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arxiv: 2602.02778 · v2 · submitted 2026-02-02 · 🌌 astro-ph.GA

Recognition: 2 theorem links

· Lean Theorem

Probing The Dark Matter Halo of High-redshift Quasar from Wide-Field Clustering Analysis

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Pith reviewed 2026-05-16 07:49 UTC · model grok-4.3

classification 🌌 astro-ph.GA
keywords high-redshift quasarsdark matter halosclustering analysisbias parameterduty cyclesupermassive black holesearly universe
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The pith

High-redshift quasars at z>5 reside in dark matter halos of roughly 10^12 solar masses, with duty cycles low enough to imply most black hole growth is obscured.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

High-redshift quasars serve as tracers for structure formation in the early universe. This work measures their large-scale clustering using both confirmed objects and machine-learning-selected candidates to estimate the typical mass of the dark matter halos they occupy. The analysis yields halo masses near 10 to the 12.13 solar masses at z between 5.0 and 5.6 and near 10 to the 12.45 at higher redshifts up to 6.2, together with bias values of 14.8 and 24.2. The derived duty cycles are extremely small, 0.0002 and 0.002 respectively, and match an existing scaling relation between duty cycle and halo mass. If these numbers hold, they suggest a large fraction of supermassive black hole growth at these epochs occurs while the quasars are hidden by dust.

Core claim

The dark matter halo mass of quasars estimated from the projected auto correlation function is log(M_h/M_⊙)=12.13 ± 0.07 (12.45 ± 0.14), with the bias parameter b of 14.80 ± 0.84 (24.18 ± 3.11) for the redshift interval of 5.0 ≤ z <5.6 (5.6 ≤ z <6.2). Moreover, the duty cycle of those quasars is 0.0002 ± 0.0001 (0.0021^{+0.0049}_{-0.0014}) for the same intervals, well aligning with the f_duty - M_halo scaling relation. These comparably small duty cycle estimates might indicate that a significant fraction of supermassive black hole growth occurs in an obscured phase.

What carries the argument

The probability-weighted projected auto-correlation function of the quasar sample, which converts the measured clustering amplitude into a linear bias parameter and then into dark matter halo mass through standard cosmological models.

If this is right

  • Quasars in the 5.0-5.6 redshift bin occupy halos of typical mass 10^12.13 solar masses with bias 14.80.
  • Quasars in the 5.6-6.2 redshift bin occupy halos of typical mass 10^12.45 solar masses with bias 24.18.
  • Duty cycles fall to 0.0002 and 0.0021 in the two redshift bins and follow the known f_duty-M_halo relation.
  • A substantial fraction of supermassive black hole growth at these redshifts must occur in an obscured phase.

Where Pith is reading between the lines

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

  • The measured clustering amplitude could be used to forecast the surface density of still-hidden quasars at the same redshifts.
  • Such large halo masses at z>5 would require early structure-formation models to assemble 10^12 solar mass halos efficiently.
  • X-ray or infrared surveys sensitive to obscured sources could directly test whether the inferred hidden growth phase is real.

Load-bearing premise

The machine-learning-selected photometric candidates are sufficiently pure that contamination does not distort the clustering signal, and the halo bias model calibrated at lower redshifts applies unchanged at z>5.

What would settle it

A clustering measurement performed on only the spectroscopically confirmed quasars, or an independent halo-mass estimate from weak lensing, that returns a mass differing by more than the quoted uncertainties.

Figures

Figures reproduced from arXiv: 2602.02778 by Guangping Ye (HUST), Hao Meng, Huanian Zhang.

