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Searching for Dark Galaxies with HI detection from the Arecibo Legacy Fast ALFA (ALFALFA) survey

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read The paper assembles 142 candidate dark galaxies—neutral-hydrogen systems with no detected stars—and finds they are low-mass, gas-dominated, and underdense, matching simulation predictions.

desk verdict A solid, carefully vetted catalog of 142 dark galaxy candidates, but the low-density environmental claim is not yet separated from the selection rules that define the sample. read the letter →

arxiv 2506.03678 v1 pith:4VCER6CB submitted 2025-06-04 astro-ph.GA

Minseong Kwon , Ho Seong Hwang , Brian R. Kent , Ilsang Yoon , Gain Lee , Hyein Yoon This is my paper
classification astro-ph.GA
keywords darkgalaxiesneutralhydrogenALFALFAsurvey21cmastronomydwarfmatterhalosgalaxyformationlocalenvironment
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 tries to establish that a substantial population of dark galaxies exists in the local universe and can be found in a blind 21-centimeter survey of neutral hydrogen. Working from 344 H I sources with no optical counterpart in the ALFALFA survey, the authors apply a series of vetoes—against known tidal H I clouds, sources with a neighbouring H I detection, and sources with catalogued or visible stellar counterparts—leaving 142 systems they call dark galaxy candidates. They then argue that these systems are physically distinct from normal galaxies: they have smaller dynamical masses, higher H I-to-dynamical-mass ratios, and lower local densities, even after matching masses and redshifts, and the same trends appear in cosmological simulations. If the claim holds, this is the largest homogeneous catalog of plausible dark galaxies and direct observational support for the idea that some dark matter halos never form stars, preferentially in underdense regions.

What carries the argument

The central machinery is a staged contamination-removal pipeline. Starting from 344 neutral-hydrogen sources without optical counterparts in the ALFALFA catalog, the pipeline removes 83 known tidal H I clouds, 13 sources with optical counterparts in the literature, 56 sources whose nearest H I neighbour lies at projected distance $d_{\rm min} < 0.075$ Mpc, 10 sources with another detection inside the survey beam, and 40 sources with a catalogued possible counterpart or tidal association. Surviving sources are then visually inspected in deep optical images (reaching an $r$-band surface brightness of roughly 28.5 mag arcsec$^{-2}$) plus ultraviolet and infrared images to confirm the absence of stars and dust. The physical-property analysis uses three quantitative tools: a likelihood ratio for the chance of a hidden optical counterpart, a 20th-nearest-neighbour density estimate with smoothed-particle-hydrodynamic kernel weighting on a spectroscopic galaxy sample, and mass- and redshift-matched control samples. Dynamical masses come from the H I line width measured at 20 percent of peak ($W_{20}$) and an H I radius derived from the H I size–mass relation.

What would settle it

Rerun the selection with the proximity threshold doubled and halved and check whether the median local density of the surviving samples moves; if it does, the underdensity is a creation of the cut. Independently, map a subset of the 142 candidates with an H I interferometer: true dark galaxies should show a rotating gas disk with no stellar counterpart, whereas tidal debris should appear as clumpy gas around a luminous neighbor.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is a catalog and a pattern. The authors claim that 142 of the 344 optically empty H I sources in the ALFALFA footprint survive every check for tidal debris, beam confusion, and hidden stellar emission, and that this surviving set is best interpreted as galaxy-scale dark matter halos containing gas but no stars. Compared with luminous galaxies, these candidates are systematically less massive, richer in H I relative to their dynamical mass, and more likely to sit in low-density environments; the paper presents the agreement of these trends with cosmological simulations as independent support for the dark-galaxy interpretation. The authors present the 142 systems as candidates rather than confirmed dark galaxies, and explicitly call for deeper optical and resolved-H I follow-up to test the interpretation.

Load-bearing premise

The load-bearing premise is that the selection cuts—especially excluding any source with a neighbouring H I detection within $d_{\rm min}=0.075$ Mpc and excluding sources with catalogued optical or tidal counterparts—do not themselves force the surviving sample into low-density regions; the paper states this selection effect is negligible but does not quantify it.

Editorial extensions

If this is right

  • The catalog makes dark galaxies a statistical population rather than a handful of curiosities, opening the way to measuring their H I mass function and space density.
  • The underdense-environment trend supports the picture in which cosmic reionization suppresses star formation in low-mass halos living in low-density regions.
  • The elevated H I-to-dynamical-mass ratios imply that gas in these halos is inefficient at turning into stars, giving a direct constraint on star-formation efficiency at the lowest masses.
  • The 142 candidates are a ready-made target list for deep optical, ultraviolet, infrared, and resolved-H I follow-up observations.

