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Void galaxies stay bluer longer, especially the small ones

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Void galaxies identified by local volume in non-spherical voids are bluer and more star-forming than non-void galaxies, with the environmental effect decreasing with stellar mass.

T0 review reviewed 2026-07-09 challenge →

load-bearing objection Local-volume-based void galaxy classification is a reasonable methodological step, but the flux-limited sample design may manufacture the central mass-dependent trend. the 2 major comments →

arxiv 2607.07268 v1 pith:LNXIP4O4 submitted 2026-07-08 astro-ph.GA astro-ph.CO

Quantifying Environmental Effects on Galaxy Properties using Non-spherical Voids Identified from SDSS DR7

Yingxiao Song , Xiaohu Yang , Yan Gong , Haitao Miao , Xingchen Zhou , Yizhou Gu , Yingjie Peng This is my paper
classification astro-ph.GA astro-ph.CO PACS 98.62.Gq98.65.Dx98.80.Es
keywords cosmic voidsgalaxy evolutiongalaxy environmentsstar formation rategalaxy colorVoronoi tessellationSDSSstellar mass
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

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 asks whether living in a cosmic void — a vast, near-empty region of the universe — actually changes how a galaxy evolves, or whether void galaxies simply look different because they tend to be less massive. The authors identify non-spherical voids using the Sloan Digital Sky Survey and classify void galaxies by the local volume around each galaxy (a proxy for local density) rather than by distance from a void center. They find that void galaxies are systematically less massive, fainter, bluer, and more actively forming stars than galaxies in denser regions. To separate intrinsic mass effects from genuine environmental effects, they bin galaxies by stellar mass and compute the ratio of blue-to-red and star-forming-to-quiescent galaxies for void versus non-void populations within each bin. Both ratios are higher for void galaxies at every mass, and the ratio of void-to-non-void values decreases with increasing stellar mass across the range where statistics are robust. This means the void environment has a stronger influence on lower-mass galaxies, where it more strongly favors blue, star-forming states over red, quiescent ones.

Core claim

The central result is that the environmental effect of cosmic voids on galaxy color and star formation is mass-dependent: when you compare the blue-to-red ratio and the star-forming-to-quiescent ratio of void galaxies to non-void galaxies at fixed stellar mass, the void-to-non-void ratio decreases with stellar mass over the range where the measurement is reliable. At the low-mass end, void galaxies are roughly twice as likely to be blue and star-forming relative to their non-void counterparts; at the high-mass end, the environmental effect nearly vanishes. The paper also demonstrates a practical method for classifying void galaxies in non-spherical voids — using the Voronoi cell volume of a

What carries the argument

The methodological core is a two-stage classification of void galaxies: (1) identify non-spherical voids via Voronoi tessellation and watershed algorithm, keeping only voids larger than 2.5 times the mean galaxy separation; (2) classify a galaxy as a void galaxy only if its Voronoi cell volume exceeds a multiple (f=2) of the mean local volume at its redshift. This local-volume criterion avoids misclassifying galaxies near overdense void boundaries that a simple distance-to-center cut would include. To isolate environmental effects, the authors apply the V_max method to correct for flux-limited survey selection, bin galaxies by stellar mass, fit bimodal Gaussian distributions to the g-r color

Load-bearing premise

The classification assumes that the Voronoi cell volume in redshift space faithfully represents the true local density around each galaxy, and that redshift-space distortions from peculiar velocities do not significantly bias which galaxies are classified as void galaxies.

What would settle it

If the same analysis were performed in real space (using true comoving positions from a simulation mock catalog) and the void-to-non-void ratios no longer showed a decreasing trend with stellar mass, the claimed mass-dependent environmental effect would be an artifact of redshift-space distortions rather than a genuine environmental signal.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • If the mass-dependent environmental effect is real, cosmological simulations of galaxy formation must reproduce a stronger void influence on low-mass galaxies, providing a testable prediction for hydrodynamical simulations.
  • The local-volume classification method can be applied to future spectroscopic surveys (e.g., DESI, Euclid) with larger volumes, enabling tighter constraints on how underdense environments affect galaxy evolution across a wider mass range.
  • If the decreasing trend with stellar mass extends below the current lower limit, the environmental effect may be even stronger for dwarf galaxies in voids, which would be testable with deeper surveys.
  • The method could be extended to quantify environmental effects in other cosmic-web structures (filaments, sheets, clusters) by using local volume as a continuous density proxy rather than a binary void/non-void classification.

