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HETDEX [OII] galaxies at $z \le 0.48$: Volume-limited samples and their power spectra

T0 review · 0 major / 6 minor · reviewed 2026-07-13 · grok-4.5

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Pith's one-line read Volume-limited HETDEX [OII] samples at z≤0.48 match flat ΛCDM mocks, with host halo masses log(M0)≈11.9–12.3 that rise weakly with luminosity.

desk verdict Solid first clustering release of high-density HETDEX [OII] volume-limited samples that match Planck-ΛCDM mocks; foundation paper, not a growth-rate result yet. read the letter →

arxiv 2607.08453 v2 pith:VVK6UNIR submitted 2026-07-09 astro-ph.CO astro-ph.GA

Jeongin Moon , Eiichiro Komatsu , Robin Ciardullo , Olivia Curtis , Dustin Davis , Daniel J. Farrow , Karl Gebhardt , Caryl Gronwall
show 10 more authors
Laura Herold Gary J. Hill Donghui Jeong Chenxu Liu Maja Lujan Niemeyer Erin Mentuch Cooper Shiro Mukae Shun Saito Ariel G. Sánchez Donald P. Schneider
This is my paper
classification astro-ph.COastro-ph.GA
keywords HETDEX[OII]galaxiesvolume-limitedsamplespowerspectrummultipoleshalooccupationdistributionlarge-scalestructureredshift-spacedistortionslow-redshiftcosmology
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

This paper turns HETDEX’s untargeted emission-line catalog into clean, volume-limited samples of [OII] galaxies in three luminosity bins across its Spring and Fall fields. The samples are unusually dense—five to ten times denser than typical emission-line galaxy surveys—and sit at low redshift where late-time structure growth is most visible. Their monopole and quadrupole power spectra match mock spectra drawn from a large N-body simulation under a flat ΛCDM cosmology fixed by Planck parameters, across the full measured range of scales. The amplitudes imply that the galaxies live in dark-matter halos of characteristic mass log(M0)≈11.9–12.3, with a mild luminosity trend M0∝La (a=0.37±0.10) and roughly 13 percent of the galaxies sitting in subhalos. The result supplies high-density, well-characterized tracers that can be used immediately for growth-rate, galaxy–halo, and cross-correlation studies in the low-redshift universe.

What carries the argument

A single-parameter log-normal halo occupation distribution (central occupation ∝ exp[−(log Mh/M0)2/(2σlogM2)], σlogM fixed at 0.6 and Fg set to match the observed number density) applied to Uchuu halo and subhalo catalogs; the free parameter M0 is fit to the measured monopole via a Sellentin–Heavens likelihood.

What would settle it

A re-fit of the same monopoles with σlogM left free (or with a standard step-function plus satellite HOD) that yields statistically unacceptable residuals, or a direct measurement of the satellite fraction that differs significantly from the mock value of ~13 percent.

Watch

Extended reading notes

Core claim

The monopole and quadrupole power spectra of three volume-limited HETDEX [OII] samples agree with Uchuu-based mocks under flat ΛCDM Planck parameters at all wavenumbers 0.01<k<0.7 h Mpc−1. The power-spectrum amplitudes are consistent with a characteristic host-halo mass log(M0 [h−1M⊙])≈11.9–12.3 that scales weakly with [OII] luminosity as M0∝La with a=0.37±0.10; the best-fit mocks place about 13 percent of the galaxies in subhalos.

Load-bearing premise

The model freezes the width of the log-normal occupation function at 0.6 by hand and uses the remaining free parameter only to match number density, so the claim that this simple form is enough rests on that fixed width remaining adequate for every luminosity bin and redshift.

Editorial extensions

If this is right

  • The same samples can be used for forthcoming redshift-space-distortion analyses that constrain the late-time growth rate without photometric pre-selection systematics.
  • Linear bias values b1~0.8–0.9 are now available for three luminosity bins and can be inserted into halo-model or cross-correlation forecasts with weak lensing and CMB lensing.
  • The high number densities make the catalogs competitive for void statistics and for cross-correlations with external low-redshift probes.
  • The measured M0–L slope supplies a concrete target for semi-analytic or hydrodynamical models of star-forming galaxies at z≤0.48.