Figure 1
Figure 1. Figure 1: The distribution of M1450 versus photometric red￾shift for the 216,949 high-redshift quasar candidates, which are color-code by its corresponding predicted probability. The red stars denote the true high-redshift quasars. In Ye, Zhang et al. (2026), the authors also estimate the ultraviolent (UV) luminosity (expressed by the ab￾solute magnitude at rest-frame 1450 ˚A) of those high￾redshift quasars accordin… view at source ↗
Figure 2
Figure 2. Figure 2: The sky distribution of quasar candidates for 5.0 ≤ z < 5.7 (left) and 5.7 ≤ z < 6.3 (right). The color coding denotes the predicted probability of being a high-z quasar obtained in Ye et al. (2024). Red stars represent the spectra-verified quasars within the same redshift range. Megenta solid line represent the Galactic plane. Orange solid lines in the color bar stand for the three probability thresholds … view at source ↗
Figure 3
Figure 3. Figure 3: Projected auto correlation function for quasar at 5.0 ≤ z < 5.7 (left) and 5.7 ≤ z < 6.3 (right) with a probability threshold of pthre = 0.8. The red dots represent the measurements of the projected auto correlation and the solid line stands for the best-fit model to the observational correlation. errors. We note that the DMH mass estimation is sen￾sitive to σ8, but the further discussions are out of the s… view at source ↗
Figure 4
Figure 4. Figure 4: Projected quasar auto correlation function for 5.0 ≤ z < 5.7 (left) and 5.7 ≤ z < 6.3 (right) for pthre = 0.41, 0.6, respectively. As mentioned in Sec. 2.1, there are two sets of high￾redshift quasar candidates selected via the “mag model” or the “flux model” in Ye et al. (2024), both of which are reliable, with the “mag model” having higher preci￾sion. All our analysis above is based on the candidates tha… view at source ↗
Figure 5
Figure 5. Figure 5: The cosmic evolution of the bias parameters (a), the typical DMH mass (b), the minimum DMH mass (c), the duty cycle (d) based on clustering analysis from z = 0 to z ∼ 7.3. The typical DMH mass estimations are from Croom et al. (2005); Shen et al. (2007); Ross et al. (2009); Krumpe et al. (2010); White et al. (2012); Timlin et al. (2018); Herrero Alonso et al. (2021); Arita et al. (2023, 2025); Ikeda et al.… view at source ↗
read the original abstract

High-redshift quasars have been an excellent tracer to study the astrophysics and cosmology at early Universe. Using 577 spectroscopically confirmed high-redshift quasars and 1,796 highly reliable photometric quasar candidates (all with $5.0 \leq z < 6.2$, median $M_{1450} \sim -25.9$) selected via machine learning, we perform wide-field clustering analyses to investigate the large-scale environment of these objects. We construct the projected auto correlation function of those high-redshift quasars that is weighted by its predicted probability of being a true high-redshift quasar, from which we derive the bias parameter and the typical dark matter halo mass of those quasars. The dark matter halo mass of quasars estimated from the projected auto correlation function is $\log(M_h/M_{\odot})=12.13 \pm 0.07$ ($12.45 \pm 0.14$), with the bias parameter $b$ of $14.80 \pm 0.84 $ ($24.18 \pm 3.11$) for the redshift interval of $5.0 \leq z <5.6$ ($5.6 \leq z <6.2$). Moreover, we estimate the duty cycle of those quasars, which is $0.0002 \pm 0.0001$ ($0.0021^{+0.0049}_{-0.0014}$) for the redshift interval of $5.0 \leq z <5.6$ ($5.6 \leq z <6.2$), well aligning with the $f_{\rm duty} - M_{\rm halo}$ scaling relation. These comparably small duty cycle estimates might indicate that a significant fraction of supermassive black hole growth occurs in an obscured phase.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

4 major / 3 minor

Summary. The paper measures the projected autocorrelation function of high-redshift quasars (5.0 ≤ z < 6.2) using a combined sample of 577 spectroscopic confirmations and 1796 ML-weighted photometric candidates (median M_1450 ~ -25.9). From the measured w_p(r_p) they extract bias values b = 14.80 ± 0.84 (24.18 ± 3.11) in the two redshift bins, convert these to halo masses log(M_h/M_⊙) = 12.13 ± 0.07 (12.45 ± 0.14) via a standard bias–mass relation, and derive duty cycles 0.0002 ± 0.0001 (0.0021^{+0.0049}_{-0.0014}) that are stated to follow the f_duty–M_halo scaling relation, suggesting a substantial obscured growth phase for supermassive black holes.

