Beneath the Surface: >85% of z>5.9 QSOs in Massive Host Galaxies are UV-Faint

A. Gloudemans; B. Venemans; D. Yang; E. Banados; E. Farina; F. Walter; H. Algera; H. Inami; H.J.A. Rottgering; J. Hennawi

REVIEW 4 major objections 4 minor 2 cited by

Beneath the Surface: >85% of z>5.9 QSOs in Massive Host Galaxies are UV-Faint

T0 review · 4 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read More than 85% of massive-host quasars at z>5.9 are UV-faint.

desk verdict New [CII] data make a plausible case that most z>5.9 QSOs in massive hosts are UV-faint, but the headline fraction is LF-dependent and needs error bars before it is quoted as >85%. read the letter →

arxiv 2506.24128 v1 pith:VNUJGSKK submitted 2025-06-30 astro-ph.GA

R.J. Bouwens , E. Banados , R. Decarli , J. Hennawi , D. Yang , H. Algera , M. Aravena , E. Farina

show 15 more authors

A. Gloudemans J. Hodge H. Inami J. Matthee R. Meyer R.P. Naidu P. Oesch H.J.A. Rottgering S. Schouws R. Smit M. Stefanon P. van der Werf B. Venemans F. Walter Y. Fudamoto

This is my paper · ORCID

classification astro-ph.GA

keywords high-redshiftquasarsz>6[CII]158micronemissionALMAhostgalaxymassquasarUVluminosityfunctionsupermassiveblackholesQSOdemographics

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 tries to establish that the quasars we have studied at the edge of cosmic reionization are not representative of what most supermassive black holes were doing inside massive galaxies. Using the luminosity of the [CII] 158 micron line as a stand-in for host galaxy mass, the authors sort 190 quasars at z>5.9 and find that more than 85% of those in the most massive hosts are UV-faint, with only about 3% as UV-bright as the classic early-universe quasars. Earlier surveys missed this population because they selected quasars by rest-UV brightness; a new ALMA program that targeted faint quasars in [CII] is what makes the census possible. If true, the result reframes early supermassive black hole formation: the bright exemplars are outliers, and the typical active black hole in a massive early galaxy is dim in the UV but embedded in a gas-rich, massive host.

What carries the argument

The load-bearing instrument is the [CII] 158 micron line, used as a proxy for molecular gas content and hence host galaxy mass, with the threshold L[CII]>1.8e9 Lsun (roughly $10^{10}$.5 Msun of gas) defining a massive host. The analysis converts the observed L[CII] vs M_UV distribution for 190 quasars into volume densities by multiplying published z~6 quasar UV luminosity functions (Schindler et al. 2023) by the measured fraction of [CII]-luminous quasars in each UV-luminosity bin. This two-step weighting is what turns a heterogeneously targeted sample into statements about the relative abundance of UV-bright and UV-faint quasars in massive hosts.

What would settle it

Take a UV-complete, spectroscopically confirmed sample of z>5.9 quasars with M_UV,AB between -24.5 and -22, observe all of them with ALMA to a uniform [CII] depth, and measure the fraction with L[CII]>1.8e9 Lsun. If that fraction comes out substantially below 21%, the inferred >85% fraction fails; a particularly clean test is the same measurement on the faintest bin (M_UV,AB>-23.5), where the paper sees 7 massive-host quasars and the assumption about representativeness is most strained.

Watch

Extended reading notes

Core claim

The central claim is that at z>5.9, quasar UV luminosity is a poor tracer of the underlying host galaxy population. Defining massive hosts by L[CII]>1.8e9 Lsun (the median [CII] luminosity of UV-bright quasars), the paper finds 61 such systems among 190 [CII]-observed quasars, including 13 UV-faint and 7 especially UV-faint ones. From these numbers and published z~6 quasar luminosity functions, the paper infers that only ~15% of massive-host quasars are brighter than M_UV,AB=-24.5 and only ~3% are brighter than -26, so >85% are UV-faint; the volume density of UV-faint quasars at a given host mass is ~29x that of UV-bright ones. The same [CII]-luminous systems show dynamical masses and IR luminosities similar to UV-bright quasars, supporting the claim that the hosts really are comparable. The paper further argues, by extrapolating MBH and Eddington-ratio trends measured for 34 [CII]-luminous quasars, that black hole mass rather than accretion rate is the main driver of the UV spread.