Reading between the lines

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

  • Because luminosity and redshift are correlated by construction in volume-limited bins, any future growth-rate measurement will need an explicit joint model of luminosity evolution and time evolution to avoid absorbing one into the other.
  • The success of a pure log-normal HOD without a high-mass step function already suggests that [OII] selection at these redshifts is closer to a star-formation-rate threshold than to a stellar-mass threshold; that distinction can be tested by stacking the same galaxies on continuum mass estimates.
  • If the ~13 percent subhalo fraction holds under more flexible HODs, satellite kinematics will contribute a non-negligible fraction of the small-scale quadrupole and should be forward-modelled rather than treated as a free nuisance.
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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

0 major / 6 minor

Summary. This paper constructs volume-limited samples of emission-line-selected [OII] galaxies from HETDEX PDR1 at z ≤ 0.48 in three luminosity bins across the Spring and Fall fields (N_gal from ~11k to ~65k; n-bar ≃ (2–5) imes10^{-3} h^3 Mpc^{-3}). Using a CIC-assigned FFT Yamamoto estimator with shot-noise subtraction, it measures monopole and quadrupole power spectra that agree with Uchuu mocks (Planck flat ΛCDM) at all 0.01 < k < 0.7 h Mpc^{-1}. A log-normal HOD (Eq. 11) with fixed σ_logM = 0.6 and F_g set to match n-bar yields best-fit characteristic halo masses log(M_0 [h^{-1} M_⊙]) ≃ 11.9–12.3 and a weak luminosity slope M_0 ∝ L^a with a = 0.37 ± 0.10; the mocks further imply ~13% of galaxies occupy subhalos. The samples are presented as the foundation for forthcoming RSD, galaxy–halo, and cross-correlation analyses.

Significance. The work supplies a unique high-density, untargeted spectroscopic tracer set in the low-redshift regime most sensitive to late-time growth and dark-energy effects, complementary to continuum-selected surveys (GAMA, DESI BGS). Strengths that raise include the public HPSC2 catalog, open power-spectrum pipeline, KS-validated volume-limited cuts, Sellentin–Heavens likelihood accounting for finite mocks (N_s = 50), high PTEs for both multipoles, and the fact that the quadrupole is a genuine prediction (not fitted). The demonstration that a minimal HOD already reproduces the data to k = 0.7 h Mpc^{-1} makes the samples immediately usable for cosmology and galaxy-formation studies.

minor comments (6)
  1. Abstract/title and several headings contain residual LaTeX spacing artifacts (e.g., “V olume-limited”, “z≤0.48”). A global clean-up of the compiled PDF is needed before final production.
  2. Section 4.2 / Eq. (11): the choice σ_logM = 0.6 is stated as “neither too large nor too small”; a one-sentence justification referencing prior ELG HOD literature (or a brief sensitivity plot already performed for ±0.1) would help readers assess robustness without re-running the mocks.
  3. Section 5.2: the conversion of M_0 to linear bias b_1 ≃ 0.8–0.9 cites the halo-model reviews but does not specify the exact mass function or bias fitting formula used. Adding the reference (or a short appendix formula) would make the numbers fully reproducible.
  4. Figure 5 and Appendix A: the N_overlap sky maps and the quantitative effect of the weight on P_0(k) at high k are useful; a single sentence in the main text noting that the weight changes the monopole amplitude by only a few percent would improve accessibility.
  5. Table 1: the effective redshifts and volumes are clear, but a column (or footnote) listing the exact KS p-values obtained for the adopted (z_min, z_max) pairs would document the volume-limited criterion more transparently.
  6. Section 4.2: Spring Bin 4 uses 21 slightly overlapping sub-boxes (9 % of the simulation volume). A brief remark that the resulting covariance is therefore mildly underestimated (or a test with non-overlapping boxes) would complete the error-budget discussion.

Circularity Check

1 steps flagged · score 2.0 of 10

Ordinary HOD amplitude fit of M0; monopole amplitude is matched by construction while shape, quadrupole, and external Planck/Uchuu cosmology remain independent tests.

  1. fitted input called prediction [Abstract; Section 5.2 Results; Eq. (11) and surrounding text]
    "We find that the power spectrum amplitudes are consistent with a characteristic dark matter halo mass of log(M0 [h−1M⊙])≃11.9–12.3, with the halo mass showing a weak dependence on [OII] luminosity, M0∝La, increasing with a slope of a=0.37±0.10. ... By fixing σlogM=0.6 and the galaxy fraction, Fg, to match the observed number density of each sample, we reduced the HOD to have a single free parameter: the characteristic halo mass M0."

    M0 is the sole free parameter adjusted to the monopole amplitudes of each luminosity bin; the quoted log(M0) values and the power-law slope a fitted to those values are therefore the direct numerical output of the fit, not an independent prediction. (The spectral shape across all k and the unused quadrupole remain non-circular consistency checks.)