Where Pith is reading between the lines

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

  • The near-disappearance of the environmental effect at high stellar mass suggests that massive galaxies may have internal processes (e.g., AGN feedback, morphological quenching) that dominate over external environmental influences, making them insensitive to large-scale density.
  • The mass-dependence could reflect different gas-accretion modes: cold-mode accretion, which dominates in low-mass galaxies, may be more sensitive to the surrounding density field than the hot-mode accretion relevant for massive galaxies.
  • If redshift-space distortions bias the local volume estimates as the authors acknowledge, the effect could be partially systematic rather than purely environmental — galaxies along the line of sight through voids may have inflated Voronoi volumes, which would make the measured environmental effect an upper limit.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 4 minor

Summary. This paper identifies non-spherical cosmic voids from the SDSS DR7 galaxy catalog using Voronoi tessellation and the watershed algorithm (VIDE). It proposes a new method for classifying void galaxies based on local Voronoi cell volume, rather than a simple distance-to-center cut, to account for the irregular shapes of voids. The authors compare the properties of void and non-void galaxies, finding that void galaxies are less massive, fainter, bluer, and have higher sSFR. To isolate environmental effects from intrinsic stellar mass correlations, they apply Vmax weighting and divide galaxies into stellar mass bins. By computing the ratio of blue-to-red (R_b/r) and star-forming-to-quiescent (R_SF/Q) galaxies for void versus non-void samples, they find that both ratios decrease with stellar mass over log[M*/M_sun] = 9.4 to 10.4, concluding that low-density environments have a stronger impact on lower-mass galaxies.

Significance. The paper addresses a relevant methodological issue: how to robustly classify galaxies residing within non-spherical voids. The local volume criterion is a sensible approach to mitigating the boundary contamination inherent to watershed-based void finders. The use of Vmax weighting and mass-binned comparisons to disentangle environmental effects from intrinsic scaling relations is standard and appropriate. The finding that environmental effects are stronger in lower-mass galaxies is consistent with expectations from galaxy formation models. The quantitative framework using R_b/r and R_SF/Q ratios provides a clear, falsifiable measurement of environmental impact.

major comments (2)
  1. Section 2.2 / Section 4.1: The central claim that R_b/r and R_SF/Q decrease with stellar mass is potentially confounded by the use of a flux-limited sample for void identification. The authors explicitly state (Section 2.2) that they do not apply an absolute magnitude cut, meaning the tracer density decreases with redshift. While the V_local(z) correction removes the mean redshift dependence, it does not account for the increased scatter in V_local at higher redshifts where tracer density is lower. In a flux-limited sample, more massive galaxies are preferentially found at higher redshifts. Thus, the higher stellar mass bins (9.8-10.4) will contain a larger fraction of high-z galaxies with noisier V_local estimates. This could lead to a higher misclassification rate of void/non-void status in high-mass bins, diluting the environmental signal and potentially manufacturing the observed 'de
  2. Section 2.3: The dismissal of redshift-space distortions (RSD) is a one-paragraph assertion without quantitative validation. The entire classification scheme depends on V_local being a faithful proxy for true local density. RSD can alter apparent local densities and Voronoi cell volumes along the line of sight, potentially biasing which galaxies are classified as void galaxies. While the authors argue that retaining only large voids mitigates this, the effect on the local volume estimates for individual galaxies is not addressed. A simple test, such as applying the analysis to a volume-limited subsample (as done in Appendix A for spherical voids) or checking the redshift distribution of the classified void galaxies, would strengthen this premise.
minor comments (4)
  1. Section 2.3: The choice of f=2 as the fiducial threshold is justified heuristically (balancing sample size vs. isolation). An objective criterion, or a demonstration that the main conclusions (the decreasing trend with mass) are robust to the choice of f (e.g., by showing results for f=1 and f=3), would be beneficial.
  2. Section 4.2.1: The last R_b/r value (1.419 ± 0.056) in the highest mass bin appears to deviate from the otherwise smooth decreasing trend. The text notes an 'overall decreasing trend' but does not comment on this specific point. A brief discussion of this feature would be helpful.
  3. Figure 4: The y-axis label 'Weighted Fraction' is used, but it would be clearer to label it as 'Number Density' or 'Weighted Number Density' to explicitly connect it to the Vmax weighting described in the text.
  4. Table 1: The sSFR values are given in log[yr^-1], but the table header could be more explicit (e.g., log[sSFR/yr^-1]) for consistency with the text and Figure 3.