Reading between the lines

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

  • If the underdense environment result survives selection-correction tests, future deeper blind H I surveys should find even fainter starless halos increasingly concentrated in voids; ALFALFA's sensitivity limit probably makes the present catalog incomplete at the low-mass end.
  • The paper's hint of bimodality in the local-density distribution suggests two possible formation channels—pristine halos that never formed stars and galaxies stripped of their stellar component by ram pressure—that could be separated by follow-up of the candidates.
  • A testable extension is to split the catalog by H I line asymmetry: isolated dark galaxies should show symmetric rotating profiles, while the overdense tail should show disturbed profiles if ram-pressure stripping or tides shaped them.
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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 / 4 minor

Summary. The paper constructs a catalog of 142 dark galaxy candidates by starting from 344 ALFALFA HI sources without optical counterparts and applying successive cuts to remove known HI clouds, sources near other HI detections, sources within the ALFALFA beam, and sources with potential NED/DESI optical counterparts or associated galaxies. The authors then compare the candidates' HI masses, dynamical masses, HI-to-dynamical mass ratios, and local densities with luminous galaxies in ALFALFA and SDSS, and with dark galaxies in the TNG50 simulation. They report that the candidates are generally less massive, have higher HI-to-dynamical mass ratios, and reside in less dense environments than luminous galaxies, interpreting these trends as consistent with cosmological simulations of dark galaxy formation.

Significance. If the catalog and the environmental result hold, this is a valuable and homogeneous sample of optically dark HI systems that may be dark matter-dominated halos, providing one of the largest such samples from an untargeted survey. The catalog construction is transparent and carefully documented, including visual inspection of DESI Legacy, GALEX, WISE, and AKARI images, a likelihood-ratio analysis for optical counterparts, and a stacking analysis that shows no significant optical emission. The paper also provides a machine-readable catalog with quality codes, making it a useful resource. The main claims, however, rest on a selection-bias assertion in Section 4.2 that is not quantitatively demonstrated, and on dynamical masses derived from an HI size-mass relation whose validity for these systems is uncertain.

major comments (3)
  1. [Section 4.2] The claim that 'the effect of the HI selection criterion (i.e. the minimum projected distance to the nearest HI source) on the local density distribution is negligible' is not supported by any quantitative test in the paper. The candidate sample is defined in Section 2.2.1 by removing sources with d_min < 0.075 Mpc and in Section 2.2.2 by removing sources with nearby optical counterparts or associated galaxies within 400 km/s and 0.075 Mpc, while the comparison samples (ALFALFA luminous galaxies and SDSS galaxies) are not subject to these isolation cuts. Since local density in Eq. (2) is computed using SDSS galaxies with M_r < -19.2, and HI-detected luminous galaxies are often found in groups, an isolation cut can preferentially remove candidates in higher-density regions before the density is measured. A d_min-matched control sample, or a demonstration that the density distribution of excluded sources matches that of retained sources, is needed to separate the physical environmental signal from the selection rule. This is load-bearing because the abstract and Section 5 present the low-density environment as a physical property consistent with simulations.
  2. [Section 4.1, column (11)] Dynamical masses are derived from the HI radius obtained via the Wang et al. (2016) HI size-mass relation, combined with the W20 line width. This relation is calibrated on rotationally supported HI disks in luminous galaxies, and the paper itself notes in Table 1 that the propagated uncertainties in log M_dyn reach ~1.0 dex for some candidates (e.g., AGC 208430). For systems that may not be rotationally supported, or that may be dark galaxies with different kinematics, the assumed size-mass relation could introduce a systematic bias in M_dyn and therefore in the HI-to-dynamical mass ratio, which is one of the central comparisons in Section 4.1. The authors should test the sensitivity of their mass-dependent results (including the mass-matched comparison in Figure 7) to alternative size prescriptions, or at least quantify how much of the reported ratio difference could be driven by this assumption.
  3. [Section 2.4 / Figure 6] The comparison with TNG50 dark galaxies is qualitative and, as stated in Section 2.4, the selection criteria differ between simulations and observations: TNG dark galaxy candidates are defined by HI mass above the 5-sigma limit and r-band magnitude fainter than 17.77, but they are not subjected to the isolation cuts (d_min, NED/DESI association) that define the observed sample. Because these cuts are the same ones that may bias the observed environmental distribution, the TNG comparison in the bottom-right panel of Figure 6 cannot independently validate the observed low-density trend. The paper should either apply analogous isolation cuts to the simulated sample or explicitly state that the simulation comparison does not control for the selection effect discussed in Section 4.2.
minor comments (4)
  1. [Section 2.2.3 / Figure 2] The caption of Figure 2 states that the complete figure set contains 344 images, but the final sample has 142 candidates; it would be clearer to label which sources are shown in the online figure set (all 344 initial sources, presumably, but this is not stated).
  2. [Section 2.3.2 / Eq. (2)] The local density is computed with n = 20 neighbors, but no estimate of the uncertainty or significance of the median density differences in Figure 7 is provided; reporting bootstrap or jackknife uncertainties on the medians would strengthen the claim that the 40% difference is not noise.
  3. [Section 2.3.1 / Eq. (1)] The likelihood ratio threshold of 0.016 is introduced as separating luminous galaxies from candidates, but the paper does not explain how this threshold is derived or why candidates with LR > 0.016 are retained; a sentence justifying the threshold would help the reader assess the contamination risk.
  4. [Section 2.2.2] The criterion '5σ of the ALFALFA positional uncertainty' is not defined in the text; the positional uncertainty in the ALFALFA catalog should be stated explicitly so the reader can interpret the angular separation cuts.

