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Density Profiles of TNG300 Voids across Cosmic Time

T0 review · 3 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Inside cosmic voids in the TNG300 simulation, galaxies trace dark matter linearly at every redshift from z=0 to z=3, with a slope that rises from about 1.22 to about 2.51 and matches the clustering bias.

desk verdict Solid measurement paper extending void density profiles and the galaxy-dark matter linear relation to z=3, but the headline claim that b_slope matches TNG bias at all redshifts is only tested at z=0; fix that overreach and it is publishable. read the letter →

arxiv 2504.15902 v1 pith:M22XS3XJ submitted 2025-04-22 astro-ph.GA astro-ph.CO

Olivia Curtis , Bryanne McDonough , Tereasa Brainerd This is my paper · ORCID
classification astro-ph.GAastro-ph.CO
keywords cosmicvoidsvoiddensityprofileslineargalaxybiasTNG300simulationdarkmatterdistributiontracershierarchyredshiftevolution
topics Dark Matter
open problems Dark Matter
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 asks how accurately galaxies trace the underlying dark matter distribution inside cosmic voids, and whether that relation changes over cosmic time. It identifies the largest significant voids in eleven snapshots of the TNG300 simulation, from z=3 to z=0, and measures radial density contrasts using both galaxies and dark matter particles. The central result is that the galaxy and dark matter density contrasts are linearly related at every redshift, with a slope that increases from about 1.22 at z=0 to about 2.51 at z=3 and closely tracks the simulation's clustering bias. If true, this means the linear-bias assumption, previously tested at low redshift, remains valid inside voids to z=3, and galaxy-only void catalogs could in principle be calibrated to infer the underlying dark matter distribution.

What carries the argument

The argument is carried by ZOBOV, a watershed-based void finder that builds a Voronoi tessellation of the galaxy field and grows zones outward from density minima without assuming void shape, with a Poisson-noise significance cut that keeps roughly 100-200 large voids per snapshot. Radial density contrast profiles, defined as $\delta(r)=n(r)/\bar{n}-1$, are measured for galaxies and dark matter in concentric shells around the emptiest sphere in each void. The linear fit $\delta_{\rm gal}=b_{\rm slope}\,\delta_{\rm DM}+c_{\rm offset}$ then converts the two profiles into a single number per snapshot, $b_{\rm slope}$, which is compared with scale-dependent clustering bias estimates. Integrated profiles and a hierarchy split at $3R_{\rm eff}$ define the void-in-void and void-in-cloud populations that account for the environmental differences.

What would settle it

Repeat the void finding and profile measurement in several independent simulation boxes of comparable or larger volume with identical tracer cuts; if $b_{\rm slope}$ at z=3 does not come out near 2.5, or if the z=0.2 to z=0 ridge decline vanishes, the claimed redshift evolution is sample variance rather than a cosmic signal.

Watch

Extended reading notes

Core claim

The central discovery is that the interior of a void empties of dark matter over time while the galaxy distribution stays almost fixed: dark matter density contrasts in void centers fall from about -0.42 at z=3 to -0.82 at z=0, while galaxy centers remain nearly empty, around -0.98, at all redshifts. Ridges behave oppositely, with dark matter ridges growing more overdense over time and galaxy ridges fluctuating without a strong trend. At all redshifts the radial profiles of galaxies and dark matter are linearly related out to about 1.2 void radii, and the slope of that relation rises steadily with redshift and is comparable to the simulation's published clustering bias. The paper also separates voids into voids-in-voids and voids-in-clouds and shows that their dark matter ridge evolution differs: dark matter accumulates in the ridges of voids-in-clouds but drains from the ridges of voids-in-voids, while galaxy ridges are similar.

Load-bearing premise

The argument assumes that one TNG300-sized box, with only about 100 to 200 significant voids per snapshot, is a representative sample, so snapshot-to-snapshot profile changes reflect cosmic evolution rather than cosmic variance or sparse tracer counts.