Circularity Check

0 steps flagged

No circularity found: the derivation chain is self-contained and the central claim is not forced by construction or self-citation.

full rationale

The paper's central claim is that the ratios R_b/r and R_SF/Q (void-to-non-void) decrease with stellar mass, indicating stronger environmental effects in lower-mass galaxies. This claim is derived from independent measurements: (1) void/non-void classification based on Voronoi cell local volumes (Section 2.3), (2) V_max-weighted number density distributions of g-r color and sSFR in stellar mass bins (Section 4.1), and (3) computation of blue-to-red and star-forming-to-quiescent ratios from these distributions (Section 4.2). None of these steps reduce to their inputs by construction. The K-correction coefficients from Wang et al. (2024) (Table 2) are external inputs that do not encode the target result. The dividing lines for blue/red classification (Section 4.2.1) are derived from bimodal Gaussian fits to the full galaxy sample, not fitted to reproduce the void/non-void contrast. The sSFR dividing line (Eq. 5) is a fixed stellar-mass-dependent threshold adopted from external convention, not fitted to the data. Self-citations (Song et al. 2024a,b, 2025a,b, 2026) appear only for void selection thresholds and cosmological context, none of which are load-bearing for the specific R_b/r and R_SF/Q trend claims. The skeptic's concern about flux-limited sample noise at high redshift is a correctness/selection-bias issue, not a circularity issue — it does not make the claimed trend tautological with respect to the paper's definitions or fitted parameters. The derivation is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

4 free parameters · 4 axioms · 0 invented entities

The paper introduces no new physical entities, particles, forces, or dimensions. It uses standard cosmological objects (voids, galaxies) and established computational constructs (Voronoi cells, watershed basins). The free parameters are methodological choices (thresholds, dividing lines) rather than new physical constants.

free parameters (4)
  • f (local volume threshold multiplier) = 2
    Chosen heuristically in Section 2.3: f=1 includes too many galaxies (~14%), f=3 too few (~4%). No objective optimization criterion is applied; f=2 is selected as a 'reliable choice' balancing sample size and isolation.
  • 2.5 x MGS(z) minimum void radius cut = 2.5
    Adopted from prior cosmological void studies (Ronconi et al. 2019; Contarini et al. 2019). This threshold determines which voids enter the catalog and thus which galaxies can be classified as void galaxies.
  • sSFR dividing line coefficients = -0.46 and -6.2
    The star-forming/quiescent dividing line log[sSFR/yr^-1] = -0.46*log[M*/M_sun] - 6.2 is adopted without derivation or citation in Section 4.2.2. These coefficients determine the SF/Q classification and directly affect R_SF/Q.
  • g-r color dividing lines = 0.628, 0.644, 0.671, 0.685, 0.694
    Derived from bimodal Gaussian fitting to the all-galaxy sample in each mass bin (Section 4.2.1). These are data-fitted parameters that determine the blue/red classification and thus R_b/r.
axioms (4)
  • domain assumption Voronoi cell volume is a faithful proxy for local galaxy density
    Section 2.2: 'The volume of this cell, V_cell, reflects the local volume V_local = V_cell around that galaxy. The local number density is then estimated as rho_local = 1/V_local.' This is a standard assumption in Voronoi-based void finding but assumes galaxies are unbiased tracers of the density field.
  • ad hoc to paper Redshift-space distortions do not significantly affect void identification or local volume estimates
    Section 2.3: 'our results are not expected to be significantly impacted by RSD effects.' This is asserted without quantitative validation, despite RSD being known to affect apparent densities along the line of sight.
  • domain assumption The K-correction coefficients from Wang et al. (2024) are applicable to the SDSS DR7 sample
    Section 4.1: coefficients a_mu, b_mu, c_mu for the mean r-band K-correction in four color bins are taken from Wang et al. (2024) over z=0-0.2. The applicability to this specific sample is assumed but not independently verified.
  • domain assumption Stellar mass is the primary intrinsic confounding variable for color and sSFR
    Section 1: the paper controls for stellar mass to isolate environmental effects, assuming other intrinsic properties (metallicity, morphology, halo mass) are secondary or correlated with stellar mass.

reviewed 2026-07-09 · how reviews work

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Cite this review

Pith. "Pith review of Quantifying Environmental Effects on Galaxy Properties using Non-spherical Voids Identified from SDSS DR7." pith.science (2026). https://pith.science/paper/LNXIP4O4

