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REVIEW 3 major objections 3 minor 27 references

spherical: A Comprehensive Database and Automated Pipeline for VLT/SPHERE High-Contrast Imaging

T0 review · 3 major / 3 minor · reviewed 2026-08-04 · deepseek-v4-flash

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Pith's one-line read The paper presents spherical, a curated database of every VLT/SPHERE observation matched to stellar properties, plus an automated Python pipeline that reduces raw IFS frames to calibrated spectral cubes and companion spectra.

desk verdict Useful integration of SPHERE tools and a new archive database, but the 'all/complete' claim is scoped down by the paper's own exclusion list and no validation is shown. read the letter →

arxiv 2509.08044 v1 pith:AHJWSLBT submitted 2025-09-09 astro-ph.IM astro-ph.EP

Matthias Samland This is my paper
classification astro-ph.IMastro-ph.EP
keywords SPHEREhigh-contrastimagingintegralfieldspectroscopydatareductionpipelineexoplanetdirectobservatoryarchivecircumstellardisksPythonsoftware
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 claims that the largest archive of direct exoplanet imaging can be made uniformly usable. It introduces spherical, a searchable table of all VLT/SPHERE observations (about 6,000 IRDIS dual-band, 1,000 IRDIS polarimetric, and 4,500 IFS sequences) cross-matched with stellar properties and observing conditions, together with a script-driven pipeline that carries IFS data from raw frames to calibrated spectral cubes, companion detection limits, and extracted spectra. If correct, a researcher no longer needs to assemble metadata by hand or stitch together separate reduction tools; the same query-and-reduce workflow works across the archived history of the instrument. That matters because homogeneous re-analysis of archival data is how occurrence-rate and population studies and searches for fainter companions are done.

What carries the argument

The load-bearing pieces are the observation table and the IFS reduction chain. The table is built by header ingestion followed by Gaia cross-matching, where ambiguity is resolved by proximity and brightness; the assumption that this identifies the intended target of every sequence is what makes the database of all observations automatic. The reduction chain is script-driven: astroquery handles retrieval from the observatory archive, an adapted CHARIS pipeline extracts spectral cubes, routines from vlt-sphere provide astrometric and photometric calibration, and TRAP produces detection maps, contrast limits, and companion spectra. The design choice that carries the argument is that every step,

What would settle it

Query the observatory archive for all SPHERE observation blocks and compare the count and target identifications with the rows in spherical's released table; any missing sequence or any cross-match that disagrees with the actual pointing by more than a few arcseconds would invalidate the completeness claim. Likewise, reducing a set of archived IFS sequences with known companions and comparing recovered astrometry and spectra against published values would test the science-readiness claim.

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Extended reading notes

Core claim

The central claim is that the whole SPHERE observing history can be captured in one regenerable table, and raw IFS observations can be reduced automatically. The database is produced by parsing observatory archive headers for every SPHERE sequence, identifying the target by cross-matching to Gaia by proximity and brightness, and combining the result with exposure, mode, and observing-condition metadata. The pipeline downloads the selected raw data and calibrations, extracts spectral cubes with an adapted version of the CHARIS pipeline, applies astrometric and photometric calibration, and runs TRAP for companion detection and spectral extraction. What the author is trying to establish is that

Load-bearing premise

The load-bearing premise is that the observatory's archive headers are complete enough, and the automatic star-catalog match by sky position and brightness is accurate enough, that every raw observation can be labeled correctly without human checks; if headers are missing or ambiguous, the all-observations claim fails.

Editorial extensions

If this is right

  • A researcher can assemble a sample of thousands of SPHERE observations with consistent metadata, and reduce the IFS subset under identical settings, removing a layer of heterogeneity from population and occurrence-rate studies.
  • Re-analysis of archival data becomes cheap enough to rerun whenever post-processing algorithms improve, which can push detection limits toward lower-mass companions.
  • Survey follow-up is faster because a query can immediately list all observations of a target across IFS, IRDIS dual-band, and IRDIS polarimetric modes, including conditions.
  • Because the tables are archived and regenerable, the database itself is reproducible rather than a static hand-built list.
  • The script-driven pipeline makes reductions version-controllable and citable, so science products can be traced back to specific code parameters.