full rationale

The paper's central results are volume-limited sample construction from HETDEX PDR1, FFT power-spectrum multipoles, and comparison to Uchuu mocks under fixed external Planck 2015 flat-ΛCDM parameters. The only free HOD parameter M0 is fitted solely to the monopole amplitude (Sellentin-Heavens likelihood on 7 k-bins); Fg is set to match the observed number density and σ_logM is fixed by hand at 0.6 (with a ±0.1 robustness check). Consequently the reported log(M0) values and the subsequent power-law slope a = 0.37 ± 0.10 are direct outputs of that fit rather than independent predictions. This is the ordinary level of any HOD amplitude analysis and does not rise to definitional circularity: the full k-dependence of the monopole, the quadrupole (never used in the fit), the subhalo fraction, and the agreement with an external N-body cosmology are genuine, non-forced tests. No self-definitional loop, no load-bearing self-citation uniqueness theorem, and no smuggled ansatz appear. Score 2 reflects only the mild fitted-input reporting of halo masses.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central clustering and halo-mass claims rest on standard flat-ΛCDM cosmology taken from Planck, the Uchuu N-body realization, a simplified log-normal HOD with one free mass scale, and the assumption that the KS-validated luminosity-redshift windows produce unbiased volume-limited samples. No new physical entities are introduced; the free parameters are the per-bin M0 values and the hand-fixed HOD width.

free parameters (3)
  • M0 (characteristic halo mass per luminosity bin) = log(M0/h−1M⊙) ≈ 11.94–12.32 depending on bin/field
    Single free parameter of the HOD; fitted to the monopole power spectrum amplitude via Sellentin-Heavens likelihood for each of the six samples.
  • σ_logM (HOD width) = 0.6 (fixed)
    Fixed by hand to 0.6 (varied ±0.1 only as a robustness check); not fitted to the data.
  • Fg (galaxy fraction) = 0.37–0.64 depending on bin
    Set solely to reproduce the observed number density of each sample once M0 and σ_logM are chosen; not an independent free parameter of the clustering fit.
assumptions (4)
  • domain assumption Flat ΛCDM cosmology with Planck 2015/2018 parameters (Ωm=0.3089, σ8=0.8159, etc.) correctly describes the matter power spectrum at z≤0.48.
    Uchuu mocks and distance-redshift conversion both adopt these parameters (Sections 1 and 4.2).
  • domain assumption A log-normal central occupation (Geach et al. 2012 form without the error-function term) plus subhalo population is an adequate description of [OII] galaxy occupation.
    Adopted in Section 4.2; justified a posteriori by the quality of the multipole fits.
  • ad hoc to paper Two-sample KS p>0.05 between data and random redshift distributions guarantees a sufficiently volume-limited sample for clustering analysis.
    Threshold chosen in Section 3.1 to define the redshift windows of each luminosity bin.
  • domain assumption Sparse IFU sampling (fill factor ~1/4.6) does not introduce significant window-function bias once the grid cell is larger than the IFU separation.
    Cited from Chiang et al. (2013) and used to set H=2.2 h−1 Mpc (Section 4.1).

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Cite this review

Pith. "Pith review of HETDEX [OII] galaxies at $z \le 0.48$: Volume-limited samples and their power spectra." pith.science (2026). https://pith.science/paper/VVK6UNIR

@misc{pith2026260708453,
  author       = {Pith},
  title        = {Pith review of: HETDEX [OII] galaxies at $z \le 0.48$: Volume-limited samples and their power spectra},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VVK6UNIR}},
  note         = {Machine review of arXiv:2607.08453}
}
abstract

The catalog from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) Public Data Release 1 (PDR1) contains half a million emission-line-selected [OII] galaxies spread across $540~\mathrm{deg}^2$ at $z \le 0.48$ from HETDEX's unprecedented untargeted spectroscopic survey. In this paper, we construct volume-limited samples from PDR1 in three luminosity bins across the two main fields: "Spring'' and "Fall''. The numbers of galaxies in the bins range from 11,354 to 64,794 and number densities, $\bar{n}\simeq (2-5)\times10^{-3}~h^3~\mathrm{Mpc}^{-3}$, are higher than those of typical cosmological spectroscopic surveys of emission-line galaxies by a factor of five to ten. The monopole and quadrupole power spectra derived from these samples are in excellent agreement with the mock power spectra from the Uchuu simulation based on a flat $\Lambda$CDM model and the cosmological parameters from the Planck cosmic microwave background data, at all wavenumbers used for the measurement ($0.01<k<0.7~h~\mathrm{Mpc}^{-1}$). We find that the power spectrum amplitudes are consistent with a characteristic dark matter halo mass of $\log(M_0~[h^{-1}M_{\odot}])\simeq 11.9$-$12.3$, with the halo mass showing a weak dependence on [OII] luminosity, $M_0\propto L^a$, increasing with a slope of $a = 0.37\pm0.10$. The best-fit mock suggests that approximately 13 percent of the [OII] galaxies in our sample reside in subhalos. The new, high-density tracers of the underlying matter distribution presented in this paper provide precise measurements of clustering in a low-redshift regime sensitive to the late-time growth of structures. These samples will form the basis for forthcoming analyses of the redshift-space distortion effect, galaxy-halo connection, and cross-correlations with external low-redshift probes.

Figures

Figures reproduced from arXiv: 2607.08453 by the authors.