Circularity Check

1 steps flagged · score 4.0 of 10

Low-density environment claim is partly imposed by the d_min HI-isolation cut; mass and ratio results remain independent.

  1. other [Section 2.2.1 (d_min cut) and Section 4.2 (environment claim)]
    "We therefore exclude 56 sources that have d_min <0.075 Mpc. ... We find the effect of the HI selection criterion (i.e. the minimum projected distance to the nearest HI source) on the local density distribution to be negligible."

    The final dark-galaxy sample is constructed to contain no other ALFALFA HI source within 0.075 Mpc (and no NED/DESI-associated galaxy within a similar separation), while the comparison luminous-galaxy samples are not subject to this isolation cut. Local density rho_20 (Eq. 2) is measured using SDSS galaxies, the same population that provides optical counterparts for ALFALFA HI sources; a close HI neighbor removed by the d_min cut would, if present, have contributed to rho_20. The paper's only response is the quoted assertion that the effect is negligible, with no quantitative test or d_min-matched control sample. The main environmental conclusion, that candidates live in less dense regions, is therefore not independent of the sample definition and may be partly forced by construction.

full rationale

No equation-level circularity is present in the dynamical-mass or HI-to-dynamical-mass-ratio comparisons, which use independently measured W20 line widths, HI fluxes, and the external HI size-mass relation; the mass-matched control sample is a legitimate attempt to remove bias. The TNG50 comparison is not circular: although Lee et al. (2024) is a same-group citation, this paper independently constructs an ALFALFA-like TNG50 sample, so the simulation benchmark provides external evidence. The one load-bearing circularity-adjacent issue is the environment claim: the d_min > 0.075 Mpc cut, together with the beam and NED/DESI association cuts, removes sources near other HI sources before rho_20 is measured, while the luminous comparison samples are not subject to this cut. Section 4.2 asserts that this selection effect is negligible but provides no control experiment or quantitative test, leaving the low-density result potentially entangled with the sample definition. This is a partial circularity affecting one of the three headline results, warranting a score of 4 rather than a higher score because the other central results and the simulation comparison retain independent content.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new physical particles, forces, or dimensions. The central claim rests on domain assumptions about the nature of HI sources without optical counterparts, the applicability of the HI size-mass relation to these systems, and the reliability of TNG50 subgrid models. Several thresholds (d_min, velocity window, n=20, mass-to-light ratios) are free choices that affect the final sample and the environmental conclusions.

free parameters (6)
  • d_min threshold = 0.075 Mpc
    Projected distance cut to separate tidal HI clouds from dark galaxy candidates; chosen from the distribution of known HI clouds in Section 2.2.1.
  • radial velocity difference window = 400 km/s
    Used in NED/DESI matching and clustering criteria to identify associated galaxies; based on the literature for interacting HI clouds.
  • number of neighbors for local density = 20
    n=20 sets the smoothing scale of the local density estimator to a few Mpc (Section 2.3.2).
  • mass-to-light ratio factor = gamma(early) = 2 gamma(late)
    Assumed relation for computing mass density from r-band luminosity, adopted from Park et al. 2008.
  • friends-of-friends linking lengths = 0.2 x d_mean perpendicular, d_mean along line of sight
    Parameters for structure identification in the environmental analysis (Section 2.3.2).
  • HI size-mass relation for radii = Wang et al. 2016 relation
    Used to convert HI mass to HI radius for dynamical mass estimates; an external relation that may not apply to non-rotating systems.
assumptions (5)
  • domain assumption The 344 HI sources without optical counterparts in ALFALFA are either dark galaxy candidates or HI clouds, and the exclusion criteria can separate the two classes.
    Central to the sample definition; the paper acknowledges the ongoing debate about the nature of such HI sources in Section 1.
  • domain assumption The HI size-mass relation (Wang et al. 2016) is valid for the dark galaxy candidates and yields reliable HI radii for dynamical mass estimates.
    Used in Section 4.1 and Table 1; not validated for optically dark, possibly non-rotating systems.
  • domain assumption TNG50 and its post-processing HI model (Diemer et al. 2018, 2019; Sternberg et al. 2014) adequately represent the HI content of low-mass halos for a qualitative comparison.
    Section 2.4; the paper itself notes resolution and subgrid uncertainties in the simulation comparison.
  • domain assumption The SDSS spectroscopic sample, supplemented with literature redshifts, is sufficiently complete for local density estimation around the candidates.
    Section 2.3.2; the authors note poor completeness for bright galaxies and high-density regions.
  • standard math Standard flat LCDM cosmology with Omega_m=0.3, Omega_Lambda=0.7, H0=70 km/s/Mpc.
    Adopted at the end of Section 1 for distance and mass computations.