@misc{pith2026260707268,
  author       = {Pith},
  title        = {Pith review of: Quantifying Environmental Effects on Galaxy Properties using Non-spherical Voids Identified from SDSS DR7},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LNXIP4O4}},
  note         = {Machine review of arXiv:2607.07268}
}
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read the original abstract

Cosmic voids provide a distinct low-density region for studying the environmental effects of galaxy properties. Using the SDSS DR7 catalog, we identify non-spherical voids via Voronoi tessellation and the watershed algorithm, and classify void galaxies based on their local volume. We compare and find that void galaxies classified by this method are systematically less massive, fainter, bluer, and have higher specific star formation rate (sSFR) than non-void galaxies and all galaxy samples. We then divide void and non-void galaxies into stellar mass bins to focus on the environmental dependence of $g-r$ color and sSFR. By further classifying galaxies into blue/red and star-forming/quiescent populations, we calculate the ratio of blue to red and star-forming to quiescent for void and non-void galaxies separately. Comparing the ratio of the void value to the non-void value for both metrics presents an overall decreasing trend with stellar mass $M_*$ over the $9.4-10.4$ range in $\log[M_*/\mathrm{M}_\odot]$, indicating a stronger environmental effect in lower-mass systems. These results show that our classification of void galaxies in non-spherical voids based on local volume offers a robust approach for quantifying the influence of underdense environments on galaxy evolution.

Figures

Figures reproduced from arXiv: 2607.07268 by Haitao Miao, Xiaohu Yang, Xingchen Zhou, Yan Gong, Yingjie Peng, Yingxiao Song, Yizhou Gu.

Figure 1
Figure 1. Figure 1: The mean galaxy separation MGS(z) (solid) and the corresponding 2.5×MGS(z) threshold used for void se￾lection (dashed), derived from our SDSS DR7 galaxy catalog at z = 0 − 0.114. The redshift bins have a width of 0.005. In this work, we first identify non-spherical void sam￾ples with Voronoi tessellation and the watershed algo￾rithm, using the galaxy catalog from the seventh data release (DR7) of the Sloan… view at source ↗
Figure 2
Figure 2. Figure 2: Left panel: The local volume distribution of our catalog from the SDSS DR7 in 0 < z ≤ 0.114. The color (from purple to yellow) and the size of the dots (from small to large) both scale with Vlocal. Right panel: The mean local volume as a function of redshift for our SDSS DR7 sample at z ≤ 0.114. The V¯local(z) is computed in redshift bins with a width of 0.005. 2. DATA ANALYSIS 2.1. Galaxy Catalog We use t… view at source ↗
Figure 3
Figure 3. Figure 3: The normalized number distributions of M∗, Mr, g − r, and sSFR for the void galaxy sample classified from our non-spherical void catalog with f = 2 (red), the non-void sample (blue), and the all galaxy sample (gray shaded). Each distribution is normalized by the total number of galaxies in the corresponding sample. luminosity, color, and SFR. Stellar mass M∗ drives the evolution of a galaxy, as it correlat… view at source ↗
Figure 4
Figure 4. Figure 4: The normalized number density distributions of g −r color (top) and sSFR (bottom) for void galaxies (dark red) and non-void galaxies (dark blue) across different stellar mass bins. These distributions are normalized to the total weight within each mass bin. lated, while the void galaxy sample with f = 3 includes only about 4% of the full sample, and the number of void galaxies would be too small to provide… view at source ↗
Figure 5
Figure 5. Figure 5: Classification of galaxies into blue cloud and red sequence populations across five stellar mass bins based on the normalized number density distribution of g − r color. The gray shaded histogram represents the distribution of all galaxies from our SDSS DR7 catalog. The blue and red dashed lines show the bimodal Gaussian fittings for the blue cloud and red sequence populations, respectively. The black soli… view at source ↗
Figure 6
Figure 6. Figure 6: The ratio of blue to red galaxies Rb/r as a func￾tion of stellar mass for void galaxies (dark red) and non-void galaxies (dark blue). The bottom panel shows the ratio of these two values, Rb/r. The error bars and shaded regions indicate the 1σ uncertainties. To calculate Vmax, we first classify galaxies into four color bins according to their color r − z within z = 0 − 0.114, denoted by µ ∈ {1, 2, 3, 4}. F… view at source ↗
Figure 7
Figure 7. Figure 7: Classification of galaxies into star-forming and quiescent populations across five stellar mass bins based on the normalized number density distribution of sSFR. The gray shaded histogram represents the distribution of all galaxies from our SDSS DR7 catalog. The black vertical dashed line marks the dividing line that separates the star-forming and quiescent populations. 4.2. Quantifying Environmental Effec… view at source ↗
Figure 8
Figure 8. Figure 8: The ratio of star-forming to quiescent galaxies RSF/Q as a function of stellar mass for void galaxies (dark red) and non-void galaxies (dark blue). The bottom panel shows the ratio of these two values, RSF/Q. The error bars and shaded regions indicate the 1σ uncertainties. 1.164 ± 0.044, and 1.419 ± 0.056, respectively. We find that this ratio exhibits an overall decreasing trend with stellar mass, confirm… view at source ↗
Figure 9
Figure 9. Figure 9: The normalized number distributions of M∗, Mr, g − r, and sSFR for the void galaxy sample classified from the spherical void catalog (red), non-void sample (blue), and the all galaxy sample (gray shaded). Each distribution is normalized by the total number of galaxies in the corresponding sample. This volume-limited sample is adopted to be consistent with previous work (e.g. Douglass et al. 2023; Zaidouni … view at source ↗