Editorial extensions

If this is right

  • Galaxy-only void catalogs can be used to estimate the linear bias at void scales at any redshift between z=0 and z=3, since $b_{\rm slope}$ reproduces the simulation's bias values.
  • The nearly constant galaxy interior density contrast implies little net galaxy flow into or out of voids over cosmic time.
  • The dark matter interior density contrast is a sensitive clock of void evolution: its drop from about -0.42 to about -0.82 traces the draining of mass onto ridges.
  • Void environment matters for tracer bias: in voids-in-clouds dark matter ridges grow denser with time, while in voids-in-voids they become relatively less dense, so a single galaxy-dark matter bias does not describe all voids at intermediate to high redshift.
  • If Lambda-CDM is correct, deep future void surveys should see the same redshift dependence in the galaxy-dark matter relation.

Reading between the lines

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

  • Because $b_{\rm slope}$ rises even between snapshots where the tracer population is nearly the same, part of the trend may be physical bias evolution, but the simultaneous drop in tracer number density with redshift means selection effects and bias evolution are entangled in the reported slopes.
  • If the linear relation holds, weak-lensing measurements of voids at z~0.5-1 could be inverted to recover the dark matter profile from galaxy counts, effectively using voids as calibrated dark matter tracers.
  • The late-time ridge decline and outward shift of the maximum-density sphere between z=0.2 and z=0 hint at void ridge expansion; a larger-volume simulation with several hundred voids per snapshot could test whether this is real or small-sample noise.
  • Comparing slopes at fixed tracer number density across redshift, rather than at fixed stellar-mass cuts, would isolate the redshift evolution of bias from tracer selection; the paper's comparison to fixed-density bias estimates suggests this is feasible.
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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 identifies voids in the galaxy distributions of eleven snapshots of the TNG300 simulation, spanning redshifts 0 ≤ z ≤ 3, using the ZOBOV watershed algorithm as implemented in REVOLVER. It presents stacked radial density profiles of these voids as traced by both galaxies and dark matter particles, finding inverse top-hat shapes at all redshifts. The dark matter profiles evolve significantly: void centers become more underdense (from δ ≈ -0.42 at z = 3 to δ ≈ -0.82 at z = 0) and ridges become more overdense, while galaxy profiles remain nearly unchanged. The paper reports a linear relationship between galaxy and dark matter density contrasts within voids, with slope b_slope increasing from about 1.22 at z = 0 to about 2.51 at z = 3, and interprets this slope as a measure of linear galaxy bias. It also divides voids into 'void-in-void' and 'void-in-cloud' populations based on the integrated density contrast at 3 R_eff and examines how the profiles differ between these environments.

Significance. The paper extends the Pollina et al. (2017) finding—that the galaxy-dark matter density contrast relation inside voids has a slope close to the linear bias—to z = 3, using the publicly available TNG300 simulation and the public REVOLVER void finder. Strengths include transparent methodology, explicit alternative stellar mass cuts, large redshift coverage, and a clear presentation of profile evolution. If the b_slope-bias identification is confirmed at all redshifts, the result would be a valuable demonstration that void profiles can serve as a probe of linear bias over cosmic time. However, the quantitative support for this interpretation currently rests on a single z = 0 comparison, and the highest-redshift point uses an inconsistent tracer threshold in void identification, so the central claim is plausible but not yet fully established.