Figure 1
Figure 1. Projected distributions of the HETDEX [O II] galaxies from HPSC2 in the plane of right ascension and comoving distance in units of h −1 Gpc with corresponding redshifts, collapsed along the declination axis with a width of 2◦ . The large-scale structure is clearly visible across all observational fields. There are 286,992 and 140,122 galaxies with z hetdex > 0 in comoving volumes of 4.7 × 107 and 2.6 × 107 h −3 Mpc3… view at source ↗
Figure 3
Figure 3. The number of [O II] emitting galaxies per bin of ∆z = 0.01 in the Spring (top) and Fall (bottom) fields. The black line represents all [O II] emitting galaxies in HPSC2, while the colored lines and shaded regions indicate the num￾ber and the applied redshift cuts for different luminosity bins used to extract volume-limited samples. Bins 1 (red) and 5 (purple) are excluded from the cosmological analysis and thus the… view at source ↗
Figure 4
Figure 4. Same format as [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figures from the paper (13 more)
Figure 5
Figure 5. Figure 5: Sky distribution of the observed and mock [O II] volume-limited samples in the Spring and Fall fields, projected onto the right ascension–comoving distance plane by collapsing along the declination axis with a width of 2◦ . Comoving distances in units of h −1 Gpc are i…
Figure 6
Figure 6. Figure 6: Monopole power spectra of HETDEX [O II] volume-limited samples in Bins 2 (left panels), 3 (middle panels), and 4 (right panels) in the Spring (top panels) and Fall (bottom panels) fields. The points with the error bars show the HETDEX data binned with a width ∆k = 0.1 …
Figure 7
Figure 7. Figure 7: Same format as [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 8
Figure 8. Figure 8: Parabolic fits to χ 2 SH(M) = −2 ln LSH [Equation (14)] estimating the best-fit log characteristic halo mass, M = log(M0), where M0 is in units of h −1M⊙, from Bins 2 (left panels), 3 (middle panels), and 4 (right panels) in the Spring (top panels) and Fall (bottom pan…
Figure 9
Figure 9. Figure 9: Distribution of χ 2 [Equation (12)] of the monopole power spectra calculated from the mock realizations with the best-fit value of M for Bins 2 (left panels), 3 (middle panels), and 4 (right panels) in the Spring (top panels) and Fall (bottom panels) fields. The histog…
Figure 10
Figure 10. Figure 10: Same format as [PITH_FULL_IMAGE:figures/full_fig_p015_10.png]
Figure 11
Figure 11. Figure 11: Monopole power spectra of the HETDEX [O II] volume-limited samples in Bins 2 (left panels), 3 (middle panels), and 4 (right panels) in the Spring (top panels) and Fall (bottom panels) fields, compared with the best-fit Uchuu mock. The solid lines represent the HETDEX …
Figure 12
Figure 12. Figure 12: Same format as [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]
Figure 13
Figure 13. Figure 13: presents the best-fit values of log(M0) as a function of log(L) and their power-law fits, M0 ∝ L a . We find that the best-fit characteristic halo mass slightly increases with luminosity. Although the Spring field shows a steeper slope than the Fall field, both are st…
Figure 14
Figure 14. Figure 14: Same format as [PITH_FULL_IMAGE:figures/full_fig_p017_14.png]
Figure 15
Figure 15. Figure 15: Sky distribution of Noverlap for Spring Bin 4 and Fall Bin 4 in the panel of right ascension and declination, where the size of the circles reflects the Noverlap value. The total number of galaxies corresponding to each Noverlap, NNoverlap , is indicated in the legend…
Figure 16
Figure 16. Figure 16: Monopole (left) and quadrupole (right) power spectra for Spring Bin 4 (top) and Fall Bin 4 (bottom), comparing results with (solid lines) and without (dashed lines) the weight, w(r) = N −1 overlap. 0.1 0.2 0.3 0.4 0.5 0.6 0.7 k [h Mpc 1 ] 100 200 300 400 500 600 kP 0(…
Figure 17
Figure 17. Figure 17: Monopole (left) and quadrupole (right) power spectra for the best-fit HOD mocks of Spring Bin 4, excluding galaxies populated in dark matter subhalos (dashed lines with shaded areas), compared to the data (points with error bars) [PITH_FULL_IMAGE:figures/full_fig_p02…

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Works this paper leans on

75 extracted references · 3 linked inside Pith

  1. [1]

    F., Aboubrahim, A., et al

    Abdalla, E., Abell´ an, G. F., Aboubrahim, A., et al. 2022, Journal of High Energy Astrophysics, 34, 49, doi: 10.1016/j.jheap.2022.04.002 Abdul Karim, M., Aguilar, J., Ahlen, S., et al. 2025, PhRvD, 112, 083515, doi: 10.1103/tr6y-kpc6

    doi:10.1016/j.jheap.2022.04.002 2022

  2. [2]