Reading between the lines

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

  • If the database stays current through the SPHERE+ upgrade, it could become the standard pre-observation check for what has already been observed, reducing duplicate archival coverage.
  • The same header-ingestion-plus-star-catalog cross-match recipe is generic enough to export to other instruments or observatory archives, making spherical a template for survey-level data organization.
  • A direct test of the pipeline's scientific value would be to reduce every archived IFS sequence and check recovered companion positions and spectra against published results; systematic deviations would flag calibration steps that need per-epoch treatment.
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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 / 3 minor

Summary. The paper presents spherical, a Python package and database for VLT/SPHERE high-contrast imaging. It combines (1) a database of SPHERE observations—approximately 6000 IRDIS DBI, ~1000 IRDIS DPI, and ~4500 IFS sequences—cross-matched with Gaia stellar properties and observing conditions, archived on Zenodo and regenerable from the ESO archive; and (2) an automated IFS reduction pipeline that retrieves raw data, calibrates, extracts spectral cubes using an adapted CHARIS pipeline, applies astrometric/photometric calibration, and runs TRAP post-processing. The stated goal is to take users from raw frames to science-ready products, enabling homogeneous population studies and efficient follow-up. The manuscript claims the database is 'complete' and covers 'all SPHERE observations,' yet it also lists ZIMPOL, IRDIS-LSS, and SAM as future releases. No validation metrics, example outputs, or comparisons against existing pipelines are reported.

Significance. If substantiated, spherical would fill a genuine gap: the SPHERE archive is the largest high-contrast imaging dataset, and its metadata are heterogeneous; a unified, searchable database plus a script-driven pipeline would enable population studies and reanalyses. The paper's strengths are that the software is publicly available, the database is archived on Zenodo with scripts to regenerate it, and the IFS pipeline builds on independently published, peer-reviewed components (CHARIS, TRAP, vlt-sphere). However, the central claims of database completeness and 'science-ready' output are currently asserted rather than demonstrated, so the significance is conditional on the validation and scope revisions requested below.

major comments (3)
  1. [Abstract; 'What spherical can do'; 'Statement of Need'] The database is advertised as containing 'all SPHERE observations' (Abstract), as the 'complete SPHERE observation history' (What spherical can do), and as 'a complete, regularly updated SPHERE observation database' (Statement of Need). The same text states that ZIMPOL, IRDIS-LSS, and SAM are 'planned for future releases.' These three modes are part of the SPHERE observation history, so the completeness claim is internally inconsistent unless explicitly scoped to IRDIS DBI/DPI and IFS. This is load-bearing because population studies that use the database as a census of SPHERE observations would be incomplete by design. Please restate the scope precisely in every occurrence or withdraw 'all'/'complete'.
  2. [Database generation (maintainer workflow); Future Work] No validation of the database is described. The step list (header ingestion, cross-matching, table construction, archival release) contains no comparison against ESO archive counts, no audit of Gaia cross-matches, and no known-target recovery test. The reported counts (~6000 DBI, ~1000 DPI, ~4500 IFS) are asserted without a completeness check. Future Work confirms that 'a small public test dataset for continuous integration' is still planned, i.e., currently absent. Given that the cross-match resolves ambiguities 'by proximity and brightness,' which can fail in crowded fields or for binaries, the 'curated' claim needs at least one quantitative validation step (e.g., archive-count reconciliation and a recovery test on a sample of known targets).
  3. [User workflow (analysis); Scientific Use] The claim that spherical takes users 'from raw IFS frames to calibrated spectral cubes and exoplanet characterization' and yields 'science-ready products' is not supported by any example output, validation metric, or comparison against existing reductions (e.g., vlt-sphere, ESO pipeline, or published SPHERE results). The pipeline reuses CHARIS and TRAP, which are individually validated, but the integration—including the astrometric/photometric calibration routines adapted from vlt-sphere—needs an end-to-end demonstration. Please include at least one worked example with measured astrometric/photometric accuracy, a contrast curve, and a comparison to a published reduction.
minor comments (3)
  1. [Summary, first paragraph] Typographical artifacts such as 'acurated, searchable databaselisting' and 'pipelinefor SPHERE'sIntegral Field Spectrograph'—missing spaces. Please proofread.
  2. [References] 'The pandas development team (n.d.)' should include a version/year and URL; 'vlt-sphere (Arthur Vigan, 2020)' is styled differently from the author-year format used elsewhere.
  3. [Abstract] The database counts ('about 6000', 'about 1000', 'about 4500') are introduced without a stated cutoff date; later 'As of May 2025' appears. The Abstract should be consistent (e.g., 'as of May 2025').

