BIG-SPARC: The new SPARC database

Enrico Di Teodoro; Federico Lelli; Francis Duey; James Schombert; Kelley M. Hess; Konstantin Haubner; Stacy McGaugh; the Apertif Team

REVIEW 3 major objections 5 minor 51 references

BIG-SPARC: The new SPARC database

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read BIG-SPARC assembles nearly 4,000 galaxies with uniformly derived rotation curves, surface brightness profiles, and mass models, more than 20 times the size of SPARC.

desk verdict A promising, much-needed status report for a homogeneous ~4000-galaxy rotation-curve database, but the headline inventory lacks public validation and a clear de-duplication description. read the letter →

arxiv 2411.13329 v1 pith:QF5NACDG submitted 2024-11-20 astro-ph.GA

Konstantin Haubner , Federico Lelli , Enrico Di Teodoro , Francis Duey , Stacy McGaugh , James Schombert , Kelley M. Hess , the Apertif Team This is my paper · ORCID

classification astro-ph.GA

keywords HIrotationcurvesgalaxykinematicsdarkmatterradiointerferometrysurveysmassmodelsWISEphotometrytilted-ringmodeling

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

BIG-SPARC is a new database, assembled from 7,914 public H I data cubes, that will provide nearly 4,000 galaxies with homogeneously derived rotation curves, near-infrared surface brightness profiles, and mass models. The paper describes the construction: sources are found with an automated source finder, crossmatched against galaxy catalogues, visually inspected, and fitted with a uniform kinematic model. This is meant to cure the two main limitations of the earlier SPARC database: its small size (175 galaxies) and the heterogeneity of rotation curves compiled from the literature. If it works, the community gains a sample more than 20 times larger, enabling tests of dark matter models, galaxy evolution, and modified gravity theories with far greater statistical power, and preparing for the much larger H I surveys expected with the Square Kilometre Array and its pathfinders.

What carries the argument

The carrying mechanism is 3DBarolo, a software package for automated source finding and kinematic modeling of H I data cubes, used in two stages: SEARCH detects sources and produces moment maps, while 3DFIT fits a tilted-ring model to derive rotation velocity, surface density, velocity dispersion, and orientation parameters at each radius. Around this sits an identification chain—crossmatching against PGC and then a general extragalactic database, followed by visual inspection—that turns raw detections into a curated list of unique galaxies; on the photometric side, Spitzer and WISE W1 imaging supply the near-infrared light needed for mass models.

What would settle it

Examine the galaxies that appear in both SPARC and BIG-SPARC and compare the rotation curves at matched radii: if well-resolved objects show systematic offsets larger than the quoted uncertainties, or a blind rerun of the source finder on empty fields shows many false detections, the homogeneous pipeline is not delivering the promised accuracy and sample size.

Watch

Extended reading notes

Core claim

The paper's claim is that a single homogeneous pipeline can be applied to the archival record of H I observations to produce a database of 3,882 unique galaxies, about 4,000 in round numbers. Starting from 7,914 data cubes drawn from many surveys and individual studies, the authors run the SEARCH source finder of the 3DBarolo software, crossmatch detections with the PGC catalogue and, when that fails, a general extragalactic database, and visually inspect every detection to remove artifacts. Kinematic fits with the 3DFIT task yield rotation curves, H I surface density profiles, velocity dispersions, and geometric parameters, while near-infrared photometry from Spitzer, supplemented by all-sky WISE W1 data, provides the baryonic mass side. The result is an order-of-magnitude jump in sample size over SPARC, extending to roughly twice the maximum distance, with the promise of uniform data products that make the small intrinsic scatter of scaling relations measurable.

Load-bearing premise

The load-bearing premise is that the 3,882-galaxy list is essentially complete and correct—that the automated source finder, catalogue crossmatching, and visual inspection admit few duplicates or artifacts, and that archived cubes, many shallow or poorly resolved, still yield trustworthy rotation curves.