    Hopkins, A. M. 2021a, MNRAS, 503, 59, doi: 10.1093/mnras/stab409

    doi:10.1093/mnras/stab409

  3. [3]

    2020, MNRAS, 497, 581, doi: 10.1093/mnras/staa1956

    Comparat, J. 2020, MNRAS, 497, 581, doi: 10.1093/mnras/staa1956

    doi:10.1093/mnras/staa1956 2020

  4. [4]

    2017, MNRAS, 470, 2617, doi: 10.1093/mnras/stx721

    Alam, S., Ata, M., Bailey, S., et al. 2017, MNRAS, 470, 2617, doi: 10.1093/mnras/stx721

    doi:10.1093/mnras/stx721 2017

  5. [5]

    2021b, PhRvD, 103, 083533, doi: 10.1103/PhysRevD.103.083533 20

    Alam, S., Aubert, M., Avila, S., et al. 2021b, PhRvD, 103, 083533, doi: 10.1103/PhysRevD.103.083533 20

    doi:10.1103/physrevd.103.083533

  6. [6]

    Anderson, T. W. 2003, An Introduction to Multivariate Statistical Analysis, 3rd edn. (Hoboken, NJ: Wiley-Interscience)

    2003

  7. [7]

    J., & Heymans, C

    Asgari, M., Mead, A. J., & Heymans, C. 2023, The Open Journal of Astrophysics, 6, 39, doi: 10.21105/astro.2303.08752

    arXiv doi:10.21105/astro.2303.08752 2023

  8. [8]

    2023, MNRAS, 519, 1648, doi: 10.1093/mnras/stac3514

    Aung, H., Nagai, D., Klypin, A., et al. 2023, MNRAS, 519, 1648, doi: 10.1093/mnras/stac3514

    doi:10.1093/mnras/stac3514 2023

Show all 75 references
  1. [9]

    G., et al

    Avila, S., Gonzalez-Perez, V., Mohammad, F. G., et al. 2020, MNRAS, 499, 5486, doi: 10.1093/mnras/staa2951

    2020 doi

  2. [10]

    H., Hearin, A

    Behroozi, P., Wechsler, R. H., Hearin, A. P., & Conroy, C. 2019, MNRAS, 488, 3143, doi: 10.1093/mnras/stz1182

    2019 doi

  3. [11]

    S., Conroy, C., & Wechsler, R

    Behroozi, P. S., Conroy, C., & Wechsler, R. H. 2010, ApJ, 717, 379, doi: 10.1088/0004-637X/717/1/379

    2010 doi

  4. [12]

    Bianchi, D., Gil-Mar´ ın, H., Ruggeri, R., & Percival, W. J. 2015, MNRAS, 453, L11, doi: 10.1093/mnrasl/slv090

    2015 doi

  5. [13]

    A., Beutler, F., et al

    Blake, C., Kazin, E. A., Beutler, F., et al. 2011, MNRAS, 418, 1707, doi: 10.1111/j.1365-2966.2011.19592.x

    2011 doi

  6. [14]

    2013, JCAP, 2013, 030, doi: 10.1088/1475-7516/2013/12/030

    Chiang, C.-T., Wullstein, P., Jeong, D., et al. 2013, JCAP, 2013, 030, doi: 10.1088/1475-7516/2013/12/030

    2013 doi

  7. [15]

    J., et al

    Ciardullo, R., Gronwall, C., Adams, J. J., et al. 2013, ApJ, 769, 83, doi: 10.1088/0004-637X/769/1/83

    2013 doi

  8. [16]

    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

    2026 doi

  9. [17]

    2002, PhR, 372, 1, doi: 10.1016/S0370-1573(02)00276-4

    Cooray, A., & Sheth, R. 2002, PhR, 372, 1, doi: 10.1016/S0370-1573(02)00276-4

    2002 doi

  10. [18]

    2023, PhRvD, 108, 123519, doi: 10.1103/PhysRevD.108.123519

    Dalal, R., Li, X., Nicola, A., et al. 2023, PhRvD, 108, 123519, doi: 10.1103/PhysRevD.108.123519

    2023 doi

  11. [19]

    M., et al

    Davis, D., Gebhardt, K., Cooper, E. M., et al. 2023, ApJ, 946, 86, doi: 10.3847/1538-4357/acb0ca de Mattia, A., Ruhlmann-Kleider, V., Raichoor, A., et al. 2021, MNRAS, 501, 5616, doi: 10.1093/mnras/staa3891

    2023 doi

  12. [20]

    2007, ApJL, 671, L101, doi: 10.1086/524950 DES Collaboration, Abbott, T

    Deng, X.-F., He, J.-Z., & Jiang, P. 2007, ApJL, 671, L101, doi: 10.1086/524950 DES Collaboration, Abbott, T. M. C., Aguena, M., et al. 2026, arXiv e-prints, arXiv:2602.10065, doi: 10.48550/arXiv.2602.10065