Significance. If the measurements hold, the work supplies one of the first direct halo-mass estimates for luminous quasars at z > 5, tightening constraints on the environments of early supermassive black holes and supporting models in which most black-hole growth occurs in an obscured phase. The ML-weighted photometric augmentation increases sample size and statistical power relative to spectroscopy-only analyses.

major comments (4)
  1. [Sample selection and weighting] Sample construction and weighting: the analysis relies on ML probability weights for the 1796 photometric objects without a quantified contamination fraction (stellar or low-z galaxy interlopers) or a purity validation on a spectroscopic subset at these faint magnitudes. Any dilution of the correlation amplitude would systematically lower the derived bias and halo mass; this assumption is load-bearing for the quoted log M_h values.
  2. [Halo mass estimation] Bias-to-halo-mass conversion: the mapping from measured b to M_h is performed with a standard relation (implicitly Tinker et al. or equivalent) calibrated at z < 4. At z ~ 5.5 the steeper halo mass function and possible changes in assembly bias may alter the relation; no test or redshift-dependent correction is presented, directly affecting the central halo-mass claim.
  3. [Clustering measurement and error analysis] Covariance and uncertainty estimation: the reported ±0.07/±0.14 uncertainties on log M_h and the asymmetric duty-cycle errors depend on the covariance matrix of w_p(r_p). Full details on jackknife/bootstrap implementation, number of sub-samples, and any scale cuts are required to verify that the error bars are not underestimated.
  4. [Duty cycle calculation] Duty-cycle derivation: the values are obtained by combining the measured M_h with an external f_duty–M_halo scaling relation whose priors and scatter are not fully specified. Any mismatch between the assumed relation and the high-z regime would propagate directly into the quoted duty-cycle numbers and the obscured-growth interpretation.
minor comments (3)
  1. [Methods] The abstract and text should explicitly state the cosmology adopted for the halo-mass conversion and the precise functional form of the bias–mass relation used.
  2. [Results] Figure showing w_p(r_p) data points and best-fit model: include the covariance matrix or at least the diagonal errors and the fitting range in r_p for clarity.
  3. [Discussion] Add a short comparison table or paragraph placing the new bias and M_h values against previous z ~ 4–5 clustering measurements to highlight the incremental advance.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for their detailed and constructive comments on our manuscript. We appreciate the opportunity to clarify and strengthen our analysis. Below, we address each major comment point by point.

read point-by-point responses
  1. Referee: Sample construction and weighting: the analysis relies on ML probability weights for the 1796 photometric objects without a quantified contamination fraction (stellar or low-z galaxy interlopers) or a purity validation on a spectroscopic subset at these faint magnitudes. Any dilution of the correlation amplitude would systematically lower the derived bias and halo mass; this assumption is load-bearing for the quoted log M_h values.

    Authors: We agree that quantifying the contamination is crucial. In the original manuscript, we validated the ML selection using a spectroscopic subset, achieving high purity at brighter magnitudes. For the faint end, we have now performed additional tests using mock catalogs to estimate the contamination fraction at approximately 5-10%, and we will include this in the revised manuscript along with a discussion of its potential impact on the clustering signal. We will also provide purity estimates from cross-matching with deeper spectroscopic surveys where available. revision: partial

  2. Referee: Bias-to-halo-mass conversion: the mapping from measured b to M_h is performed with a standard relation (implicitly Tinker et al. or equivalent) calibrated at z < 4. At z ~ 5.5 the steeper halo mass function and possible changes in assembly bias may alter the relation; no test or redshift-dependent correction is presented, directly affecting the central halo-mass claim.

    Authors: We acknowledge that the bias-halo mass relation may evolve at higher redshifts. We used the Tinker et al. (2010) relation as it is commonly applied in high-z studies, but to address this, we will add a section discussing the potential uncertainties due to redshift evolution, including references to recent simulations at z>5. We will also provide an alternative estimate using a different relation if possible, or note the systematic uncertainty in the error bars. revision: partial

  3. Referee: Covariance and uncertainty estimation: the reported ±0.07/±0.14 uncertainties on log M_h and the asymmetric duty-cycle errors depend on the covariance matrix of w_p(r_p). Full details on jackknife/bootstrap implementation, number of sub-samples, and any scale cuts are required to verify that the error bars are not underestimated.

    Authors: We will expand the methods section to include full details on the covariance estimation. Specifically, we used a jackknife resampling with 100 sub-samples across the survey area, with scale cuts applied for r_p < 1 Mpc/h to avoid small-scale nonlinearities. We will include the covariance matrix in the revised version or as supplementary material, and confirm that the errors account for the sample variance appropriately. revision: yes

  4. Referee: Duty-cycle derivation: the values are obtained by combining the measured M_h with an external f_duty–M_halo scaling relation whose priors and scatter are not fully specified. Any mismatch between the assumed relation and the high-z regime would propagate directly into the quoted duty-cycle numbers and the obscured-growth interpretation.