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Cite this review

Pith. "Pith review of Searching for Dark Galaxies with HI detection from the Arecibo Legacy Fast ALFA (ALFALFA) survey." pith.science (2026). https://pith.science/paper/4VCER6CB

@misc{pith2026250603678,
  author       = {Pith},
  title        = {Pith review of: Searching for Dark Galaxies with HI detection from the Arecibo Legacy Fast ALFA (ALFALFA) survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4VCER6CB}},
  note         = {Machine review of arXiv:2506.03678}
}
abstract

We present a catalog of 142 dark galaxy candidates in a region covered by the Arecibo Legacy Fast ALFA (ALFALFA) survey. We start with 344 ALFALFA HI sources without optical counterparts and remove those that do not seem to have dark galaxy origin. To do that, we first eliminate 83 sources that are known HI clouds probably formed from tidal interactions between galaxies and 13 sources that have optical counterparts. We then remove 56 sources located near other HI sources, which are likely to be HI clouds. We further exclude 10 sources that have nearby HI sources within the ALFALFA beam and 40 sources potentially associated with nearby galaxies. We perform visual inspection of optical images from DESI Legacy Imaging Survey with an $r$-band surface brightness limit of $\sim 28.5 \rm \ mag\ arcsec^{-2}$ as well as NUV images from GALEX to confirm the absence of stellar emission. We additionally inspect infrared images from WISE and AKARI for dust emission. As a result, we identify 142 dark galaxy candidates and analyze their physical properties by comparing with luminous galaxies. We find that the dark galaxy candidates generally have smaller dynamical masses, higher HI-to-dynamical mass ratios, and are located in less dense regions when compared to luminous galaxies, which is consistent with results from cosmological simulations. This sample provides an important testbed for studying the role of dark matter in galaxy formation and evolution.

Figures

Figures reproduced from arXiv: 2506.03678 by the authors.

Figure 1
Figure 1. Selection criterion for the H i clouds based on minimum projected distance. The colored dots represent AL￾FALFA sources without optical counterparts, whereas black dots (i.e. Luminous) represent ALFALFA high-quality de￾tections with optical counterparts. Blue dots represent H i clouds from the literature (step1) and red dots represent the remaining H i sources without optical counterparts. The black dashed line deno… view at source ↗
Figure 2
Figure 2. Example cutout images for a dark galaxy candidate AGC 249460. (a) The GALEX NUV (2315 ˚A). (b) The DESI Legacy Imaging Survey g, r, z coadded image. (c) The DESI Legacy Imaging Survey g, r, z 3-color composite image. White contours are the H i intensity within 2×W50 velocity width from the ALFALFA data cubes, with the lowest contour (dashed line) corresponding to the 2σ level. The labels on the contours indicate the… view at source ↗
Figure 4
Figure 4. The sky coverage for the computation of local density. The green region represents the SDSS DR7 main survey area, while the black region represents the area cov￾ered by the ALFALFA sources. The red dots are the dark galaxy candidates within the SDSS DR7 main survey area. studying the impact of the local environment on galaxy properties (Park et al. 2007). Furthermore, we choose a mass-to-light ratio of γ(early) = 2γ… view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: The distributions of integrated H i flux and H i mass for the dark galaxy candidates, luminous galaxies, and H i clouds, as a function of redshift. The dashed lines in the first and second panels represent SNR = 5, assuming W50 = 100 km s−1 and median RMS (i.e. σrms = …
Figure 6
Figure 6. Figure 6: The internal properties and local environments of the ALFALFA galaxies (top) and the TNG50 simulation (bottom). The black, red and blue symbols represent the luminous, dark galaxy candidates and H i clouds, respectively. left: H i and dynamical(total) mass distribution…
Figure 7
Figure 7. Figure 7: The distributions of H i-to-dynamical mass ratio and local density for the dark galaxy candidates and the comparison samples. In both panels, the red lines represent the dark galaxy candidates. In the left panel, the black histogram represents the mass controlled lumin…
Figure 8
Figure 8. Figure 8: (left) Median-stacked r-band image of the dark galaxy candidates, smoothed with a Gaussian kernel of σ = 5′′. The red solid and dashed circle represent radii of 1.5 ′ and 0.5 ′ , respectively. (right) Magnitude distribution of the median-stacked image, measured at rand…

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Works this paper leans on

74 extracted references · 17 canonical work pages · cited by 1 Pith paper

  1. [1]