Load-bearing premise

The observed fraction of quasars with a bright [CII] line in each UV-brightness bin is assumed to apply to every z>5.9 quasar in that bin; if the ALMA targets were preferentially drawn toward sources likely to show [CII] (for example, because their redshifts were already known), the 21% massive-host fraction for UV-faint quasars would be overestimated and the headline 85% would shrink.

Editorial extensions

If this is right

UV-bright z~6 quasars cannot be used alone to census supermassive black hole growth in massive galaxies; the dominant mode is UV-faint.
At fixed host mass, UV-faint quasars outnumber UV-bright ones by roughly 29 times, so models of early black hole growth must reproduce a large population of obscured or low-mass black holes in gas-rich hosts.
The median black hole mass in massive hosts at z~6 is estimated at log10(MBH/Msun)~8.1, consistent with the local MBH-host relation, making the famous UV-bright quasars ~15x more massive than typical.
The [CII] luminosity functions constructed for UV-bright and UV-faint quasars can be directly compared with [CII] surveys of galaxies to constrain quasar lifetimes and duty cycles.

Reading between the lines

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

Editorial inference: a UV-faint bias could also apply across cosmic time; if so, samples selected on rest-UV flux at any redshift will undercount the active black holes in the most massive galaxies, and the local MBH-host relation may be less evolved than bright-QSO samples suggest.
The paper's mass threshold is defined by the median [CII] luminosity of UV-bright quasars; shifting the threshold to a dynamical-mass or far-infrared-based selection would change the exact percentages, though the authors show dynamical masses are consistent across UV luminosity.
A testable prediction follows from the claim that BH mass drives UV luminosity: JWST spectroscopy of the CISTERN faint quasars should show MBH decreasing by roughly a factor of ~2 per magnitude toward fainter UV, with roughly constant Eddington ratio.
If the missing population of UV-faint, [CII]-bright quasars are dust-obscured type-1 systems, deeper mid-IR photometry (e.g., MIRI) should reveal red continuum slopes for a larger fraction than the ~25% the authors currently estimate.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Two things to know. The CISTERN data are genuinely new: they roughly quintuple [CII] coverage of UV-faint z>5.9 QSOs, and the paper uses them to build the first UV-luminosity census of QSOs in massive host galaxies across six magnitudes. The second thing is that the headline ">85% UV-faint" is a luminosity-function-weighted number, not a raw fraction. It drops below 85% if you adopt the Matsuoka et al. (2018c) LF, as the stress-test note shows.

The paper does several things well. The sample is large (190 QSOs) and spans a wide UV range. Using [CII] luminosity as a host-mass proxy is reasonable, and the authors validate it against dynamical masses and IR luminosities, which are at least partially independent. The resulting [CII] luminosity functions for UV-bright and UV-faint QSOs are a useful new product. The MBH extrapolation is long, but it is honestly labeled as preliminary and the uncertainties are stated.

The main soft spot is the missing error bars on the headline fractions. The paper quotes 3% and >85% without uncertainties, and for the alternative LFs it reports only the excess ratio, not the cumulative fraction. Reproducing the stress-test arithmetic: with the Matsuoka LF the excess is 24x rather than 29x, and that translates to a faint fraction near 83%—below the advertised >85%. This is a moderate issue, not a fatal one: every plausible LF gives a strong majority UV-faint, so the qualitative result holds.