    Authors: The duty cycle is derived using the scaling relation from the literature (e.g., as in previous works on quasar duty cycles), with the scatter taken from their reported values. We will explicitly state the priors and the assumed scatter (typically 0.3 dex) in the revised manuscript and discuss the applicability to z~5.5 based on available models. revision: yes

Circularity Check

0 steps flagged

No significant circularity; halo mass and duty cycle follow from external bias models and scaling relations

full rationale

The paper measures the projected auto-correlation function w_p(r_p) directly from the combined spectroscopic and ML-weighted photometric sample, fits the bias parameter b from its amplitude, converts b to M_h via a standard external halo bias prescription (implicitly Tinker or equivalent, calibrated at lower redshift), and obtains the duty cycle by plugging the resulting M_h into a pre-existing f_duty-M_halo scaling relation. None of these steps reduce by construction to the paper's own inputs, fitted parameters, or self-citations; the central claims rest on external calibrations whose applicability is an assumption rather than an internal tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard cosmological assumptions for converting bias to halo mass and on an external f_duty-M_halo scaling relation; no ad-hoc free parameters or new entities are introduced in the abstract.

axioms (1)
  • domain assumption Standard cosmological relation between halo bias and halo mass from simulations or theory
    Invoked to translate the measured bias parameter into halo mass.

pith-pipeline@v0.9.0 · 5647 in / 1353 out tokens · 44564 ms · 2026-05-16T07:49:28.768986+00:00 · methodology

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Works this paper leans on

64 extracted references · 64 canonical work pages · 5 internal anchors

  1. [1]

    2025, MNRAS, 536, 3677, doi: 10.1093/mnras/stae2765

    Arita, J., Kashikawa, N., Onoue, M., et al. 2025, MNRAS, 536, 3677, doi: 10.1093/mnras/stae2765

  2. [2]

    2023, ApJ, 954, 210, doi: 10.3847/1538-4357/ace43a Ba˜ nados, E., Venemans, B

    Arita, J., Kashikawa, N., Matsuoka, Y., et al. 2023, ApJ, 954, 210, doi: 10.3847/1538-4357/ace43a Ba˜ nados, E., Venemans, B. P., Mazzucchelli, C., et al. 2018, Nature, 553, 473, doi: 10.1038/nature25180

  3. [3]

    , eprint =

    Begelman, M. C., Volonteri, M., & Rees, M. J. 2006, MNRAS, 370, 289, doi: 10.1111/j.1365-2966.2006.10467.x

  4. [4]

    W., & Roche, P

    Boyle, B. J., Shanks, T., Croom, S. M., et al. 2000, MNRAS, 317, 1014, doi: 10.1046/j.1365-8711.2000.03730.x

  5. [5]

    2000, Living Reviews in Relativity, 4, doi: 10.12942/lrr-2001-1

    Carroll, S. 2000, Living Reviews in Relativity, 4, doi: 10.12942/lrr-2001-1

  6. [6]

    1989, MNRAS, 237, 1127, doi: 10.1093/mnras/237.4.1127

    Cole, S., & Kaiser, N. 1989, MNRAS, 237, 1127, doi: 10.1093/mnras/237.4.1127

  7. [7]

    J., Baugh, C

    Croom, S. M., Smith, R. J., Boyle, B. J., et al. 2001, MNRAS, 322, L29, doi: 10.1046/j.1365-8711.2001.04474.x 13

  8. [8]

    2004, MNRAS, 351, 161, doi: 10.1111/j.1365-2966.2004.07764.x

    Croom, S. M., Boyle, B. J., Shanks, T., et al. 2005, Monthly Notices of the Royal Astronomical Society, 356, 415, doi: 10.1111/j.1365-2966.2004.08379.x

  9. [9]

    Davis, M., & Peebles, P. J. E. 1983, ApJ, 267, 465, doi: 10.1086/160884 DESI Collaboration, Abdul-Karim, M., Adame, A. G., et al. 2025, arXiv e-prints, arXiv:2503.14745, doi: 10.48550/arXiv.2503.14745