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Reference graph

Works this paper leans on

55 extracted references · 55 canonical work pages · 5 internal anchors

  1. [1]

    , keywords =

    Ahumada, R., Allende Prieto, C., Almeida, A., et al. 2020, ApJS, 249, 3, doi: 10.3847/1538-4365/ab929e

    work page doi:10.3847/1538-4365/ab929e 2020

  2. [2]

    2022, MNRAS, 513, 186, doi: 10.1093/mnras/stac828 4 https://github.com/desi-ur/vast

    Aubert, M., Cousinou, M.-C., Escoffier, S., et al. 2022, MNRAS, 513, 186, doi: 10.1093/mnras/stac828 4 https://github.com/desi-ur/vast

    work page doi:10.1093/mnras/stac828 2022

  3. [3]

    M., et al

    Beygu, B., Kreckel, K., van der Hulst, J. M., et al. 2016, MNRAS, 458, 394, doi: 10.1093/mnras/stw280

    work page doi:10.1093/mnras/stw280 2016

  4. [4]

    2011, AJ, 142, 31, doi: 10.1088/0004-6256/142/1/31 Non-spherical Void Galaxies11

    Price-Whelan, A. 2011, AJ, 142, 31, doi: 10.1088/0004-6256/142/1/31 Non-spherical Void Galaxies11

    work page doi:10.1088/0004-6256/142/1/31 2011

  5. [5]

    R., Kofman, L., & Pogosyan, D

    Bond, J. R., Kofman, L., & Pogosyan, D. 1996, Nature, 380, 603, doi: 10.1038/380603a0

    work page internal anchor Pith review doi:10.1038/380603a0 1996

  6. [6]

    2023, A&A, 670, A47, doi: 10.1051/0004-6361/202244445

    Bonici, M., Carbone, C., Davini, S., et al. 2023, A&A, 670, A47, doi: 10.1051/0004-6361/202244445

    work page doi:10.1051/0004-6361/202244445 2023

  7. [7]

    Brinchmann, J., Charlot, S., White, S. D. M., et al. 2004, MNRAS, 351, 1151, doi: 10.1111/j.1365-2966.2004.07881.x

    work page doi:10.1111/j.1365-2966.2004.07881.x 2004

  8. [8]

    2021, MNRAS, 504, 5021, doi: 10.1093/mnras/stab1112

    Contarini, S., Marulli, F., Moscardini, L., et al. 2021, MNRAS, 504, 5021, doi: 10.1093/mnras/stab1112

    work page doi:10.1093/mnras/stab1112 2021

  9. [9]

    2023, ApJ, 953, 46, doi: 10.3847/1538-4357/acde54 —

    Contarini, S., Pisani, A., Hamaus, N., et al. 2023, ApJ, 953, 46, doi: 10.3847/1538-4357/acde54 —. 2024, A&A, 682, A20, doi: 10.1051/0004-6361/202347572

    work page doi:10.3847/1538-4357/acde54 2023

  10. [10]

    2019, MNRAS, 488, 3526, doi: 10.1093/mnras/stz1989

    Contarini, S., Ronconi, T., Marulli, F., et al. 2019, MNRAS, 488, 3526, doi: 10.1093/mnras/stz1989

    work page doi:10.1093/mnras/stz1989 2019

  11. [11]