Editorial extensions

If this is right

The galaxy sample grows from 175 to roughly 3,900, a more than 20-fold increase in the number of rotation curves.
All rotation curves, surface brightness profiles, and mass models come from one uniform fitting procedure, removing the largest source of heterogeneity in the predecessor database.
The distance baseline roughly doubles: the farthest galaxy sits at about twice the distance of the farthest in SPARC, with direct distances from Cosmicflows-4 where available and Hubble-flow distances elsewhere.
The database can support scaling-relation studies at fixed mass, environment, or gas fraction, where SPARC's 175 galaxies lacked statistical power.
The pipeline and data products establish a template for handling the next order-of-magnitude increase in H I sources expected from SKA-era surveys.

Reading between the lines

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

Inference: if the sample is as clean as claimed, the radial acceleration relation and baryonic Tully-Fisher relation can be tested at fixed stellar mass with hundreds of galaxies per bin, which would sharpen the question of whether their scatter is truly intrinsic.
Inference: because the input cubes range from well-resolved to barely resolved, the database will contain a long tail of low-quality rotation curves; future users will need per-galaxy reliability flags, which this paper does not yet specify.
Inference: the sky coverage is telescope-driven and northern-biased, so BIG-SPARC is not a volume-limited or flux-limited sample; scaling-relation work will need to model these selection effects rather than treat the catalogue as representative.
Inference: WISE W1 photometry is all-sky but not identical to Spitzer mid-infrared photometry, so combining the two without a matched zeropoint calibration would propagate a systematic into stellar mass estimates.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Konstantin — quick read of the BIG-SPARC status report. The pitch is right: SPARC's limits are its 175 galaxies and heterogeneous literature curves, and building a successor from 7,914 public HI cubes with a uniform 3DBarolo pipeline is the logical next step. The two example fits in Fig. 3 suggest the tools can handle both well-resolved and marginal data. That part I buy.

What gives me pause is the inventory. Table 1 says 3,882 unique galaxies, but the column sums exactly to that. Since galaxies like NGC 3198 appear in multiple surveys, the per-survey 'unique' counts can only sum to the total if every overlap was explicitly assigned to one survey. The text doesn't say that. If cross-survey duplicates were left in, the real count is lower, possibly still in the thousands, but the 'factor of more than 20' and 'almost 4000' claims in the abstract would be overstated. The largest single contribution — 1,740 galaxies from APERTIF — rests on Hess et al. (in prep.), so the 3.4 cubes-per-galaxy ratio isn't checkable against a public source list. There are also no completeness, contamination, or resolution/S/N thresholds defining what counts as a usable rotation curve, and no catalog is released yet. For a status report that's defensible, but for a paper whose central number is the inventory, it's a gap.

I'd send this to a referee, but a sharp one who will ask for the de-duplication pipeline, a public source list or a testable subset, and a statement of the resolution limits that the final RC products will meet. The project is clearly worth building, and the authors seem aware they're early in the process. As written, the paper is a useful programmatic update, but not yet something to build scaling relations on — which is fine, as long as the 'about 4000' doesn't become a citation datum before the catalog is out.

Referee Report

3 major / 5 minor

Summary. This manuscript presents BIG-SPARC, a forthcoming database intended to provide homogeneously derived HI rotation curves, surface brightness profiles, and mass models for about 4000 galaxies, roughly 20 times more than the original SPARC. The paper describes the data collection from 7914 public HI data cubes (Table 1), source finding with SEARCH, crossmatching to PGC and NED, visual inspection, and the resulting claimed list of 3882 unique galaxies. It shows the all-sky distribution and systemic-velocity histogram, and illustrates the planned 3DFIT kinematic analysis with two example galaxies. The paper is explicitly a status report rather than a release of the catalog or derived products.

Significance. If the stated sample size and homogeneity are delivered, BIG-SPARC would be a major community resource, enabling statistically powerful tests of scaling relations, dark matter models, and galaxy evolution that are not possible with the 175-galaxy SPARC sample. The use of public archives and a single analysis pipeline (3DBarolo) is a clear strength, as is the attention to diverse data qualities exemplified by the two test galaxies. However, the paper's central quantitative claims—the number of unique galaxies and the factor-of-twenty improvement over SPARC—rest on internal inventory numbers that are not yet validated by completeness, contamination, or duplicate tests. At this stage the contribution is a plausible and useful progress report, but the quantitative claims need substantiation before the database can be relied upon as a sample definition.

major comments (3)