major comments (3)
  1. [Section 5.1 and Abstract] The claim that b_slope is 'similar to the bias estimates for TNG300 snapshots' is only quantitatively tested at z = 0.0. The comparison with Springel et al. (2018) quotes b(k) = 1.17 and 1.38 at k = 0.067 h/Mpc for z = 0 only; no bias values are quoted or compared for any of the ten higher-redshift snapshots. Because b_slope rises monotonically from 1.22 at z = 0 to 2.51 at z = 3, the abstract's claim is a testable extrapolation. The authors should either extract and compare the corresponding Springel et al. (2018) bias values at each snapshot (matching tracer number densities), compute the bias directly from TNG300 galaxy clustering at each redshift, or explicitly limit the claim to z = 0 and discuss the higher-redshift behavior as qualitatively consistent with expectations. Without this, the central interpretation is unsupported beyond z = 0.
  2. [Section 2 and Table 2] The z = 3 snapshot uses a lower stellar mass threshold (10^7.75 h^-1 M_sun) than the 10^8 h^-1 M_sun used at all other redshifts for the tracer population with which voids are identified. This introduces a discontinuity in void selection at the highest redshift. Although Table 2 shows the increasing b_slope trend persists for two uniform higher mass cuts (≥4.66×10^8 and ≥1.54×10^10 h^-1 M_sun), those slopes are measured within a void catalog defined with the lower-threshold tracer population, so the void sample itself is not matched across redshift. The authors should test the sensitivity of the b_slope evolution to the void-finding tracer, for example by re-running ZOBOV with a uniform mass cut at all snapshots (or at least at z = 3), or by demonstrating that b_slope is insensitive to the void catalog definition.
  3. [Sections 2 and 5.1] The quoted uncertainties on b_slope (as small as ±0.01 for the original mass cut) are derived from standard errors of the mean density profiles and linear fits that treat radial bins as independent. However, the ~100-200 voids per snapshot are not independent, and the radial bins of the stacked profile are strongly correlated. No estimate of cosmic variance or sample variance is provided. The paper acknowledges the small TNG300 volume but does not quantify its impact on the b_slope evolution or the bias comparison. A jackknife over sub-boxes, or a comparison with an independent simulation box, would yield more realistic uncertainties; without such an estimate, the statistical significance of the claimed redshift trend is unclear.
minor comments (4)
  1. [Section 5.1, paragraph after Eq. (4)] The text states 'b_slope = 1.22 ± 0.4', but Table 2 reports the uncertainty as 0.04; this is a typo that should be corrected to '1.22 ± 0.04'.
  2. [Table 2, z = 1.0 row] For the M* ≥ 1.54 × 10^10 h^-1 M_sun column at z = 1.0, the reported c_offset is '2.46 ± 0.03', which is inconsistent with all other c_offset values being near zero; this appears to be a typo, likely for '0.02 ± 0.03'.
  3. [Section 5.2, text near Figure 8] The sentence 'blue points and blue lines show profiles for the "voids-in-clouds"' should read 'voids-in-voids' to match the figure caption and the surrounding text.
  4. [Section 2] The statement that TNG300 'encompasses a co-moving volume of 2053 h^-3 Mpc^3' is ambiguous; it should read '205^3 h^-3 Mpc^3' or '(205 h^-1 Mpc)^3'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: b_slope is an empirical fitted slope cross-checked against an independent z=0 bias measurement; the high-redshift bias claim is an extrapolation, not a circularity.

full rationale

The paper's derivation chain is self-contained: voids are identified in TNG300 galaxy fields with ZOBOV/REVOLVER, radial density contrasts are computed with Eq. (2), and Eq. (3) is fit to obtain b_slope. This slope is then compared with an external measurement (Springel et al. 2018) of clustering bias in TNG300 at z=0.0. No equation defines b_slope in terms of the bias, and no fitted parameter is renamed as a prediction; the z=0 comparison is an independent cross-check using a different estimator on the same simulation. The Curtis et al. (2024) self-citations are contextual and not load-bearing. The abstract's claim that the slope matches bias estimates at all snapshots is not quantitatively tested for z>0, but that is an unsupported extrapolation or robustness concern, not a circular reduction of the derivation to its inputs.