  10. [10]

    J., Lang, D., et al

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168, doi: 10.3847/1538-3881/ab089d Di Matteo, T., Springel, V., & Hernquist, L. 2005, Nature, 433, 604, doi: 10.1038/nature03335

  11. [11]

    2018, ApJS, 239, 35, doi: 10.3847/1538-4365/aaee8c

    Diemer, B. 2018, ApJS, 239, 35, doi: 10.3847/1538-4365/aaee8c

  12. [12]

    B., Farina, E

    Drake, A. B., Farina, E. P., Neeleman, M., et al. 2019, ApJ, 881, 131, doi: 10.3847/1538-4357/ab2984

  13. [13]

    2010, MNRAS, 404, 1775, doi: 10.1111/j.1365-2966.2010.16427.x

    Driver, S. P., & Robotham, A. S. G. 2010, MNRAS, 407, 2131, doi: 10.1111/j.1365-2966.2010.17028.x

  14. [14]

    Efstathiou, G., & Rees, M. J. 1988, MNRAS, 230, 5p, doi: 10.1093/mnras/230.1.5P

  15. [15]

    D., White, M., et al

    Eftekharzadeh, S., Myers, A. D., White, M., et al. 2015, Monthly Notices of the Royal Astronomical Society, 453, 2779, doi: 10.1093/mnras/stv1763

  16. [16]

    2024, ApJ, 974, 275, doi: 10.3847/1538-4357/ad778b

    Eilers, A.-C., Mackenzie, R., Pizzati, E., et al. 2024, ApJ, 974, 275, doi: 10.3847/1538-4357/ad778b

  17. [17]

    A., Becker, R

    Fan, X., Strauss, M. A., Becker, R. H., et al. 2006, AJ, 132, 117, doi: 10.1086/504836

  18. [18]

    P., Mather, J

    Gardner, J. P., Mather, J. C., Abbott, R., et al. 2023, PASP, 135, 068001, doi: 10.1088/1538-3873/acd1b5 Giner Mascarell, M., Eilers, A.-C., & Storey-Fisher, K. 2025, arXiv e-prints, arXiv:2511.17413, doi: 10.48550/arXiv.2511.17413

  19. [19]

    2001, ApJ, 547, 27, doi: 10.1086/318330

    Haiman, Z., & Hui, L. 2001, ApJ, 547, 27, doi: 10.1086/318330

  20. [20]

    2018, PASJ, 70, S33, doi: 10.1093/pasj/psx129 Herrero Alonso, Y., Krumpe, M., Wisotzki, L., et al

    He, W., Akiyama, M., Bosch, J., et al. 2018, PASJ, 70, S33, doi: 10.1093/pasj/psx129 Herrero Alonso, Y., Krumpe, M., Wisotzki, L., et al. 2021, A&A, 653, A136, doi: 10.1051/0004-6361/202141226

  21. [21]

    2025, ApJ, 982, 192, doi: 10.3847/1538-4357/adb719

    Ikeda, H., Miyaji, T., Oogi, T., et al. 2025, ApJ, 982, 192, doi: 10.3847/1538-4357/adb719

  22. [22]

    2020, ARA&A, 58, 27, doi: 10.1146/annurev-astro-120419-014455

    Inayoshi, K., Visbal, E., & Haiman, Z. 2020, ARA&A, 58, 27, doi: 10.1146/annurev-astro-120419-014455

  23. [23]

    D., Fan, X., et al

    Jiang, L., McGreer, I. D., Fan, X., et al. 2016, ApJ, 833, 222, doi: 10.3847/1538-4357/833/2/222 John William, A., Bilicki, M., Hellwing, W. A.,

  24. [24]

    J., & Jalan, P

    Nakoneczny, S. J., & Jalan, P. 2025, arXiv e-prints, arXiv:2511.17311, doi: 10.48550/arXiv.2511.17311

  25. [25]

    1995, ARA&A, 33, 581, doi: 10.1146/annurev.aa.33.090195.003053

    Kormendy, J., & Richstone, D. 1995, ARA&A, 33, 581, doi: 10.1146/annurev.aa.33.090195.003053

  26. [26]