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the database is assembled from external ESO archive headers and Gaia cross-matching, and the pipeline components (CHARIS, TRAP) are independently published, code-reproduced methods; the paper's admitted coverage gaps are completeness caveats, not circular reasoning.

full rationale

Walked the paper's derivation chain. The database generation workflow is: parse ESO archive headers, cross-match with Gaia by proximity and brightness, construct observation tables, and release on Zenodo. The IFS pipeline adapts the CHARIS pipeline and TRAP, both previously published with their own validation (Brandt et al. 2017; Samland et al. 2021; Samland et al. 2022). There is no fitted parameter later relabeled as a prediction, no equation where the target result is defined in terms of the inputs, and no uniqueness theorem imported from the author's own prior work to force a particular choice. The self-citations are to independently published, code-reproduced algorithms and therefore count as real evidence under the review rules, not circularity. The manuscript does contain clear self-acknowledged limitations: the abstract and bullet list claim 'all SPHERE observations' and 'the complete SPHERE observation history,' while the text also says 'Other modes—ZIMPOL..., IRDIS long-slit spectroscopy (LSS...), and Sparse Aperture Masking (SAM...)—are planned for future releases,' and Future Work lists 'Provide a small public test dataset for continuous integration of selected pipeline stages' as planned rather than delivered. These are overclaim/completeness and validation gaps, not circular steps: the central database/pipeline contribution is not constructed from its own outputs. Therefore no circularity is found; score 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities or fitted parameters. Its central claim rests on domain assumptions about the ESO archive, Gaia cross-matching, and the validity of reused pipelines (CHARIS, TRAP) for SPHERE IFS.

assumptions (3)
  • domain assumption The ESO archive contains parseable headers and raw data for every SPHERE observation, allowing complete database construction.
    Invoked in 'Database generation (maintainer workflow)'; the database is claimed to include about 6000 IRDIS DBI, 1000 DPI, and 4500 IFS sequences. No checks on header completeness are reported.
  • domain assumption Gaia cross-matching by proximity and brightness unambiguously identifies the correct target star for each observation.
    Stated in the 'Cross-matching' step of database generation; no error rate or ambiguity statistics are given.
  • domain assumption The adapted CHARIS pipeline and TRAP post-processor correctly reduce SPHERE IFS data without retuning.
    The 'Reduce (IFS)' and 'Post-process' steps reuse Samland et al. (2022) and Samland et al. (2021); the paper provides no new validation for SPHERE IFS.

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Cite this review

Pith. "Pith review of spherical: A Comprehensive Database and Automated Pipeline for VLT/SPHERE High-Contrast Imaging." pith.science (2026). https://pith.science/paper/AHJWSLBT

@misc{pith2026250908044,
  author       = {Pith},
  title        = {Pith review of: spherical: A Comprehensive Database and Automated Pipeline for VLT/SPHERE High-Contrast Imaging},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/AHJWSLBT}},
  note         = {Machine review of arXiv:2509.08044}
}
read the original abstract

I present spherical (https://github.com/m-samland/spherical), a software package and database designed for the ESO VLT/SPHERE high-contrast imager. SPHERE has produced the world's largest archive of direct imaging observations of exoplanets and circumstellar disks, but its heterogeneous metadata and fragmented reduction tools make end-to-end analysis labor-intensive. spherical addresses this by combining (1) a curated, regularly updated, and searchable database of all SPHERE observations, cross-matched with stellar properties and observing conditions, and (2) a Python-based, script-driven pipeline for the Integral Field Spectrograph (IFS). The database, archived on Zenodo (https://doi.org/10.5281/zenodo.15147730) and reproducible from the ESO archive, currently includes about 6000 IRDIS dual-band imaging, about 1000 IRDIS polarimetric, and about 4500 IFS sequences, with additional modes (ZIMPOL, IRDIS-LSS, SAM) planned. The pipeline automates raw data retrieval, calibration, and IFS reduction with the adapted open-source CHARIS instrument pipeline, followed by astrometric and photometric calibration and post-processing with TRAP for companion detection and spectral extraction. spherical lowers the barrier from raw files to science-ready products, enabling homogeneous population studies, atmospheric characterization of companions, and efficient survey follow-up, while remaining interoperable with community tools such as VIP, pyKLIP, and IRDAP.