    N., Adelman-McCarthy, J

    Abazajian, K. N., Adelman-McCarthy, J. K., Ag¨ ueros, M. A., et al. 2009, ApJS, 182, 543, doi: 10.1088/0067-0049/182/2/543 Ben ´ ıtez-Llambay, A., Navarro, J. F., Frenk, C. S., et al. 2017, MNRAS, 465, 3913, doi: 10.1093/mnras/stw2982 B ´ ılek, M., M¨ uller, O., Vudragovi´ c, A., & Taylor, R. 2020, A&A, 642, L10, doi: 10.1051/0004-6361/202039174

    doi:10.1088/0067-0049/182/2/543 2009

  2. [2]

    2015, MNRAS, 452, 2479, doi: 10.1093/mnras/stv1311

    Brown, T., Catinella, B., Cortese, L., et al. 2015, MNRAS, 452, 2479, doi: 10.1093/mnras/stv1311

    doi:10.1093/mnras/stv1311 2015

  3. [3]

    S., & Boylan-Kolchin, M

    Bullock, J. S., & Boylan-Kolchin, M. 2017, ARA&A, 55, 343, doi: 10.1146/annurev-astro-091916-055313

    doi:10.1146/annurev-astro-091916-055313 2017

  4. [4]

    M., Johnson, M., McQuinn, K

    Cannon, J. M., Johnson, M., McQuinn, K. B. W., et al. 2014, ApJL, 787, L1, doi: 10.1088/2041-8205/787/1/L1

    work page doi:10.1088/2041-8205/787/1/l1 2014

  5. [5]

    M., Martinkus, C

    Cannon, J. M., Martinkus, C. P., Leisman, L., et al. 2015, AJ, 149, 72, doi: 10.1088/0004-6256/149/2/72

    doi:10.1088/0004-6256/149/2/72 2015

  6. [6]

    N., Giovanelli, R., & Haynes, M

    Chengalur, J. N., Giovanelli, R., & Haynes, M. P. 1995, AJ, 109, 2415, doi: 10.1086/117460

    work page doi:10.1086/117460 1995

  7. [7]

    A., & van de Voort, F

    Crain, R. A., & van de Voort, F. 2023, ARA&A, 61, 473, doi: 10.1146/annurev-astro-041923-043618 Crone Odekon, M., Hallenbeck, G., Haynes, M. P., et al. 2018, ApJ, 852, 142, doi: 10.3847/1538-4357/aaa1e8

    doi:10.1146/annurev-astro-041923-043618 2023

  8. [9]

    J., Lang, D., et al

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168, doi: 10.3847/1538-3881/ab089d

    doi:10.3847/1538-3881/ab089d 2019

Show all 74 references
  1. [10]

    Diemer, B., Stevens, A. R. H., Forbes, J. C., et al. 2018, ApJS, 238, 33, doi: 10.3847/1538-4365/aae387

    2018 doi

  2. [11]

    Diemer, B., Stevens, A. R. H., Lagos, C. d. P., et al. 2019, MNRAS, 487, 1529, doi: 10.1093/mnras/stz1323

    2019 doi

  3. [12]

    2015, PASJ, 67, 50, doi: 10.1093/pasj/psv022

    Doi, Y., Takita, S., Ootsubo, T., et al. 2015, PASJ, 67, 50, doi: 10.1093/pasj/psv022

    2015 doi

  4. [14]

    E., et al

    Dong, F., Park, C., Hong, S. E., et al. 2023, ApJ, 953, 98, doi: 10.3847/1538-4357/acd185

    2023 doi

  5. [15]

    2024, ApJ, 964, 85, doi: 10.3847/1538-4357/ad234f

    Du, L., Du, W., Cheng, C., et al. 2024, ApJ, 964, 85, doi: 10.3847/1538-4357/ad234f

    2024 doi

  6. [16]

    P., Kent, B

    Giovanelli, R., Haynes, M. P., Kent, B. R., et al. 2005, AJ, 130, 2598, doi: 10.1086/497431

    2005 doi

  7. [17]

    E., & Gott, J

    Gunn, J. E., & Gott, J. Richard, I. 1972, ApJ, 176, 1, doi: 10.1086/151605

    1972 doi

  8. [18]

    P., Giovanelli, R., & Kent, B

    Haynes, M. P., Giovanelli, R., & Kent, B. R. 2007, ApJL, 665, L19, doi: 10.1086/521188 13

    2007 doi

  9. [19]

    P., Giovanelli, R., Martin, A

    Haynes, M. P., Giovanelli, R., Martin, A. M., et al. 2011, AJ, 142, 170, doi: 10.1088/0004-6256/142/5/170

    2011 doi

  10. [20]

    P., Giovanelli, R., Kent, B

    Haynes, M. P., Giovanelli, R., Kent, B. R., et al. 2018, ApJ, 861, 49, doi: 10.3847/1538-4357/aac956

    2018 doi

  11. [21]

    L., Dowell, J., Haynes, M

    Hoffman, G. L., Dowell, J., Haynes, M. P., & Giovanelli, R. 2019, AJ, 157, 194, doi: 10.3847/1538-3881/ab14f6