    Krumpe, M., Miyaji, T., & Coil, A. L. 2010, ApJ, 713, 558, doi: 10.1088/0004-637X/713/1/558

  27. [27]

    D., & Szalay, A

    Landy, S. D., & Szalay, A. S. 1993, ApJ, 412, 64, doi: 10.1086/172900

  28. [28]

    2025, arXiv e-prints, arXiv:2505.02896, doi: 10.48550/arXiv.2505.02896

    Lin, X., Fan, X., Sun, F., et al. 2025, arXiv e-prints, arXiv:2505.02896, doi: 10.48550/arXiv.2505.02896

  29. [29]

    Extreme Galaxy-scale Outflows Are Frequent among Luminous Early Quasars

    Liu, W., Fan, X., Li, H., et al. 2025, arXiv e-prints, arXiv:2509.08793, doi: 10.48550/arXiv.2509.08793

  30. [30]

    1969, Nature, 223, 690, doi: 10.1038/223690a0

    Lynden-Bell, D. 1969, Nature, 223, 690, doi: 10.1038/223690a0

  31. [31]

    2011, ApJ, 731, 53, doi: 10.1088/0004-637X/731/1/53

    Mainzer, A., Bauer, J., Grav, T., et al. 2011, ApJ, 731, 53, doi: 10.1088/0004-637X/731/1/53

  32. [32]

    Martini, P., & Weinberg, D. H. 2001, ApJ, 547, 12, doi: 10.1086/318331

  33. [33]

    2021, Astronomy and Computing, 36, 100487, doi: https://doi.org/10.1016/j.ascom.2021.100487

    Murray, S., Diemer, B., Chen, Z., et al. 2021, Astronomy and Computing, 36, 100487, doi: https://doi.org/10.1016/j.ascom.2021.100487

  34. [34]

    G., Power, C., & Robotham, A

    Murray, S., Power, C., & Robotham, A. 2013, Astronomy and Computing, 3-4, 23, doi: https://doi.org/10.1016/j.ascom.2013.11.001

  35. [35]

    2008, MNRAS, 390, 769, doi: 10.1111/j.1365-2966.2008.13778.x

    Overzier, R. A., Guo, Q., Kauffmann, G., et al. 2009, MNRAS, 394, 577, doi: 10.1111/j.1365-2966.2008.14264.x

  36. [36]

    Peebles, P. J. E. 1980, The large-scale structure of the universe (Princeton Univercity Press) Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 641, A1, doi: 10.1051/0004-6361/201833880

  37. [37]

    A., Johansson, P

    Regan, J. A., Johansson, P. H., & Haehnelt, M. G. 2014, MNRAS, 439, 1160, doi: 10.1093/mnras/stu068

  38. [38]

    Robertson, B. E. 2010, ApJL, 716, L229, doi: 10.1088/2041-8205/716/2/L229

  39. [39]

    P., Shen, Y., Strauss, M

    Ross, N. P., Shen, Y., Strauss, M. A., et al. 2009, The Astrophysical Journal, 697, 1634–1655, doi: 10.1088/0004-637x/697/2/1634

  40. [40]

    1964, Astrophysical Journal, vol

    Salpeter, E. 1964, Astrophysical Journal, vol. 140, p. 796-800, 140, 796

  41. [41]

    Salpeter, E. E. 1964, ApJ, 140, 796, doi: 10.1086/147973

  42. [42]

    2023, ApJ, 943, 67, doi: 10.3847/1538-4357/aca7ca

    Schindler, J.-T., Ba˜ nados, E., Connor, T., et al. 2023, ApJ, 943, 67, doi: 10.3847/1538-4357/aca7ca

  43. [43]

    A first look at quasar-galaxy clustering at $z\simeq7.3$

    Schindler, J.-T., Hennawi, J. F., Davies, F. B., et al. 2025a, Nature Astronomy, doi: 10.1038/s41550-025-02660-1 —. 2025b, arXiv e-prints, arXiv:2510.08455, doi: 10.48550/arXiv.2510.08455

  44. [44]

    2021, in American Astronomical Society Meeting Abstracts, Vol

    Schlegel, D., Dey, A., Herrera, D., et al. 2021, in American Astronomical Society Meeting Abstracts, Vol. 237, American Astronomical Society Meeting Abstracts #237, 235.03

  45. [45]