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Works this paper leans on

27 extracted references · 8 canonical work pages

  1. [1]

    M., Lim, P

    Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., Earl, N., Starkman, N., Bradley, L., Shupe, D. L., Patil, A. A., Corrales, L., Brasseur, C. E., Nöthe, M., Donath, A., Tollerud, E., Morris, B. M., Ginsburg, A., Vaher, E., Weaver, B. A., Tocknell, J., Jamieson, W., Astropy Project Contributors. (2022). The Astropy Project: Sustaining and Growing a C...

    2022

  2. [2]

    M., Sipőcz, B

    Astropy Collaboration, Price-Whelan, A. M., Sipőcz, B. M., Günther, H. M., Lim, P. L., Crawford, S. M., Conseil, S., Shupe, D. L., Craig, M. W., Dencheva, N., Ginsburg, A., VanderPlas, J. T., Bradley, L. D., Pérez-Suárez, D., de Val-Borro, M., Aldcroft, T. L., Cruz, K. L., Robitaille, T. P., Tollerud, E. J., Astropy Contributors. (2018). The Astropy Proje...

    doi:10.3847/1538- 2018

  3. [3]

    P., Tollerud, E

    Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., Greenfield, P., Droettboom, M., Bray, E., Aldcroft, T., Davis, M., Ginsburg, A., Price-Whelan, A. M., Kerzendorf, W. E., Conley, A., Crighton, N., Barbary, K., Muna, D., Ferguson, H., Grollier, F., Parikh, M. M., Nair, P. H., Streicher, O. (2013). Astropy: A community Python package for astronomy ...

    doi:10.1051/0004-6361/201322068 2013

  4. [4]

    M., Langlois, M., Petit, C., Baruffolo, A., Feldt, M., Milli, J., Wahhaj, Z., Abe, L., Anselmi, U., Antichi, J., Barette, R., Baudrand, J., Zurlo, A

    Beuzit, J.-L., Vigan, A., Mouillet, D., Dohlen, K., Gratton, R., Boccaletti, A., Sauvage, J.-F., Schmid, H. M., Langlois, M., Petit, C., Baruffolo, A., Feldt, M., Milli, J., Wahhaj, Z., Abe, L., Anselmi, U., Antichi, J., Barette, R., Baudrand, J., Zurlo, A. (2019). SPHERE: the exoplanet imager for the Very Large Telescope . Astronomy & Astrophysics, 631, ...

    doi:10.1051/0004-6361/201935251 2019

  5. [5]

    Boccaletti, A., Chauvin, G., Mouillet, D., Absil, O., Allard, F., Antoniucci, S., Augereau, J.-C., Barge, P., Baruffolo, A., Baudino, J.-L., Baudoz, P., Beaulieu, M., Benisty, M., Beuzit, J.-L., Bianco, A., Biller, B., Bonavita, B., Bonnefoy, M., Bos, S., Zurlo, A. (2020). SPHERE+: Imaging young Jupiters down to the snowline . arXiv e-Prints, arXiv:2003.0...

    arXiv doi:10.48550/arxiv.2003.05714 2020

  6. [6]

    Boccaletti, A., Chauvin, G., Wildi, F., Milli, J., Stadler, E., Diolaiti, E., Gratton, R., Vidal, F., Loupias, M., Langlois, M., Cantalloube, F., N'Diaye, M., Gratadour, D., Ferreira, F., Tallon, M., Mazoyer, J., Segransan, D., Mouillet, D., Beuzit, J.-L., Zanutta, A. (2022). Upgrading the high contrast imaging facility SPHERE: science drivers and instrum...

    work page 2022

  7. [7]

    D., Rizzo, M., Groff, T., Chilcote, J., Greco, J

    Brandt, T. D., Rizzo, M., Groff, T., Chilcote, J., Greco, J. P., Kasdin, N. J., Limbach, M. A., Galvin, M., Loomis, C., Knapp, G., McElwain, M. W., Jovanovic, N., Currie, T., Mede, K., Tamura, M., Takato, N., & Hayashi, M. (2017). Data reduction pipeline for the CHARIS integral-field spectrograph I: detector readout calibration and data cube extraction . ...

    doi:10.1117/1.jatis.3.4.048002 2017

  8. [8]