[Table 1 and Sec. 2] The per-survey unique-galaxy counts in Table 1 sum exactly to the stated total of 3882. This arithmetic consistency can only hold if every galaxy appearing in more than one survey was explicitly assigned to a single survey, but Sec. 2 describes no such global de-duplication step. Several listed surveys target heavily overlapping populations (e.g., WHISP and later WSRT/APERTIF programs; THINGS, HALOGAS, and PHANGS-VLA all observe nearby VLA-visible disks), so cross-survey duplicates are expected. Please specify the global de-duplication procedure, report the number of duplicates found, or revise the claimed total and the factor-of-20 improvement accordingly.
[Sec. 2] The central inventory is based entirely on SEARCH source finding, PGC/NED crossmatching, and visual inspection, but no completeness or contamination tests are presented. The false-detection rate after visual inspection, the completeness as a function of HI mass and distance, and the cross-match success rate all directly affect the promised sample size and homogeneity. Please provide basic validation statistics (e.g., number of SEARCH detections, number rejected as artifacts, cross-match failure rate, duplicate rate), or state explicitly which numbers remain preliminary pending the full data release.
[Sec. 4 and Fig. 3] No resolution or signal-to-noise criterion is given for what constitutes a usable rotation curve. The two examples in Fig. 3 bracket the data-quality range, but the claim of providing homogeneously derived rotation curves requires a defined quality threshold (e.g., minimum number of independent beams across the galaxy, peak signal-to-noise ratio, or significance of the velocity gradient). Without such a criterion, the promised homogeneous sample is not yet well-defined, especially given the very heterogeneous nature of the input data cubes.

minor comments (5)

[Abstract and Sec. 1] The term 'HI' appears in several places (e.g., 'HI datacubes' in the abstract) while elsewhere the correct 'H I' is used; please standardize the notation.
[Sec. 2] The text says the procedure gave 'a list of almost 4000 galaxies' while Table 1 gives exactly 3882; please use a precise number or state that the count is current as of a specific date to avoid ambiguity.
[Fig. 2] The upper axis shows Hubble distances computed with H0 = 75 km/s/Mpc, but Sec. 3 states the final database will use Cosmicflows-4 distances where available; please clarify whether the histogram is illustrative only and whether the velocity–distance conversion will be updated in the final version.
[Sec. 4] The text refers to 'Duey et al. 2024, in prep.' for the WISE photometry, but the reference list cites 'Duey, F., Schombert, J., McGaugh, S., & Lelli, F. 2024, AJ, 168, 19'; please confirm which status is correct and update the citation.
[Fig. 3] The caption is very dense; labeling the columns and rows with explicit panel identifiers or adding a short legend for the sub-panels would greatly improve readability.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the database inventory is assembled from external archives and source-finder outputs, not derived from the paper's own inputs.

full rationale

This paper is a status report on assembling an observational database, not a derivation. The central claim—a list of almost 4000 galaxies from 7914 HI data cubes—is produced by running the SEARCH source finder, crossmatching to PGC and NED, and visually inspecting detections (Sec. 2); it is not obtained from a model, fit, or theory. No equation relates an output to an input by construction. The only numeric constant, H0 = 75 km/s/Mpc (Tully et al. 2016), is used solely to display Hubble distances in Fig. 2 and is explicitly stated to be replaced by Cosmicflows-4 distances in the final database. The planned 3DFIT kinematic fits and WISE photometry are future deliverables, not predictions validated against the same inputs. Self-citations to SPARC and related papers describe prior context and software usage; they do not carry the current inventory claim. The reader's concern about duplicate counting (the unique counts in Table 1 sum exactly to 3882) is a potential data-quality issue, not circular reasoning, because cross-survey overlap would change the sample count without making any claimed result true by definition. Therefore no circular step is identifiable.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No numbers are fitted to data in this status report; H0 is adopted from prior literature and no new physical entities are introduced. The central claim rests on standard domain assumptions about HI kinematics, NIR photometry, and the reliability of public data cubes and source finding.