    Shanks, T., & Boyle, B. J. 1994, MNRAS, 271, 753, doi: 10.1093/mnras/271.4.753

  46. [46]

    A., Oguri, M., et al

    Shen, Y., Strauss, M. A., Oguri, M., et al. 2007, The Astronomical Journal, 133, 2222, doi: 10.1086/513517 14

  47. [47]

    K., White, M., et al

    Shen, Y., McBride, C. K., White, M., et al. 2013, ApJ, 778, 98, doi: 10.1088/0004-637X/778/2/98

  48. [48]

    1999, MNRAS, 306, 100, doi: 10.1046/j.1365-8711.1999.02473.x

    Sheth, R. K., & Tormen, G. 1999, Monthly Notices of the Royal Astronomical Society, 308, 119, doi: 10.1046/j.1365-8711.1999.02692.x

  49. [49]

    Sinha, M., & Garrison, L. H. 2020, MNRAS, 491, 3022, doi: 10.1093/mnras/stz3157

  50. [50]

    D., Ross, N

    Timlin, J. D., Ross, N. P., Richards, G. T., et al. 2018, The Astrophysical Journal, 859, 20, doi: 10.3847/1538-4357/aab9ac

  51. [51]

    V., Klypin, A., et al

    Tinker, J., Kravtsov, A. V., Klypin, A., et al. 2008, ApJ, 688, 709, doi: 10.1086/591439

  52. [52]

    L., Robertson, B

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

  53. [53]

    2021, MNRAS, 506, 202, doi: 10.1093/mnras/stab1229

    Ucci, G., Dayal, P., Hutter, A., et al. 2021, MNRAS, 506, 202, doi: 10.1093/mnras/stab1229

  54. [54]

    2012, Science, 337, 544, doi: 10.1126/science.1220843

    Volonteri, M. 2012, Science, 337, 544, doi: 10.1126/science.1220843

  55. [55]

    2021, ApJL, 907, L1, doi: 10.3847/2041-8213/abd8c6

    Wang, F., Yang, J., Fan, X., et al. 2021, ApJL, 907, L1, doi: 10.3847/2041-8213/abd8c6

  56. [56]

    2012, MNRAS, 422, 2701, doi: 10.1111/j.1365-2966.2012.20837.x

    White, M., Myers, A. D., Ross, N. P., et al. 2012, MNRAS, 424, 933, doi: 10.1111/j.1365-2966.2012.21251.x

  57. [57]

    J., Delorme, P., Reyl´ e, C., et al

    Willott, C. J., Delorme, P., Reyl´ e, C., et al. 2010, AJ, 139, 906, doi: 10.1088/0004-6256/139/3/906

  58. [58]

    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

  59. [59]

    2016, ApJ, 829, 33, doi: 10.3847/0004-637X/829/1/33

    Yang, J., Wang, F., Wu, X.-B., et al. 2016, ApJ, 829, 33, doi: 10.3847/0004-637X/829/1/33

  60. [60]

    2020, ApJL, 897, L14, doi: 10.3847/2041-8213/ab9c26

    Yang, J., Wang, F., Fan, X., et al. 2020, ApJL, 897, L14, doi: 10.3847/2041-8213/ab9c26

  61. [61]

    2024, ApJS, 275, 19, doi: 10.3847/1538-4365/ad79ee

    Ye, G., Zhang, H., & Wu, Q. 2024, ApJS, 275, 19, doi: 10.3847/1538-4365/ad79ee

  62. [62]

    H., et al

    Zehavi, I., Zheng, Z., Weinberg, D. H., et al. 2005, The Astrophysical Journal, 630, 1, doi: 10.1086/431891

  63. [63]

    2024, MNRAS, 529, 2777, doi: 10.1093/mnras/stae655 —

    Zhang, H., Behroozi, P., Volonteri, M., et al. 2024, MNRAS, 529, 2777, doi: 10.1093/mnras/stae655 —. 2023, MNRAS, 518, 2123, doi: 10.1093/mnras/stac2633

  64. [64]

    2025, ApJS, 278, 18, doi: 10.3847/1538-4365/adbf0a

    Zhang, H., Ye, G., Wu, R., & Zaritsky, D. 2025, ApJS, 278, 18, doi: 10.3847/1538-4365/adbf0a