., Becklin, E. E., Schneider, G., et al. 1999, ApJ, 525, L53

    1999

  66. [74]

    2000, A&AS, 143, 9

    Wenger, M., Ochsenbein, F., Egret, D., et al. 2000, A&AS, 143, 9

    2000

  67. [75]

    Wyatt, M. C. & Dent, W. R. F. 2002, MNRAS, 334, 589

    2002

  68. [76]

    2022, A&A, 666, A32 Article number, page 11 of 16 A&A proofs: manuscript no

    Xie, C., Choquet, E., Vigan, A., et al. 2022, A&A, 666, A32 Article number, page 11 of 16 A&A proofs: manuscript no. aa55338-25 Appendix A: Science target metadata Table A.1. Science target observation sequences used in this paper and their metadata. Target name SpT MG MH Obse...

    2022

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