assumptions (5)

domain assumption Observed HI rotation velocity is a close proxy of the circular velocity because HI disks have low velocity dispersion (around 10 km/s).
Used in Sec. 1 to justify deriving mass models from HI rotation curves.
domain assumption Near-infrared photometry traces the stellar mass distribution better than optical light.
Used in Sec. 1 and Sec. 4 to justify using WISE W1 photometry for mass models.
domain assumption The public HI data cubes have sufficient astrometric and flux calibration for homogeneous kinematic fitting across many telescopes.
The entire homogenization effort assumes this; Sec. 2 does not report calibration checks.
domain assumption The SEARCH source finder plus PGC and NED crossmatching plus visual inspection yields a reliable galaxy list.
Sec. 2 states the procedure and result but gives no completeness or false-detection statistics.
domain assumption Hubble distances computed with H0 = 75 km/s/Mpc are adequate for galaxies lacking direct distances.
Used in Fig. 2 and planned for the final database when Cosmicflows-4 distances are unavailable (Sec. 3).

how reviews work

0 comments

Cite this review

Pith. "Pith review of BIG-SPARC: The new SPARC database." pith.science (2026). https://pith.science/paper/QF5NACDG

@misc{pith2026241113329,
  author       = {Pith},
  title        = {Pith review of: BIG-SPARC: The new SPARC database},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QF5NACDG}},
  note         = {Machine review of arXiv:2411.13329}
}

read the original abstract

The Surface Photometry and Accurate Rotation Curves (SPARC) database has provided the community with mass models for 175 nearby galaxies, allowing different research teams to test different dark matter models, galaxy evolution models, and modified gravity theories. Extensive tests, however, are hampered by the somewhat heterogeneous nature of the HI rotation curves and the limited sample size of SPARC. To overcome these limitations, we are working on BIG-SPARC, a new database that consists of about 4000 galaxies with HI datacubes from public telescope archives (APERTIF, ASKAP, ATCA, GMRT, MeerKAT, VLA, and WSRT) and near infrared photometry from WISE. For these galaxies, we will provide homogeneously derived HI rotation curves, surface brightness profiles, and mass models. BIG-SPARC is expected to increase the size of its predecessor by a factor of more than 20. This is a necessary step to prepare for the additional order of magnitude increase in sample size expected from ongoing and future HI surveys with the Square Kilometre Array (SKA) and its pathfinders

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

51 extracted references · 41 canonical work pages

[1]

Adams, E. A. K., Adebahr, B., de Blok, W. J. G., et al. 2022, A&A, 667, A38

2022
[2]

N., Karachentsev, I

Begum, A., Chengalur, J. N., Karachentsev, I. D., Sharina, M. E., & Kaisin, S. S. 2008,MNRAS, 386, 1667

work page 2008
[3]

S., & Schombert, J

Chae, K.-H., Desmond, H., Lelli, F., McGaugh, S. S., & Schombert, J. M. 2021, ApJ, 921, 104

work page 2021
[4]

2020, ApJ, 904, 51

Chae, K.-H., Lelli, F., Desmond, H., et al. 2020, ApJ, 904, 51

work page 2020
[5]

S., & Schombert, J

Chae, K.-H., Lelli, F., Desmond, H., McGaugh, S. S., & Schombert, J. M. 2022,Phys. Rev. D, 106, 103025

work page 2022
[6]

M., Chastenet, J., et al

Chiang, I.-D., Sandstrom, K. M., Chastenet, J., et al. 2024, ApJ, 964, 18

work page 2024
[7]

H., Kenney, J

Chung, A., van Gorkom, J. H., Kenney, J. D. P., Crowl, H., & V ollmer, B. 2009,AJ, 138, 1741 de Blok, W. J. G., McGaugh, S. S., & van der Hulst, J. M. 1996, MNRAS, 283, 18

work page 2009
[8]

2022,PASA, 39, e059

Deg, N., Spekkens, K., Westmeier, T., et al. 2022,PASA, 39, e059

work page 2022

Show all 51 references

[9]

2019, MNRAS, 484, 239 6 Haubner et al

Desmond, H., Katz, H., Lelli, F., & McGaugh, S. 2019, MNRAS, 484, 239 6 Haubner et al. Di Teodoro, E. M. & Fraternali, F. 2015,MNRAS, 451, 3021

2019
[10]