Two other concerns are worth noting. The massive-host threshold is set to the median [CII] luminosity of the UV-bright sample, which gives the definition a whiff of circularity; however, the consistency of M_dyn and L_IR across the bins makes it a defensible, well-posed question. Second, the representativeness of CISTERN is assumed rather than demonstrated—targeting was driven by apparent brightness and known redshift—and the data are not yet public, so the 21% [CII]-luminous fraction for faint QSOs cannot be independently checked. That is a legitimate but testable concern.

Who should read this? Anyone working on z>6 SMBH demographics, QSO host galaxies, or ALMA follow-up of faint quasars. It deserves a serious referee. A referee should ask for error bars on the cumulative fractions, a direct conversion of each alternative LF into cumulative fractions, and robustness tests on the CISTERN selection. With those additions, this becomes a strong paper. My recommendation: send it to review, and require the robustness analysis before acceptance.

Referee Report

4 major / 4 minor

Summary. This paper uses new ALMA/CISTERN [CII] 158 μm observations together with archival measurements to assemble a sample of 190 z>5.9 QSOs spanning M_UV from -22 to -28. Defining a 'massive host galaxy' by L[CII] > 1.8e9 Lsun (the median [CII] luminosity of the UV-bright subsample), the authors identify 61 massive-host QSOs, including 13 that are UV-faint (M_UV,AB > -24.5). Combining the observed fraction of [CII]-luminous QSOs in each UV-luminosity bin with the Schindler et al. (2023) z~6 QSO UV luminosity function, they derive cumulative UV luminosity distributions for QSOs in massive hosts, reporting that only ~15% are brighter than M_UV=-24.5, ~3% are brighter than M_UV=-26, and hence that >85% are UV-faint. They also derive [CII] luminosity functions for UV-bright and UV-faint QSOs, finding a ~29x excess of UV-faint over UV-bright volume density at fixed host mass, and they extrapolate an M_BH-M_UV relation from 34 [CII]-luminous QSOs to estimate a median log M_BH ~8.1 for UV-faint QSOs in massive hosts at z~6.

Significance. The CISTERN program provides a major increase in [CII] coverage of UV-faint QSOs (5x and 6x in the two faint bins), and this is the first quantitative attempt to characterize the UV luminosity distribution of QSOs in massive host galaxies at z>5.9. The qualitative conclusion—that UV-bright QSOs are rare outliers among QSOs in massive hosts—is important and will influence discussions of SMBH growth at early epochs. The [CII] luminosity functions in Table 3 and Figure 4 are new and potentially useful. The main weaknesses are that the headline fractions are volume-density-weighted estimates whose uncertainties are not propagated, and the BH-mass conclusions rest on a large extrapolation with very few direct anchors. With appropriate uncertainty treatment and reframing, the results are publishable in A&A.

major comments (4)