    2007 doi

  13. [21]

    2018, Physics Reports, 733, 1, doi: 10.1016/j.physrep.2017.12.002 Dong-P´ aez, C

    Desjacques, V., Jeong, D., & Schmidt, F. 2018, Physics Reports, 733, 1, doi: 10.1016/j.physrep.2017.12.002 Dong-P´ aez, C. A., Smith, A., Szewciw, A. O., et al. 2024, MNRAS, 528, 7236, doi: 10.1093/mnras/stae062

    2018 doi

  14. [22]

    P., Hill, D

    Driver, S. P., Hill, D. T., Kelvin, L. S., et al. 2011, MNRAS, 413, 971, doi: 10.1111/j.1365-2966.2010.18188.x Euclid Collaboration, Mellier, Y., Abdurro’uf, et al. 2025, A&A, 697, A1, doi: 10.1051/0004-6361/202450810

    2011 doi

  15. [23]

    J., Cole, S., Norberg, P., et al

    Farrow, D. J., Cole, S., Norberg, P., et al. 2015, MNRAS, 454, 2120, doi: 10.1093/mnras/stv2075

    2015 doi

  16. [24]

    2026, ApJ, 1002, 90, doi: 10.3847/1538-4357/ae592a

    Favole, G., Kitaura, F.-S., Hadzhiyska, B., et al. 2026, ApJ, 1002, 90, doi: 10.3847/1538-4357/ae592a

    2026 doi

  17. [25]

    A., Comparat, J., et al

    Favole, G., Rodr´ ıguez-Torres, S. A., Comparat, J., et al. 2017, MNRAS, 472, 550, doi: 10.1093/mnras/stx1980

    2017 doi

  18. [26]

    2016, MNRAS, 461, 3421, doi: 10.1093/mnras/stw1483

    Favole, G., Comparat, J., Prada, F., et al. 2016, MNRAS, 461, 3421, doi: 10.1093/mnras/stw1483

    2016 doi

  19. [27]

    A., Kaiser, N., & Peacock, J

    Feldman, H. A., Kaiser, N., & Peacock, J. A. 1994, ApJ, 426, 23, doi: 10.1086/174036

    1994 doi

  20. [28]

    P., Gui, S., et al

    Gao, H., Jing, Y. P., Gui, S., et al. 2023, ApJ, 954, 207, doi: 10.3847/1538-4357/ace90a

    2023 doi

  21. [29]

    E., Sobral, D., Hickox, R

    Geach, J. E., Sobral, D., Hickox, R. C., et al. 2012, MNRAS, 426, 679, doi: 10.1111/j.1365-2966.2012.21725.x

    2012 doi

  22. [30]

    2021, ApJ, 923, 217, doi: 10.3847/1538-4357/ac2e03

    Gebhardt, K., Mentuch Cooper, E., Ciardullo, R., et al. 2021, ApJ, 923, 217, doi: 10.3847/1538-4357/ac2e03

    2021 doi

  23. [31]

    2018, MNRAS, 474, 4024, doi: 10.1093/mnras/stx2807

    Gonzalez-Perez, V., Comparat, J., Norberg, P., et al. 2018, MNRAS, 474, 4024, doi: 10.1093/mnras/stx2807

    2018 doi

  24. [32]

    J., Ruiz-Macias, O., et al

    Hahn, C., Wilson, M. J., Ruiz-Macias, O., et al. 2023, AJ, 165, 253, doi: 10.3847/1538-3881/accff8

    2023 doi

  25. [33]

    2017, JCAP, 2017, 002, doi: 10.1088/1475-7516/2017/07/002

    Hand, N., Li, Y., Slepian, Z., & Seljak, U. 2017, JCAP, 2017, 002, doi: 10.1088/1475-7516/2017/07/002

    2017 doi

  26. [34]

    J., Lee, H., MacQueen, P

    Hill, G. J., Lee, H., MacQueen, P. J., et al. 2021, AJ, 162, 298, doi: 10.3847/1538-3881/ac2c02

    2021 doi

  27. [35]

    W., & Eastwood, J

    Hockney, R. W., & Eastwood, J. W. 1988, Computer simulation using particles (CRC Press)

    1988

  28. [36]

    A., et al

    Ishiyama, T., Prada, F., Klypin, A. A., et al. 2021, MNRAS, 506, 4210, doi: 10.1093/mnras/stab1755

    2021 doi

  29. [37]

    1997, ApJ, 484, 560, doi: 10.1086/304372 Jim´ enez, E., Padilla, N., Contreras, S., et al

    Jain, B., & Seljak, U. 1997, ApJ, 484, 560, doi: 10.1086/304372 Jim´ enez, E., Padilla, N., Contreras, S., et al. 2021, MNRAS, 506, 3155, doi: 10.1093/mnras/stab1819