2024,AJ, 168, 19

Duey, F., Schombert, J., McGaugh, S., & Lelli, F. 2024,AJ, 168, 19

2024
[11]

2011, A&A, 526, A118

Heald, G., Józsa, G., Serra, P., et al. 2011, A&A, 526, A118

2011
[12]

A., Ficut-Vicas, D., Ashley, T., et al

Hunter, D. A., Ficut-Vicas, D., Ashley, T., et al. 2012, AJ, 144, 134

2012
[13]

G., Verdes-Montenegro, L., Moldon, J., et al

Jones, M. G., Verdes-Montenegro, L., Moldon, J., et al. 2023, A&A, 670, A21

2023
[14]

2018, MNRAS, 480, 4287

Katz, H., Desmond, H., Lelli, F., et al. 2018, MNRAS, 480, 4287

2018
[15]

2019, MNRAS, 483, L98

Katz, H., Desmond, H., McGaugh, S., & Lelli, F. 2019, MNRAS, 483, L98

2019
[16]

S., & others

Katz, H., Lelli, F., McGaugh, S. S., & others. 2017, MNRAS, 466, 1648

2017
[17]

S., Staveley-Smith, L., Westmeier, T., et al

Koribalski, B. S., Staveley-Smith, L., Westmeier, T., et al. 2020,Ap&SS, 365, 118

2020
[18]

S., Wang, J., Kamphuis, P., et al

Koribalski, B. S., Wang, J., Kamphuis, P., et al. 2018,MNRAS, 478, 1611

2018
[19]

A., et al

Kreckel, K., Platen, E., Aragón-Calvo, M. A., et al. 2012, AJ, 144, 16

2012
[20]

S., Schombert, J

Lelli, F., McGaugh, S. S., Schombert, J. M., Desmond, H., & Katz, H. 2019, MNRAS, 484, 3267

2019
[21]

2014,A&A, 566, A71

Lelli, F., Verheijen, M., & Fraternali, F. 2014,A&A, 566, A71

2014
[22]

2018,A&A, 615, A3

Li, P., Lelli, F., McGaugh, S., & Schombert, J. 2018,A&A, 615, A3

2018
[23]

2020,ApJS, 247, 31

Li, P., Lelli, F., McGaugh, S., & Schombert, J. 2020,ApJS, 247, 31

2020
[24]

2021,A&A, 646, L13

Li, P., Lelli, F., McGaugh, S., Schombert, J., & Chae, K.-H. 2021,A&A, 646, L13

2021
[25]

A., Kilborn, V

Lutz, K. A., Kilborn, V . A., Koribalski, B. S., et al. 2018,MNRAS, 476, 3744

2018
[26]

S., Lelli, F., & Schombert, J

McGaugh, S. S., Lelli, F., & Schombert, J. M. 2016, Phys. Rev. Lett., 117, 201101

2016
[27]

S., Lelli, F., Schombert, J

McGaugh, S. S., Lelli, F., Schombert, J. M., et al. 2021, AJ, 162, 202

2021
[28]

T., Oosterloo, T

Morganti, R., de Zeeuw, P. T., Oosterloo, T. A., et al. 2006,MNRAS, 371, 157

2006
[29]

2003, A&A, 412, 45

Paturel, G., Petit, C., Prugniel, P., et al. 2003, A&A, 412, 45

2003
[30]

& Lelli, F

Petersen, J. & Lelli, F. 2020, A&A, 636, A56

2020
[31]

E., Impey, C

Pickering, T. E., Impey, C. D., van Gorkom, J. H., & Bothun, G. D. 1997, AJ, 114, 1858

1997
[32]

Ramatsoku, M., Verheijen, M. A. W., Kraan-Korteweg, R. C., et al. 2016,MNRAS, 460, 923

2016
[33]

E., van Zee, L., Barnes, K

Richards, E. E., van Zee, L., Barnes, K. L., et al. 2016, MNRAS, 460, 689

2016
[34]

E., van Zee, L., Barnes, K

Richards, E. E., van Zee, L., Barnes, K. L., et al. 2018, MNRAS, 476, 5127

2018
[35]

C., Ford, W

Rubin, V . C., Ford, W. K., J., & Thonnard, N. 1978,ApJL, 225, L107

1978
[36]