    Chauvin, G., Desidera, S., Lagrange, A.-M., Vigan, A., Feldt, M., Gratton, R., Langlois, M., Cheetham, A., Bonnefoy, M., & Meyer, M. (2017). SHINE, The SpHere INfrared survey for Exoplanets . In C. Reylé, P. Di Matteo, F. Herpin, E. Lagadec, A. Lançon, Z. Meliani, & F. Royer (Eds.), SF2A-2017: Proceedings of the annual meeting of the french society of ast...

    work page 2017

Show all 27 references
  1. [9]

    C., Girard, J., Lacour, S., Schworer, G., Haubois, X., & Beuzit, J.-L

    Cheetham, A. C., Girard, J., Lacour, S., Schworer, G., Haubois, X., & Beuzit, J.-L. (2016). Sparse aperture masking with SPHERE . In F. Malbet, M. J. Creech-Eakman, & P. G. Tuthill (Eds.), Optical and infrared interferometry and imaging v (Vol. 9907, p. 99072T). https://doi.or...

    2016 doi

  2. [10]

    Chomez, A., Delorme, P., Lagrange, A.-M., Gratton, R., Flasseur, O., Chauvin, G., Langlois, M., Mazoyer, J., Zurlo, A., Desidera, S., Mesa, D., Bonnefoy, M., Feldt, M., Hagelberg, J., Meyer, M., Vigan, A., Ginski, C., Kenworthy, M., Albert, D., Wildi, F. (2025). The SPHERE inf...

    2025 doi

  3. [11]

    Christiaens, V., Gonzalez, C., Farkas, R., Dahlqvist, C.-H., Nasedkin, E., Milli, J., Absil, O., Ngo, H., Cantero, C., Rainot, A., Hammond, I., Bonse, M., Cantalloube, F., Vigan, A., Kompella, V., & Hancock, P. (2023). VIP: A Python package for high-contrast imaging . The Jour...

    2023 doi

  4. [12]

    G., Girard, J

    de Boer, J., Langlois, M., van Holstein, R. G., Girard, J. H., Mouillet, D., Vigan, A., Dohlen, K., Snik, F., Keller, C. U., Ginski, C., Stam, D. M., Milli, J., Wahhaj, Z., Kasper, M., Schmid, H. M., Rabou, P., Gluck, L., Hugot, E., Perret, D., Beuzit, J.-L. (2020). Polarimetr...

    2020 doi

  5. [13]

    Delorme, P., Meunier, N., Albert, D., Lagadec, E., Le Coroller, H., Galicher, R., Mouillet, D., Boccaletti, A., Mesa, D., Meunier, J.-C., Beuzit, J.-L., Lagrange, A.-M., Chauvin, G., Sapone, A., Langlois, M., Maire, A.-L., Montargès, M., Gratton, R., Vigan, A., & Surace, C. (2...

    2017 doi arXiv

  6. [14]

    M., Brasseur, C

    Ginsburg, A., Sipőcz, B. M., Brasseur, C. E., Cowperthwaite, P. S., Craig, M. W., Deil, C., Guillochon, J., Guzman, G., Liedtke, S., Lian Lim, P., Lockhart, K. E., Mommert, M., Morris, B. M., Norman, H., Parikh, M., Persson, M. V., Robitaille, T. P., Segovia, J.-C., Singer, L....

    2019 doi

  7. [15]

    A., Wertz, O., Absil, O., Christiaens, V., Defrère, D., Mawet, D., Milli, J., Absil, P.-A., Van Droogenbroeck, M., Cantalloube, F., Hinz, P

    Gomez Gonzalez, C. A., Wertz, O., Absil, O., Christiaens, V., Defrère, D., Mawet, D., Milli, J., Absil, P.-A., Van Droogenbroeck, M., Cantalloube, F., Hinz, P. M., Skemer, A. J., Karlsson, M., & Surdej, J. (2017). VIP: Vortex Image Processing Package for High-contrast Direct I...

    2017 doi

  8. [16]

    R., Millman, K

    Harris, C. R., Millman, K. J., Walt, S. J. van der, Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., Kerkwijk, M. H. van, Brett, M., Haldane, A., Río, J. F. del, Wiebe, M., Peterson, P., Oliphant, T. E. ...