    2026, A&A Rv, 34, 1, doi: 10.1007/s00159-026-00166-x

    Contarini, S., Verza, G., & Pisani, A. 2026, A&A Rv, 34, 1, doi: 10.1007/s00159-026-00166-x

    work page doi:10.1007/s00159-026-00166-x 2026

  12. [12]

    2022, A&A, 667, A162, doi: 10.1051/0004-6361/202244095

    Contarini, S., Verza, G., Pisani, A., et al. 2022, A&A, 667, A162, doi: 10.1051/0004-6361/202244095

    work page doi:10.1051/0004-6361/202244095 2022

  13. [13]

    Curtis, O., McDonough, B., & Brainerd, T. G. 2024, ApJ, 962, 58, doi: 10.3847/1538-4357/ad18b4 —. 2026, ApJ, 998, 325, doi: 10.3847/1538-4357/ae2f48 Dom´ ınguez-G´ omez, J., P´ erez, I., Ruiz-Lara, T., et al. 2023, Nature, 619, 269, doi: 10.1038/s41586-023-06109-1

    work page doi:10.3847/1538-4357/ad18b4 2024

  14. [14]

    2022, The Journal of Open Source Software, 7, 4033, doi: 10.21105/joss.04033

    Douglass, K., Veyrat, D., O’Neill, S., et al. 2022, The Journal of Open Source Software, 7, 4033, doi: 10.21105/joss.04033

    work page doi:10.21105/joss.04033 2022

  15. [15]

    A., Veyrat, D., & BenZvi, S

    Douglass, K. A., Veyrat, D., & BenZvi, S. 2023, ApJS, 265, 7, doi: 10.3847/1538-4365/acabcf

    work page doi:10.3847/1538-4365/acabcf 2023

  16. [16]

    1997, ApJ, 491, 421, doi: 10.1086/304973

    El-Ad, H., & Piran, T. 1997, ApJ, 491, 421, doi: 10.1086/304973

    work page doi:10.1086/304973 1997

  17. [17]

    A., Kannappan, S

    Florez, J., Berlind, A. A., Kannappan, S. J., et al. 2021, ApJ, 906, 97, doi: 10.3847/1538-4357/abca9f

    work page doi:10.3847/1538-4357/abca9f 2021

  18. [18]

    E., Siegmund, W

    Gunn, J. E., Siegmund, W. A., Mannery, E. J., et al. 2006, AJ, 131, 2332, doi: 10.1086/500975

    work page doi:10.1086/500975 2006

  19. [19]

    M., et al

    Hamaus, N., Pisani, A., Sutter, P. M., et al. 2016, PhRvL, 117, 091302, doi: 10.1103/PhysRevLett.117.091302

    work page doi:10.1103/physrevlett.117.091302 2016

  20. [20]

    2022, A&A, 658, A20, doi: 10.1051/0004-6361/202142073

    Hamaus, N., Aubert, M., Pisani, A., et al. 2022, A&A, 658, A20, doi: 10.1051/0004-6361/202142073

    work page doi:10.1051/0004-6361/202142073 2022

  21. [21]

    Hoyle, F., & Vogeley, M. S. 2002, ApJ, 566, 641, doi: 10.1086/338340

    work page doi:10.1086/338340 2002

  22. [22]

    MNRAS , author =

    Hoyle, F., Vogeley, M. S., & Pan, D. 2012, MNRAS, 426, 3041, doi: 10.1111/j.1365-2966.2012.21943.x

    work page doi:10.1111/j.1365-2966.2012.21943.x 2012

  23. [23]

    A., et al

    Kreckel, K., Platen, E., Arag´ on-Calvo, M. A., et al. 2012, AJ, 144, 16, doi: 10.1088/0004-6256/144/1/16

    work page doi:10.1088/0004-6256/144/1/16 2012

  24. [24]

    2026, arXiv e-prints, arXiv:2602.22990, doi: 10.48550/arXiv.2602.22990

    Zaman, Z. 2026, arXiv e-prints, arXiv:2602.22990, doi: 10.48550/arXiv.2602.22990

    work page doi:10.48550/arxiv.2602.22990 2026

  25. [25]

    F., & Winther, H

    Mauland, R., Elgarøy, Ø., Mota, D. F., & Winther, H. A. 2023, A&A, 674, A185, doi: 10.1051/0004-6361/202346287

    work page doi:10.1051/0004-6361/202346287 2023

  26. [26]

    M., Moreno, J., White, A., et al

    Moorman, C. M., Moreno, J., White, A., et al. 2016, ApJ, 831, 118, doi: 10.3847/0004-637X/831/2/118

    work page doi:10.3847/0004-637x/831/2/118 2016

  27. [27]