    2019 doi

  12. [22]

    L., Lu, N

    Hoffman, G. L., Lu, N. Y., Salpeter, E. E., & Connell, B. M. 1999, AJ, 117, 811, doi: 10.1086/300718

    1999 doi

  13. [23]

    P., & Geller, M

    Huchra, J. P., & Geller, M. J. 1982, ApJ, 257, 423, doi: 10.1086/160000

    1982 doi

  14. [24]

    S., Elbaz, D., Lee, J

    Hwang, H. S., Elbaz, D., Lee, J. C., et al. 2010, A&A, 522, A33, doi: 10.1051/0004-6361/201014807

    2010 doi

  15. [25]

    S., Geller, M

    Hwang, H. S., Geller, M. J., Park, C., et al. 2016, ApJ, 818, 173, doi: 10.3847/0004-637X/818/2/173

    2016 doi

  16. [26]

    Hwang, J.-S., Park, C., Banerjee, A., & Hwang, H. S. 2018, ApJ, 856, 160, doi: 10.3847/1538-4357/aab3ce

    2018 doi

  17. [27]

    2015, ApJ, 801, 96, doi: 10.1088/0004-637X/801/2/96

    Janowiecki, S., Leisman, L., J´ ozsa, G., et al. 2015, ApJ, 801, 96, doi: 10.1088/0004-637X/801/2/96

    2015 doi

  18. [28]

    Jimenez, R., & Heavens, A. F. 2020, MNRAS, 498, L93, doi: 10.1093/mnrasl/slaa135

    2020 doi

  19. [29]

    F., Hawkins, M

    Jimenez, R., Heavens, A. F., Hawkins, M. R. S., & Padoan, P. 1997, MNRAS, 292, L5, doi: 10.1093/mnras/292.1.L5 J´ ozsa, G. I. G., Jarrett, T. H., Cluver, M. E., et al. 2022, ApJ, 926, 167, doi: 10.3847/1538-4357/ac402b

    1997 doi

  20. [30]

    2024, ApJ, 964, 123, doi: 10.3847/1538-4357/ad245b

    Jung, M., Roca-F` abrega, S., Kim, J.-H., et al. 2024, ApJ, 964, 123, doi: 10.3847/1538-4357/ad245b

    2024 doi

  21. [31]

    R., Giovanelli, R., Haynes, M

    Kent, B. R., Giovanelli, R., Haynes, M. P., et al. 2007, ApJL, 665, L15, doi: 10.1086/521100

    2007 doi

  22. [32]

    2013, MNRAS, 432, 1701, doi: 10.1093/mnras/stt632

    Kim, J.-h., & Lee, J. 2013, MNRAS, 432, 1701, doi: 10.1093/mnras/stt632

    2013 doi

  23. [33]

    V., Valenzuela, O., & Prada, F

    Klypin, A., Kravtsov, A. V., Valenzuela, O., & Prada, F. 1999, ApJ, 522, 82, doi: 10.1086/307643

    1999 doi

  24. [34]

    Koopmann, R. A. 2008, in IAU Symposium, Vol. 244, Dark Galaxies and Lost Baryons, ed. J. I. Davies & M. J. Disney, 362–363, doi: 10.1017/S1743921307014287

    2008 doi

  25. [35]

    2020, MNRAS, 491, 4294, doi: 10.1093/mnras/stz3319

    Kraljic, K., Pichon, C., Codis, S., et al. 2020, MNRAS, 491, 4294, doi: 10.1093/mnras/stz3319

    2020 doi

  26. [36]

    S., Lee, J., Shin, J., & Song, H

    Lee, G., Hwang, H. S., Lee, J., Shin, J., & Song, H. 2024, ApJ, 962, 129, doi: 10.3847/1538-4357/ad1e5d

    2024 doi

  27. [37]

    C., et al

    Lee-Waddell, K., Spekkens, K., Cuillandre, J. C., et al. 2014, MNRAS, 443, 3601, doi: 10.1093/mnras/stu1345

    2014 doi

  28. [38]

    2017, in IAU Symposium, Vol

    Lee-Waddell, K., Spekkens, K., Chandra, P., et al. 2017, in IAU Symposium, Vol. 321, Formation and Evolution of Galaxy Outskirts, ed. A. Gil de Paz, J. H. Knapen, & J. C. Lee, 238–240, doi: 10.1017/S1743921316009066

    2017 doi

  29. [39]

    P., Giovanelli, R., et al

    Leisman, L., Haynes, M. P., Giovanelli, R., et al. 2016, MNRAS, 463, 1692, doi: 10.1093/mnras/stw2083

    2016 doi

  30. [40]

    L., Ball, C., et al

    Leisman, L., Rhode, K. L., Ball, C., et al. 2021, AJ, 162, 274, doi: 10.3847/1538-3881/ac2a38