    1997 doi

  30. [38]

    Jing, Y. P. 2005, ApJ, 620, 559, doi: 10.1086/427087

    2005 doi

  31. [39]

    2013, ApJ, 768, 51, doi: 10.1088/0004-637X/768/1/51

    Kajisawa, M., Shioya, Y., Aida, Y., et al. 2013, ApJ, 768, 51, doi: 10.1088/0004-637X/768/1/51

    2013 doi

  32. [40]

    C., & Evans, N

    Kennicutt, R. C., & Evans, N. J. 2012, ARA&A, 50, 531, doi: 10.1146/annurev-astro-081811-125610

    2012 doi

  33. [41]

    Kennicutt, Jr., R. C. 1998, ApJ, 498, 541, doi: 10.1086/305588

    1998 doi

  34. [42]

    A., Sobral, D., Mobasher, B., et al

    Khostovan, A. A., Sobral, D., Mobasher, B., et al. 2018, MNRAS, 478, 2999, doi: 10.1093/mnras/sty925

    2018 doi

  35. [43]

    V., Berlind, A

    Kravtsov, A. V., Berlind, A. A., Wechsler, R. H., et al. 2004, ApJ, 609, 35, doi: 10.1086/420959

    2004 doi

  36. [44]

    K., Driver, S

    Liske, J., Baldry, I. K., Driver, S. P., et al. 2015, MNRAS, 452, 2087, doi: 10.1093/mnras/stv1436 Mentuch Cooper, E., Gebhardt, K., Davis, D., et al. 2023, ApJ, 943, 177, doi: 10.3847/1538-4357/aca962 Mentuch Cooper, E., Gebhardt, K., Davis, D., et al. 2026, ApJS, 284, 67, do...

    2015 doi

  37. [45]

    J., & White, S

    Mo, H. J., & White, S. D. M. 1996, MNRAS, 282, 347, doi: 10.1093/mnras/282.2.347

    1996 doi

  38. [46]

    2023, PhRvL, 131, 111001, doi: 10.1103/PhysRevLett.131.111001 21

    Nguyen, N.-M., Huterer, D., & Wen, Y. 2023, PhRvL, 131, 111001, doi: 10.1103/PhysRevLett.131.111001 21

    2023 doi

  39. [47]

    M., Hawkins, E., et al

    Norberg, P., Baugh, C. M., Hawkins, E., et al. 2001, MNRAS, 328, 64, doi: 10.1046/j.1365-8711.2001.04839.x

    2001 doi

  40. [48]

    2021, PASJ, 73, 1186, doi: 10.1093/pasj/psab068

    Okumura, T., Hayashi, M., Chiu, I.-N., et al. 2021, PASJ, 73, 1186, doi: 10.1093/pasj/psab068

    2021 doi

  41. [49]

    2023, MNRAS, 525, 3879, doi: 10.1093/mnras/stad2401

    Oogi, T., Ishiyama, T., Prada, F., et al. 2023, MNRAS, 525, 3879, doi: 10.1093/mnras/stad2401

    2023 doi

  42. [50]

    2025, A&A, 697, A226, doi: 10.1051/0004-6361/202453086

    Chaves-Montero, J. 2025, A&A, 697, A226, doi: 10.1051/0004-6361/202453086

    2025 doi

  43. [51]

    E., Contreras, S., et al

    Ortega-Martinez, S., Angulo, R. E., Contreras, S., et al. 2026, arXiv e-prints, arXiv:2604.19449, doi: 10.48550/arXiv.2604.19449

    2026 doi arXiv

  44. [52]

    2023, MNRAS, 519, 1771, doi: 10.1093/mnras/stac3582

    Osato, K., & Okumura, T. 2023, MNRAS, 519, 1771, doi: 10.1093/mnras/stac3582

    2023 doi

  45. [53]

    P., et al

    Pakmor, R., Springel, V., Coles, J. P., et al. 2023, MNRAS, 524, 2539, doi: 10.1093/mnras/stad2027

    2023 doi

  46. [54]

    J., Friedrich, O., Sellentin, E., & Heavens, A

    Percival, W. J., Friedrich, O., Sellentin, E., & Heavens, A. 2022, MNRAS, 510, 3207, doi: 10.1093/mnras/stab3540

    2022 doi

  47. [55]

    Philcox, O. H. E., & Ivanov, M. M. 2022, PhRvD, 105, 043517, doi: 10.1103/PhysRevD.105.043517 Planck Collaboration, Ade, P. A. R., Aghanim, N., et al. 2016, A&A, 594, A13, doi: 10.1051/0004-6361/201525830 Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 641, A6...