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

The paper's central claims rest on standard assumptions of cosmological simulations and void-finding algorithms, plus hand-chosen selection thresholds. No new physical entities are introduced. The main free parameters are the stellar mass cuts, the significance threshold, the void population boundary, and the radial fitting range; the b_slope values themselves are fitted outputs rather than a priori model parameters.

free parameters (5)
  • Stellar mass threshold (original cut) = 10^8 h^-1 Msun (z<3); 10^7.75 h^-1 Msun (z=3)
    Chosen by hand in Section 2 to balance galaxy counts and resolution; these thresholds define the tracer populations used for void finding and affect b_slope.
  • Alternative stellar mass thresholds = 4.66e8 h^-1 Msun and 1.54e10 h^-1 Msun at all z
    Used in Section 5.1 to compare with Springel et al. (2018) bias measurements; changing the threshold changes tracer density, void sizes, and the fitted slope.
  • Void significance threshold P(r) = 4.55e-2 (2 sigma)
    Adopted in Section 3 from Neyrinck (2008) to reject Poisson-noise voids; this threshold selects which underdense zones enter the final catalogs.
  • Void-in-void and void-in-cloud boundary = Integrated galaxy density contrast = 0 at r = 3 R_eff
    Used in Section 5.2 to split void populations, following Sheth and van de Weygaert (2004); the exact choice affects the relative sizes of the two populations.
  • Radial fitting range for b_slope = 0.1 to 1.2 R_eff
    Used in Section 5.1 for the linear fit in Equation (3); restricting the range to interior and ridge scales sets which parts of the profile determine the slope.
assumptions (5)
  • domain assumption The Lambda CDM cosmology with Planck 2016 parameters (Omega_m=0.3089, Omega_b=0.0486, sigma8=0.8159, ns=0.9667, h=0.6774) underlies the TNG300 simulation.
    The simulation used for all results assumes this cosmology; if the cosmological parameters were substantially different, the growth of voids and the bias evolution would differ.
  • domain assumption ZOBOV watershed zones correspond to physical cosmic voids.
    The paper identifies voids using ZOBOV in Section 3; this is a standard method but it assumes watershed basins in the galaxy density field trace true matter underdensities.
  • domain assumption Galaxies trace the matter distribution with a linear bias, delta_gal = b_slope delta_DM + c_offset.
    Equation (3) in Section 5.1 posits this relation; the paper tests it empirically but does not derive it from first principles, and it is the basis for interpreting b_slope as the clustering bias.
  • domain assumption TNG300 particle sampling is sufficient to measure dark matter density contrasts in void interiors and ridges.
    The dark matter profiles rely on counting simulation particles in radial shells; resolution limits, especially at high redshift, could bias the inferred density contrasts.
  • standard math The Neyrinck (2008) P(r) statistic, calibrated from Monte Carlo Poisson samples, estimates the probability that an underdense region arises from Poisson noise.
    Used in Section 3 to select significant voids; the formula and its interpretation are taken from cited literature and are not re-derived here.

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Cite this review

Pith. "Pith review of Density Profiles of TNG300 Voids across Cosmic Time." pith.science (2026). https://pith.science/paper/M22XS3XJ

@misc{pith2026250415902,
  author       = {Pith},
  title        = {Pith review of: Density Profiles of TNG300 Voids across Cosmic Time},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M22XS3XJ}},
  note         = {Machine review of arXiv:2504.15902}
}
abstract

We present radial density profiles, as traced by luminous galaxies and dark matter particles, for voids in eleven snapshots of the \texttt{TNG300} simulation. The snapshots span 11.65~Gyr of cosmic time, corresponding to the redshift range $0 \le z \le 3$. Using the comoving galaxy fields, voids were identified via a well-tested, watershed transformation-based algorithm. Voids were defined to be underdense regions that are unlikely to have arisen from Poisson noise, resulting in the selection of $\sim100-200$ of the largest underdense regions in each snapshot. At all redshifts, the radial density profiles as traced by both the galaxies and the dark matter resemble inverse top-hat functions. However, details of the functions (particularly the underdensities of the innermost regions and the overdensities of the ridges) evolve considerably more for the dark matter density profiles than for the galaxy density profiles. At all redshifts, a linear relationship between the galaxy and dark matter density profiles exists, and the slope of the relationship is similar to the bias estimates for \texttt{TNG300} snapshots. Lastly, we identify distinct environments in which voids can exist, defining ``void-in-void" and ``void-in-cloud" populations (i.e., voids that reside in larger underdense or overdense regions, respectively) and we investigate ways in which the relative densities of dark matter and galaxies in the interiors and ridges of these structures vary as a function of void environment.