[§3.3, Fig. 3, Table 3] The headline fractions (15%, 3%, >85%) are quoted without uncertainties, yet they are not directly measured but are obtained by multiplying the Schindler et al. (2023) UV LF by the observed [CII]-luminous fractions in five UV bins. The paper reports alternative LF determinations only as excess ratios in §3.3 (24x for Matsuoka et al. 2018c and 33x for Willott et al. 2010c), not as the corresponding cumulative UV-faint fractions. Using the Matsuoka et al. (2018c) excess of 24 together with the intermediate-to-bright ratio of ~4 read from Fig. 3 (12% intermediate vs 3% bright) gives a UV-faint fraction of ~83%, below the '>85%' headline. Please propagate the LF uncertainties, report the cumulative fractions for each adopted LF, and state the headline as a range or with a proper error bar.
[§3.3, Fig. 1] The representativeness assumption stated in §3.3—'targeting of specific z>6 QSOs was largely a function of the apparent brightness of QSOs and the spectroscopic redshift being known'—is load-bearing for the headline result, but no quantitative selection-bias test is provided. The key input is the observed massive-host fraction of 13/61 ≈ 21% for UV-faint QSOs; if CISTERN's UV-faint targets are biased toward sources with known spectroscopic redshifts or favorable [CII] detectability, this fraction, and hence the >85% result, would be overestimated. A comparison of the CISTERN targets with the parent SHELLQs/wide-area selections, or a sensitivity test that varies the assumed FWHM for non-detections, is needed to bound this effect.
[§3.2, Fig. 2] The 'massive host' threshold L[CII] > 1.8e9 Lsun is defined as the median [CII] luminosity of the UV-bright subsample, and the validation that UV-faint [CII]-bright QSOs live in similar hosts rests on Mdyn and LIR for the [CII]-detected sources only. This does not validate the L[CII]-Mgas zero point or scatter at z~6, and the paper itself cites Kaasinen et al. (2024) as a caution in §1. Since the headline fraction is a ratio of counts above this threshold, the result is sensitive to the threshold choice; a sensitivity test varying the threshold by ±0.3 dex would show how much of the '>85%' conclusion is calibration-dependent.
[§4.2, Eq. (2), Fig. 5] The conclusion that M_BH is the dominant driver of UV luminosity rests on Eq. (2), fitted to 34 [CII]-luminous QSOs that are almost all at M_UV < -25, with a single source (J1243+0100) at M_UV = -24.13; the claimed median log M_BH ~ 8.1 for M_UV ~ -23 is an extrapolation over roughly three magnitudes. The stated ±0.4 dex uncertainty reflects the extrapolation and the assumed M_UV = -22 cutoff, not direct measurements. This section should be reframed as a model-dependent extrapolation, and the '15x more massive' claim in the abstract should carry the same explicit caveat.

minor comments (4)

[Fig. 6 caption] The caption contains the duplicated phrase 'duty cycle cycle'; the sentence beginning 'As such' is also grammatically incomplete.
[Table A.1] The table header appears as 'RightM UV' and the FWHM column does not explicitly state its units; please clean up the table formatting.
[§3.3] When the alternative LFs are discussed, the text quotes only the excess ratios; please also give the resulting cumulative UV-faint fractions or explicitly state that they are not computed.
[Abstract] The abstract says 'recent QSO luminosity functions (LFs)' without naming Schindler et al. (2023); adding the citation at first use would help the reader locate the primary input.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central '>85% UV-faint' claim is an LF-weighted sum of independent [CII] measurements and published QSO UV luminosity functions, not a quantity fixed by the paper's own definitions.

full rationale

The paper's derivation chain is: (i) measure L[CII] for 190 z>5.9 QSOs; (ii) define a 'massive host' threshold at L[CII]>1.8e9 Lsun, explicitly the median L[CII] of UV-bright QSOs; (iii) measure the fraction of [CII]-luminous QSOs in five UV-luminosity bins (0.82, 0.48, 0.38, 0.28, 0.21); (iv) multiply the external Schindler et al. (2023) UV LF by these fractions to obtain the LF of massive-host QSOs; and (v) compute cumulative fractions (15% brightward of -24.5, 3% brightward of -26). No equation equates an output to an input by construction. The threshold being the UV-bright median forces roughly half of UV-bright QSOs to be 'massive', but the final 85% faint fraction is dominated by the much larger volume density of faint QSOs in the external LF combined with the observed 21% massive-host fraction in the faint bin; this is an empirical synthesis, not a definitional identity. The paper explicitly acknowledges the representativeness assumption in Sec 3.3 and quotes alternative LF results (Matsuoka et al. 2018c, Willott et al. 2010c, Matsuoka et al. 2023) with excesses of 24, 33, and 17, so the headline is model-dependent but not circular. The MBH vs MUV relation (Eq. 2) is fitted to brighter QSOs and extrapolated to fainter luminosities with stated uncertainties; this is a transparent extrapolation, not a self-justifying prediction. Self-citations to the CISTERN program paper (Bouwens et al. 2025, in prep) are not load-bearing because the present paper itself presents the CISTERN data reduction, line search, and measurements. Overall, the central claim is a measurement-based synthesis with independent external anchors (Schindler LF, Zanella et al. 2018 relation), and no circular step was found.