    M., Vogeley, M

    Moorman, C. M., Vogeley, M. S., Hoyle, F., et al. 2015, ApJ, 810, 108, doi: 10.1088/0004-637X/810/2/108

    work page doi:10.1088/0004-637x/810/2/108 2015

  28. [28]

    Nadathur, S., & Percival, W. J. 2019, MNRAS, 483, 3472, doi: 10.1093/mnras/sty3372

    work page doi:10.1093/mnras/sty3372 2019

  29. [29]

    J., et al

    Nadathur, S., Woodfinden, A., Percival, W. J., et al. 2020, MNRAS, 499, 4140, doi: 10.1093/mnras/staa3074

    work page doi:10.1093/mnras/staa3074 2020

  30. [30]

    Neyrinck, M. C. 2008, MNRAS, 386, 2101, doi: 10.1111/j.1365-2966.2008.13180.x

    work page doi:10.1111/j.1365-2966.2008.13180.x 2008

  31. [31]

    S., et al

    Park, C., Choi, Y.-Y., Vogeley, M. S., et al. 2007, ApJ, 658, 898, doi: 10.1086/511059 Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 641, A6, doi: 10.1051/0004-6361/201833910

    work page doi:10.1086/511059 2007

  32. [32]

    Platen, E., van de Weygaert, R., & Jones, B. J. T. 2007, MNRAS, 380, 551, doi: 10.1111/j.1365-2966.2007.12125.x

    work page doi:10.1111/j.1365-2966.2007.12125.x 2007

  33. [33]

    Environmental Dependence of Galaxy properties: A study of 341 Ring Galaxies in Cosmic Voids

    Poudel, S., & Aryal, B. 2026, arXiv e-prints, arXiv:2604.09983, doi: 10.48550/arXiv.2604.09983 Radinovi´ c, S., Winther, H. A., Nadathur, S., et al. 2024, A&A, 691, A39, doi: 10.1051/0004-6361/202451358 Radinovi´ c, S., Nadathur, S., Winther, H. A., et al. 2023, A&A, 677, A78, doi: 10.1051/0004-6361/202346121 Rodr´ ıguez-Medrano, A. M., Springel, V., Stas...

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2604.09983 2026

  34. [34]

    R., Vogeley, M

    Rojas, R. R., Vogeley, M. S., Hoyle, F., & Brinkmann, J. 2004, ApJ, 617, 50, doi: 10.1086/425225 —. 2005, ApJ, 624, 571, doi: 10.1086/428476

    work page doi:10.1086/425225 2004

  35. [35]

    2019, MNRAS, 488, 5075, doi: 10.1093/mnras/stz2115

    Moscardini, L. 2019, MNRAS, 488, 5075, doi: 10.1093/mnras/stz2115

    work page doi:10.1093/mnras/stz2115 2019

  36. [36]

    2025a, MNRAS, 538, 114, doi: 10.1093/mnras/staf305

    Song, Y., Gong, Y., Xiong, Q., et al. 2025a, MNRAS, 538, 114, doi: 10.1093/mnras/staf305

    work page doi:10.1093/mnras/staf305

  37. [37]

    2025b, MNRAS, 540, 2853, doi: 10.1093/mnras/staf918

    Song, Y., Gong, Y., Zhou, X., et al. 2025b, MNRAS, 540, 2853, doi: 10.1093/mnras/staf918

    work page doi:10.1093/mnras/staf918

  38. [38]

    2024a, MNRAS, 534, 128, doi: 10.1093/mnras/stae2094 —

    Song, Y., Xiong, Q., Gong, Y., et al. 2024a, MNRAS, 534, 128, doi: 10.1093/mnras/stae2094 —. 2024b, ApJ, 976, 244, doi: 10.3847/1538-4357/ad8de9 —. 2024c, MNRAS, 532, 1049, doi: 10.1093/mnras/stae1575

    work page doi:10.1093/mnras/stae2094

  39. [39]

    CSST large-scale structure analysis pipeline: IV. Cosmic Voids Identified from Galaxy Group Samples as Probes of the Large-scale Structure

    Song, Y., Yang, X., Gong, Y., et al. 2026, arXiv e-prints, arXiv:2606.01907, doi: 10.48550/arXiv.2606.01907