Figures

Figures reproduced from arXiv: 2504.15902 by the authors.

Figure 1
Figure 1. Distributions of void effective radii, 𝑟v, for each snapshot. Error bars are calculated from 10,000 bootstrap resamplings of the data. The average number of significant voids per snapshot is 147 ± 11. For the most part, the total number of significant voids increases over time. At the earliest redshifts, there is an increase from 70 significant voids at 𝑧 = 3.0 to 123 at 𝑧 = 1.5, then at lower redshifts ∼ 145 − 175 … view at source ↗
Figure 2
Figure 2. shows the median values of 𝑅eff as function of redshift and lookback time. The median effective void radius decreases from ∼ 20ℎ −1Mpc at 𝑧 = 3.0 to ∼ 15ℎ −1Mpc at 𝑧 = 1.0, where it remains for the rest of the snapshots. As discussed above, the fact that, on average, voids at earlier redshifts are systematically larger than they are at lower redshifts is due to the fact that there are fewer galaxies in the earliest … view at source ↗
Figure 3
Figure 3. Density contrast profiles (top) and integrated density profiles (bottom) for all significant voids in the 𝑧 = 0.0 snapshot. Panel a): dark matter density contrast profiles (dashed purple lines). Panel c): integrated dark matter density profiles for the “void-in-cloud” (dashed orange lines, see text) and “void-in-void” (dot-dashed blue lines) populations. Panel b): galaxy density contrast profiles (green). Panel d) i… view at source ↗
Figures from the paper (8 more)
Figure 4
Figure 4. Figure 4: Average radial number density profiles of TNG300 voids across time. Green diamonds: galaxies are used as density tracers. Purple circles: dark matter particles are used as density tracers. Error bars: standard error of the mean density profile. Shaded regions: middle 6…
Figure 5
Figure 5. Figure 5: Panels a) and c) show the average maximum and minimum density contrast of all void profiles over time when galaxies (diamonds) and dark matter particles (circles) are used as tracers of the density contrast. Panels b) and d) show the ratios between the corresponding po…
Figure 6
Figure 6. Figure 6: Panel a): the centers of the radial shells at which 𝛿 = 𝛿max for the radial density profiles when galaxies (diamonds) and dark matter particles (circles) are used as tracers. Vertical bars show the bin widths used when creating [PITH_FULL_IMAGE:figures/full_fig_p019_6.png]
Figure 7
Figure 7. Figure 7: Void galaxy density contrasts vs. dark matter density contrasts in each snapshot. Points: average galaxy and dark matter radial density contrasts in spherical shells with radii between 0.1 − 1.2𝑅eff. Minimum density contrasts correspond to void centers; maximum density…
Figure 8
Figure 8. Figure 8: Integrated density profiles for “voids-in-voids,” “voids-in-clouds,” and the entire void population when galaxies (points) and dark matter particles (lines) are used as density tracers. Error bars and shaded regions show the standard error of the mean dark matter and g…
Figure 9
Figure 9. Figure 9: Same as [PITH_FULL_IMAGE:figures/full_fig_p027_9.png]
Figure 10
Figure 10. Figure 10: Same as [PITH_FULL_IMAGE:figures/full_fig_p028_10.png]
Figure 11
Figure 11. Figure 11: Same as [PITH_FULL_IMAGE:figures/full_fig_p030_11.png]

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Why Cosmic Voids Matter: Pristine Evolution

    astro-ph.CO 2025-09 conditional novelty 7.0 of 10

    Cosmic voids traced by halos become stable at late times, and the matter around them evolves linearly, supporting their use as clean dark-energy probes.

  2. Multi-tracer mass bias in matched cosmic voids from SDSS DR7 and the ELUCID constrained simulation

    astro-ph.CO 2026-08 conditional novelty 6.0 of 10

    Galaxy and subhalo mass fractions relative to dark matter decrease towards the centres of matched cosmic voids, with galaxy-to-subhalo ratios limited by the scarcity of massive subhaloes.

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