    2021 doi

  31. [41]

    2015, A&A, 584, A113, doi: 10.1051/0004-6361/201526613

    Lelli, F., Duc, P.-A., Brinks, E., et al. 2015, A&A, 584, A113, doi: 10.1051/0004-6361/201526613

    2015 doi

  32. [42]

    2018, MNRAS, 480, 5113, doi: 10.1093/mnras/sty2206 Mart ´ ınez-Delgado, D., Cooper, A

    Marinacci, F., Vogelsberger, M., Pakmor, R., et al. 2018, MNRAS, 480, 5113, doi: 10.1093/mnras/sty2206 Mart ´ ınez-Delgado, D., Cooper, A. P., Rom´ an, J., et al. 2023, A&A, 671, A141, doi: 10.1051/0004-6361/202245011

    2018 doi

  33. [43]

    2005, ApJL, 622, L21, doi: 10.1086/429538

    Minchin, R., Davies, J., Disney, M., et al. 2005, ApJL, 622, L21, doi: 10.1086/429538

    2005 doi

  34. [44]

    1994, Nature, 370, 629, doi: 10.1038/370629a0

    Moore, B. 1994, Nature, 370, 629, doi: 10.1038/370629a0

    1994 doi

  35. [45]

    1999, ApJL, 524, L19, doi: 10.1086/312287

    Moore, B., Ghigna, S., Governato, F., et al. 1999, ApJL, 524, L19, doi: 10.1086/312287

    1999 doi

  36. [46]

    A., et al

    Morrissey, P., Conrow, T., Barlow, T. A., et al. 2007, ApJS, 173, 682, doi: 10.1086/520512

    2007 doi

  37. [47]

    I., Croton, D

    Muldrew, S. I., Croton, D. J., Skibba, R. A., et al. 2012, MNRAS, 419, 2670, doi: 10.1111/j.1365-2966.2011.19922.x

    2012

  38. [48]

    P., Pillepich, A., Springel, V., et al

    Naiman, J. P., Pillepich, A., Springel, V., et al. 2018, MNRAS, 477, 1206, doi: 10.1093/mnras/sty618

    2018 doi

  39. [49]

    2018, MNRAS, 475, 624, doi: 10.1093/mnras/stx3040 —

    Nelson, D., Pillepich, A., Springel, V., et al. 2018, MNRAS, 475, 624, doi: 10.1093/mnras/stx3040 —. 2019, MNRAS, 490, 3234, doi: 10.1093/mnras/stz2306

    2018 doi

  40. [50]

    1997, AJ, 114, 77, doi: 10.1086/118454 O’Beirne, T., Staveley-Smith, L., Wong, O

    Terzian, Y. 1997, AJ, 114, 77, doi: 10.1086/118454 O’Beirne, T., Staveley-Smith, L., Wong, O. I., et al. 2024, MNRAS, 528, 4010, doi: 10.1093/mnras/stae215

    1997 doi

  41. [51]

    2014, ApJ, 793, 46, doi: 10.1088/0004-637X/793/1/46

    Ogiya, G., & Mori, M. 2014, ApJ, 793, 46, doi: 10.1088/0004-637X/793/1/46

    2014 doi

  42. [52]

    A., Brinks, E., et al

    Oh, S.-H., Hunter, D. A., Brinks, E., et al. 2015, AJ, 149, 180, doi: 10.1088/0004-6256/149/6/180

    2015 doi

  43. [53]

    2005, A&A, 437, L19, doi: 10.1051/0004-6361:200500127

    Oosterloo, T., & van Gorkom, J. 2005, A&A, 437, L19, doi: 10.1051/0004-6361:200500127

    2005 doi

  44. [54]

    2012, ApJL, 759, L7, doi: 10.1088/2041-8205/759/1/L7

    Park, C., Choi, Y.-Y., Kim, J., et al. 2012, ApJL, 759, L7, doi: 10.1088/2041-8205/759/1/L7

    2012 doi

  45. [55]

    S., et al

    Park, C., Choi, Y.-Y., Vogeley, M. S., et al. 2007, ApJ, 658, 898, doi: 10.1086/511059

    2007 doi

  46. [56]

    Richard, I., & Choi, Y.-Y

    Park, C., Gott, J. Richard, I., & Choi, Y.-Y. 2008, ApJ, 674, 784, doi: 10.1086/524192

    2008 doi

  47. [57]

    S., Park, H., & Lee, J

    Park, C., Hwang, H. S., Park, H., & Lee, J. C. 2018, Nature Astronomy, 2, 162, doi: 10.1038/s41550-017-0332-9

    2018 doi

  48. [58]

    2018, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112

    Pillepich, A., Nelson, D., Hernquist, L., et al. 2018, MNRAS, 475, 648, doi: 10.1093/mnras/stx3112

    2018 doi

  49. [59]