    2020 doi

  9. [17]

    T., Kammerer, J., Leveratto, S., Rajani, C., Bristow, P., Le Louarn, M., Madec, P.-Y., Ströbele, S., Verinaud, C., Glauser, A., Quanz, S

    Kasper, M., Cerpa Urra, N., Pathak, P., Bonse, M., Nousiainen, J., Engler, B., Heritier, C. T., Kammerer, J., Leveratto, S., Rajani, C., Bristow, P., Le Louarn, M., Madec, P.-Y., Ströbele, S., Verinaud, C., Glauser, A., Quanz, S. P., Helin, T., Keller, C., Raynaud, H.-F. (2021...

    2021 doi

  10. [18]

    W., Brandner, W., Henning, T., & Janson, M

    Samland, M., Bouwman, J., Hogg, D. W., Brandner, W., Henning, T., & Janson, M. (2021). TRAP: a temporal systematics model for improved direct detection of exoplanets at small angular separations . Astronomy & Astrophysics, 646, A24. https://doi.org/10.1051/0004-6361/201937308

    2021 doi

  11. [19]

    D., Milli, J., Delorme, P., & Vigan, A

    Samland, M., Brandt, T. D., Milli, J., Delorme, P., & Vigan, A. (2022). Spectral cube extraction for the VLT/SPHERE IFS. Open-source pipeline with full forward modeling and improved sensitivity . Astronomy & Astrophysics, 668, A84. https://doi.org/10.1051/0004-6361/202244587

    2022 doi

  12. [20]

    Schmid, H. M., Bazzon, A., Roelfsema, R., Mouillet, D., Milli, J., Menard, F., Gisler, D., Hunziker, S., Pragt, J., Dominik, C., Boccaletti, A., Ginski, C., Abe, L., Antoniucci, S., Avenhaus, H., Baruffolo, A., Baudoz, P., Beuzit, J. L., Carbillet, M., Wildi, F. (2018). SPHERE...

    2018 doi

  13. [21]

    Stadler, E., Diolaiti, E., Schreiber, L., Cortecchia, F., Lombini, M., Loupias, M., Magnard, Y., De Rosa, A., Malaguti, G., Maurel, D., Morgante, G., Rabou, P., Rochat, S., Schiavone, F., Terenzi, L., Vidal, F., Cantalloube, F., Gendron, E., Gratton, R., Boccaletti, A. (2022)....

    2022

  14. [22]

    The pandas development team. (n.d.). pandas-dev/pandas: Pandas . https://doi.org/10.5281/zenodo.3509134

    doi

  15. [23]

    G., Girard, J

    van Holstein, R. G., Girard, J. H., de Boer, J., Snik, F., Milli, J., Stam, D. M., Ginski, C., Mouillet, D., Wahhaj, Z., Schmid, H. M., Keller, C. U., Langlois, M., Dohlen, K., Vigan, A., Pohl, A., Carbillet, M., Fantinel, D., Maurel, D., Origné, A., Beuzit, J.-L. (2020a). Pol...

    doi

  16. [24]

    G., Girard, J

    van Holstein, R. G., Girard, J. H., de Boer, J., Snik, F., Milli, J., Stam, D. M., Ginski, C., Mouillet, D., Wahhaj, Z., Schmid, H. M., Keller, C. U., Langlois, M., Dohlen, K., Vigan, A., Pohl, A., Carbillet, M., Fantinel, D., Maurel, D., Origné, A., Beuzit, J.-L. (2020b). IRD...

    2004

  17. [25]

    Vigan, Arthur. (2020). vlt-sphere: Automatic VLT/SPHERE data reduction and analysis (p. ascl:2009.002). https://doi.org/10.5281/zenodo.6563998

    2020 doi

  18. [26]

    Vigan, A., Langlois, M., Moutou, C., & Dohlen, K. (2008). Exoplanet characterization with long slit spectroscopy . Astronomy & Astrophysics, 489(3), 1345--1354. https://doi.org/10.1051/0004-6361:200810090

    2008 doi

  19. [27]

    J., Ruffio, J.-B., De Rosa, R

    Wang, J. J., Ruffio, J.-B., De Rosa, R. J., Aguilar, J., Wolff, S. G., & Pueyo, L. (2015). pyKLIP: PSF Subtraction for Exoplanets and Disks . Astrophysics Source Code Library, record ascl:1506.001. CSLReferences document

    2015

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