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2606.01907 2026

  40. [40]

    and Weinberg, David H

    Strauss, M. A., Weinberg, D. H., Lupton, R. H., et al. 2002, AJ, 124, 1810, doi: 10.1086/342343

    work page doi:10.1086/342343 2002

  41. [41]

    M., Lavaux, G., Hamaus, N., et al

    Sutter, P. M., Lavaux, G., Hamaus, N., et al. 2015, Astronomy and Computing, 9, 1, doi: 10.1016/j.ascom.2014.10.002 12Y.Song et al

    work page doi:10.1016/j.ascom.2014.10.002 2015

  42. [42]

    2024, ApJ, 969, 89, doi: 10.3847/1538-4357/ad434e

    Thiele, L., Massara, E., Pisani, A., et al. 2024, ApJ, 969, 89, doi: 10.3847/1538-4357/ad434e

    work page doi:10.3847/1538-4357/ad434e 2024

  43. [43]

    2024, JCAP, 2024, 079, doi: 10.1088/1475-7516/2024/10/079

    Matarrese, S. 2024, JCAP, 2024, 079, doi: 10.1088/1475-7516/2024/10/079

    work page doi:10.1088/1475-7516/2024/10/079 2024

  44. [44]

    2025, ApJ, 993, 227, doi: 10.3847/1538-4357/ae07d9

    Verza, G., Degni, G., Pisani, A., et al. 2025, ApJ, 993, 227, doi: 10.3847/1538-4357/ae07d9

    work page doi:10.3847/1538-4357/ae07d9 2025

  45. [45]

    A., & BenZvi, S

    Veyrat, D., Douglass, K. A., & BenZvi, S. 2023, ApJ, 958, 59, doi: 10.3847/1538-4357/acf4f5

    work page doi:10.3847/1538-4357/acf4f5 2023

  46. [46]

    2023, JCAP, 2023, 010, doi: 10.1088/1475-7516/2023/08/010

    Baccigalupi, C. 2023, JCAP, 2023, 010, doi: 10.1088/1475-7516/2023/08/010

    work page doi:10.1088/1475-7516/2023/08/010 2023

  47. [47]

    Y., & Pisani, A

    Wang, B. Y., & Pisani, A. 2024, ApJL, 970, L32, doi: 10.3847/2041-8213/ad5ffe

    work page doi:10.3847/2041-8213/ad5ffe 2024

  48. [48]

    Wandelt, B. D. 2023, ApJ, 955, 131, doi: 10.3847/1538-4357/aceaf6

    work page doi:10.3847/1538-4357/aceaf6 2023

  49. [49]

    2024, ApJ, 971, 119, doi: 10.3847/1538-4357/ad5294

    Wang, Y., Yang, X., Gu, Y., et al. 2024, ApJ, 971, 119, doi: 10.3847/1538-4357/ad5294

    work page doi:10.3847/1538-4357/ad5294 2024

  50. [50]

    2026, in preparation

    Wang, Y., Li, Q., Gu, Y., et al. 2026, in preparation

    work page 2026

  51. [51]

    J., et al

    Woodfinden, A., Nadathur, S., Percival, W. J., et al. 2022, MNRAS, 516, 4307, doi: 10.1093/mnras/stac2475

    work page doi:10.1093/mnras/stac2475 2022

  52. [52]

    Constraining Neutrino Mass with the Void Weak Lensing Effect

    Xu, W., Zhao, C., Su, C., Shan, H., & Liu, Y. 2026, arXiv e-prints, arXiv:2603.09278, doi: 10.48550/arXiv.2603.09278

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2603.09278 2026

  53. [53]

    A., & BenZvi, S

    Zaidouni, F., Veyrat, D., Douglass, K. A., & BenZvi, S. 2025, ApJ, 978, 3, doi: 10.3847/1538-4357/ad9585

    work page doi:10.3847/1538-4357/ad9585 2025

  54. [54]

    Robustness of cosmic void statistics: insights from SDSS DR7 and the ELUCID simulation

    Zhang, Y., Yang, X., Guo, H., Wang, P., & Shi, F. 2026, arXiv e-prints, arXiv:2603.29706, doi: 10.48550/arXiv.2603.29706

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2603.29706 2026

  55. [55]

    2022, MNRAS, 511, 5492, doi: 10.1093/mnras/stac390

    Zhao, C., Variu, A., He, M., et al. 2022, MNRAS, 511, 5492, doi: 10.1093/mnras/stac390

    work page doi:10.1093/mnras/stac390 2022

This paper was first reviewed by glm-5.2 on July 9, 2026.