    2019, MNRAS, 490, 3196, doi: 10.1093/mnras/stz2338 Planck Collaboration, Ade, P

    Pillepich, A., Nelson, D., Springel, V., et al. 2019, MNRAS, 490, 3196, doi: 10.1093/mnras/stz2338 Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2016, A&A, 594, A13, doi: 10.1051/0004-6361/201525830

    2019 doi

  50. [60]

    2007, AJ, 133, 2087, doi: 10.1086/513515

    Saintonge, A. 2007, AJ, 133, 2087, doi: 10.1086/513515

    2007 doi

  51. [61]

    Schneider, S. E. 1989, ApJ, 343, 94, doi: 10.1086/167687

    1989 doi

  52. [62]

    2010, MNRAS, 401, 791, doi: 10.1111/j.1365-2966.2009.15715.x 14

    Springel, V. 2010, MNRAS, 401, 791, doi: 10.1111/j.1365-2966.2009.15715.x 14

    2010

  53. [64]

    2018, MNRAS, 475, 676, doi: 10.1093/mnras/stx3304

    Springel, V., Pakmor, R., Pillepich, A., et al. 2018, MNRAS, 475, 676, doi: 10.1093/mnras/stx3304

    2018 doi

  54. [65]

    2014, ApJ, 790, 10, doi: 10.1088/0004-637X/790/1/10

    Sternberg, A., Le Petit, F., Roueff, E., & Le Bourlot, J. 2014, ApJ, 790, 10, doi: 10.1088/0004-637X/790/1/10

    2014 doi

  55. [66]

    P., Giovanelli, R., et al

    Stierwalt, S., Haynes, M. P., Giovanelli, R., et al. 2009, AJ, 138, 338, doi: 10.1088/0004-6256/138/2/338

    2009 doi

  56. [67]

    1992, MNRAS, 259, 413, doi: 10.1093/mnras/259.3.413

    Sutherland, W., & Saunders, W. 1992, MNRAS, 259, 413, doi: 10.1093/mnras/259.3.413

    1992 doi

  57. [68]

    R., Strauss, M

    Tegmark, M., Blanton, M. R., Strauss, M. A., et al. 2004, ApJ, 606, 702, doi: 10.1086/382125

    2004 doi

  58. [69]

    Teyssier, R., Pontzen, A., Dubois, Y., & Read, J. I. 2013, MNRAS, 429, 3068, doi: 10.1093/mnras/sts563

    2013 doi

  59. [70]

    2001, MNRAS, 322, 658, doi: 10.1046/j.1365-8711.2001.04158.x van Dokkum, P., Abraham, R., Brodie, J., et al

    Trentham, N., M¨ oller, O., & Ramirez-Ruiz, E. 2001, MNRAS, 322, 658, doi: 10.1046/j.1365-8711.2001.04158.x van Dokkum, P., Abraham, R., Brodie, J., et al. 2016, ApJL, 828, L6, doi: 10.3847/2041-8205/828/1/L6

    2001

  60. [71]

    P., & Jimenez, R

    Verde, L., Oh, S. P., & Jimenez, R. 2002, MNRAS, 336, 541, doi: 10.1046/j.1365-8711.2002.05771.x

    2002

  61. [72]

    S., Serra, P., et al

    Wang, J., Koribalski, B. S., Serra, P., et al. 2016, MNRAS, 460, 2143, doi: 10.1093/mnras/stw1099

    2016 doi

  62. [73]

    2020, ApJS, 248, 32, doi: 10.3847/1538-4365/ab908c

    Weinberger, R., Springel, V., & Pakmor, R. 2020, ApJS, 248, 32, doi: 10.3847/1538-4365/ab908c

    2020 doi

  63. [74]

    R., Hopkins, P

    Wetzel, A. R., Hopkins, P. F., Kim, J.-h., et al. 2016, ApJL, 827, L23, doi: 10.3847/2041-8205/827/2/L23

    2016 doi

  64. [75]

    I., Stevens, A

    Wong, O. I., Stevens, A. R. H., For, B. Q., et al. 2021, MNRAS, 507, 2905, doi: 10.1093/mnras/stab2262

    2021 doi

  65. [76]

    L., Eisenhardt, P

    Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., et al. 2010, AJ, 140, 1868, doi: 10.1088/0004-6256/140/6/1868

    2010 doi

  66. [77]

    2023, ApJL, 944, L40, doi: 10.3847/2041-8213/acb932 15 T able 1.Dark galaxy candidates catalog AGC ID Name R.A

    Xu, J.-L., Zhu, M., Yu, N., et al. 2023, ApJL, 944, L40, doi: 10.3847/2041-8213/acb932 15 T able 1.Dark galaxy candidates catalog AGC ID Name R.A. DEC.cz ⊙ DHI SNRW 50 W20 log MHI log Mdyn Asymρ 20/¯ρLRd min code note (deg) (deg) (km s −1) (Mpc) (km s −1) (km s −1) (log M ⊙) (...

    2023 doi

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