2019, MNRAS, 483, 1496

Schombert, J., McGaugh, S., & Lelli, F. 2019, MNRAS, 483, 1496

2019
[37]

2020, AJ, 160, 71

Schombert, J., McGaugh, S., & Lelli, F. 2020, AJ, 160, 71

2020
[38]

2022, AJ, 163, 154

Schombert, J., McGaugh, S., & Lelli, F. 2022, AJ, 163, 154

2022
[39]

M., Kleiner, D., et al

Serra, P., Maccagni, F. M., Kleiner, D., et al. 2023,A&A, 673, A146

2023
[40]

2012,MNRAS, 422, 1835

Serra, P., Oosterloo, T., Morganti, R., et al. 2012,MNRAS, 422, 1835

2012
[41]

2018, MNRAS, 480, 2292

Starkman, N., Lelli, F., McGaugh, S., & Schombert, J. 2018, MNRAS, 480, 2292

2018
[42]

K., Ostriker, E

Sun, J., Leroy, A. K., Ostriker, E. C., et al. 2020, ApJ, 892, 148

2020
[43]

A., van Albada, T

Swaters, R. A., van Albada, T. S., van der Hulst, J. M., & Sancisi, R. 2002, A&A, 390, 829

2002
[44]

Trachternach, C., de Blok, W. J. G., McGaugh, S. S., et al. 2009, A&A, 505, 577

2009
[45]

B., Courtois, H

Tully, R. B., Courtois, H. M., & Sorce, J. G. 2016, AJ, 152, 50

2016
[46]

B., Kourkchi, E., Courtois, H

Tully, R. B., Kourkchi, E., Courtois, H. M., et al. 2023, ApJ, 944, 94 van Albada, T. S., Bahcall, J. N., Begeman, K., & Sancisi, R. 1985, ApJ, 295, 305 van der Hulst, J. M., Skillman, E. D., Smith, T. R., et al. 1993, AJ, 106, 548

2023
[47]

Verheijen, M. A. W. 2001,ApJ, 563, 694

2001
[48]

Verheijen, M. A. W. & Sancisi, R. 2001, A&A, 370, 765

2001
[49]

Walter, F., Brinks, E., de Blok, W. J. G., et al. 2008,AJ, 136, 2563

2008
[50]

Wang, J., Kauffmann, G., Józsa, G. I. G., et al. 2013, MNRAS, 433, 270

2013
[51]

2022,PASA, 39, e058

Westmeier, T., Deg, N., Spekkens, K., et al. 2022,PASA, 39, e058

2022

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Adams, E. A. K., Adebahr, B., de Blok, W. J. G., et al. 2022, A&A, 667, A38

2022

[2] [2]

N., Karachentsev, I

Begum, A., Chengalur, J. N., Karachentsev, I. D., Sharina, M. E., & Kaisin, S. S. 2008,MNRAS, 386, 1667

work page 2008

[3] [3]

S., & Schombert, J

Chae, K.-H., Desmond, H., Lelli, F., McGaugh, S. S., & Schombert, J. M. 2021, ApJ, 921, 104

work page 2021

[4] [4]

2020, ApJ, 904, 51

Chae, K.-H., Lelli, F., Desmond, H., et al. 2020, ApJ, 904, 51

work page 2020

[5] [5]

S., & Schombert, J

Chae, K.-H., Lelli, F., Desmond, H., McGaugh, S. S., & Schombert, J. M. 2022,Phys. Rev. D, 106, 103025

work page 2022

[6] [6]

M., Chastenet, J., et al

Chiang, I.-D., Sandstrom, K. M., Chastenet, J., et al. 2024, ApJ, 964, 18

work page 2024

[7] [7]

H., Kenney, J

Chung, A., van Gorkom, J. H., Kenney, J. D. P., Crowl, H., & V ollmer, B. 2009,AJ, 138, 1741 de Blok, W. J. G., McGaugh, S. S., & van der Hulst, J. M. 1996, MNRAS, 283, 18

work page 2009

[8] [8]

2022,PASA, 39, e059

Deg, N., Spekkens, K., Westmeier, T., et al. 2022,PASA, 39, e059

work page 2022