    2022 doi

  48. [56]

    2023, arXiv e-prints, arXiv:2304.11911, doi: 10.48550/arXiv.2304.11911

    Prada, F., Behroozi, P., Ishiyama, T., Klypin, A., & P´ erez, E. 2023, arXiv e-prints, arXiv:2304.11911, doi: 10.48550/arXiv.2304.11911

    2023 doi arXiv

  49. [57]

    J., Hang, Q., Farren, G., et al

    Qu, F. J., Hang, Q., Farren, G., et al. 2025, PhRvD, 111, 103503, doi: 10.1103/PhysRevD.111.103503

    2025 doi

  50. [58]

    W., Adams, M

    Ramsey, L. W., Adams, M. T., Barnes, T. G., et al. 1998, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 3352, Advanced Technology Optical/IR Telescopes VI, ed. L. M. Stepp, 34–42, doi: 10.1117/12.319287

    1998 doi

  51. [59]

    2025, MNRAS, 539, 3627, doi: 10.1093/mnras/staf700

    Said, K., Howlett, C., Davis, T., et al. 2025, MNRAS, 539, 3627, doi: 10.1093/mnras/staf700

    2025 doi

  52. [60]

    2015, PhRvD, 92, 083532, doi: 10.1103/PhysRevD.92.083532

    Scoccimarro, R. 2015, PhRvD, 92, 083532, doi: 10.1103/PhysRevD.92.083532

    2015 doi

  53. [61]

    Sellentin, E., & Heavens, A. F. 2016, MNRAS, 456, L132, doi: 10.1093/mnrasl/slv190

    2016 doi

  54. [62]

    Springel, V., White, S. D. M., Jenkins, A., et al. 2005, Nature, 435, 629, doi: 10.1038/nature03597

    2005 doi

  55. [63]

    2012, MNRAS, 423, 2617, doi: 10.1111/j.1365-2966.2012.21063.x

    Tadaki, K.-i., Kodama, T., Ota, K., et al. 2012, MNRAS, 423, 2617, doi: 10.1111/j.1365-2966.2012.21063.x

    2012 doi

  56. [64]

    S., Chiba, M., et al

    Takada, M., Ellis, R. S., Chiba, M., et al. 2014, PASJ, 66, R1, doi: 10.1093/pasj/pst019

    2014 doi

  57. [65]

    R., Strauss, M

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

    2004 doi

  58. [66]

    2014, A&A, 566, A1, doi: 10.1051/0004-6361/201423585

    Tempel, E., Tamm, A., Gramann, M., et al. 2014, A&A, 566, A1, doi: 10.1051/0004-6361/201423585

    2014 doi

  59. [67]

    L., Robertson, B

    Tinker, J. L., Robertson, B. E., Kravtsov, A. V., et al. 2010, ApJ, 724, 878, doi: 10.1088/0004-637X/724/2/878

    2010 doi

  60. [68]

    Vale, A., & Ostriker, J. P. 2004, MNRAS, 353, 189, doi: 10.1111/j.1365-2966.2004.08059.x

    2004 doi

  61. [69]

    2024, JCAP, 2024, 044, doi: 10.1088/1475-7516/2024/09/044

    Wang, Y., & Yu, Y. 2024, JCAP, 2024, 044, doi: 10.1088/1475-7516/2024/09/044

    2024 doi

  62. [70]

    2022, ApJ, 928, 1, doi: 10.3847/1538-4357/ac4973

    Wang, Y., Zhai, Z., Alavi, A., et al. 2022, ApJ, 928, 1, doi: 10.3847/1538-4357/ac4973

    2022 doi

  63. [71]

    H., St¨ olzner, B., Asgari, M., et al

    Wright, A. H., St¨ olzner, B., Asgari, M., et al. 2025, A&A, 703, A158, doi: 10.1051/0004-6361/202554908

    2025 doi

  64. [72]

    A., & Nishioka, H

    Yamamoto, K., Nakamichi, M., Kamino, A., Bassett, B. A., & Nishioka, H. 2006, PASJ, 58, 93, doi: 10.1093/pasj/58.1.93

    2006 doi

  65. [73]

    H., Wang, Y., et al

    Yuan, S., Wechsler, R. H., Wang, Y., et al. 2025, MNRAS, 538, 1216, doi: 10.1093/mnras/staf368

    2025 doi

  66. [74]

    R., Frieman, J

    Zehavi, I., Blanton, M. R., Frieman, J. A., et al. 2002, ApJ, 571, 172, doi: 10.1086/339893

    2002 doi

  67. [75]

    A., Weinberg, D

    Zheng, Z., Berlind, A. A., Weinberg, D. H., et al. 2005, ApJ, 633, 791, doi: 10.1086/466510 22 160180200220240 R.A.[deg] 46 48 50 52 54 56Dec.[deg] N1 = 59195 N2 = 4562 N3 = 461 N4 = 42 N5 = 13 5101520253035 R.A.[deg] 2 1 0 1 2 Dec.[deg] N1 = 25386 N2 = 1885 N3 = 129 N4 = 3 Fi...

    2005 doi

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