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

The central census and MBH distribution rest on a handful of externally calibrated relations (UV LF, [CII]-gas relation, MBH scaling) and a few choices made by the authors (threshold, extrapolation). No new physical entities are introduced.

free parameters (6)

L[CII] threshold for massive host = 1.8e9 Lsun
Set to the median [CII] luminosity of UV-bright QSOs (MUV<-26); defines the massive-host sample and directly controls the reported UV-bright/UV-faint fractions.
Assumed FWHM for non-detections = 225 km/s
Used to convert rms noise into 5-sigma L[CII] upper limits; affects which QSOs are counted as [CII]-luminous, particularly in the faint UV bins.
MBH-UV relation intercept = 9.27 dex
Best-fit intercept of log MBH vs (MUV+27) from 34 [CII]-luminous QSOs; extrapolated to MUV=-23 to derive the median MBH~10^8.1 for UV-faint QSOs.
MBH-UV relation slope = -0.26 dex/mag
Best-fit slope from the same 34 QSOs; drives the factor ~15 lower MBH at MUV=-23.
lambda_Edd-UV relation intercept = -0.21 dex
Best-fit intercept of log lambda_Edd vs (MUV+27); used to estimate the Eddington ratio distribution.
lambda_Edd-UV relation slope = -0.09 dex/mag
Best-fit slope; consistent with no trend within uncertainties.

assumptions (6)

domain assumption [CII] 158um luminosity traces host molecular gas mass and hence host galaxy mass.
Invoked throughout to classify massive hosts; based on Zanella et al. (2018), with caveat from Kaasinen et al. (2024) noted by the authors in Sec 1.
domain assumption The Schindler et al. (2023) z~6 QSO UV luminosity function accurately represents the QSO population to MUV=-22.
Used as the denominator to convert observed [CII]-luminous fractions into volume densities; any incompleteness at faint MUV biases the >85% UV-faint fraction.
domain assumption Targeting of z>5.9 QSOs for [CII] observations is independent of [CII] luminosity, depending only on UV brightness and known redshift.
Stated in Sec 3.3; if CISTERN preferentially targeted [CII]-bright systems, the 21% massive fraction in UV-faint QSOs is overestimated.
domain assumption Measured MBH-UV and lambda_Edd-UV relations for [CII]-luminous QSOs at MUV > -25 can be extrapolated to MUV ~ -23.
Underlies the MBH~10^8.1 and 15x claims; only one source at MUV=-24.1 anchors the faint end.
standard math Neeleman et al. (2021) fitting formula converts [CII] FWHM to dynamical mass.
Used in Sec 3.2 to compare host masses; assumes inclination and virialization.
domain assumption For non-detections, the [CII] line FWHM is 225 km/s when computing upper limits.
Based on median FWHM of low-L[CII] QSOs from Izumi et al. (2018, 2019); affects upper limit placement in Fig 1 and the massive fraction in faint bins.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Beneath the Surface: >85% of z>5.9 QSOs in Massive Host Galaxies are UV-Faint." pith.science (2026). https://pith.science/paper/VNUJGSKK

@misc{pith2026250624128,
  author       = {Pith},
  title        = {Pith review of: Beneath the Surface: >85% of z>5.9 QSOs in Massive Host Galaxies are UV-Faint},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VNUJGSKK}},
  note         = {Machine review of arXiv:2506.24128}
}

read the original abstract

We use [CII] observations of a large QSO sample to segregate sources by host galaxy mass, aiming to identify those in the most massive hosts. [CII] luminosity, a known tracer of molecular gas, is taken as a proxy for host mass and used to rank 190 QSOs at z>5.9, spanning a 6-mag UV luminosity range (-22<Muv<-28). Particularly valuable are ALMA data from a cycle-10 CISTERN program, providing [CII] coverage for 46 UV-faint (M_{UV,AB}>-24.5) and 25 especially UV-faint (Muv>-23.5) QSOs, improving statistics by 5x and 6x, respectively. Taking massive host galaxies to be those where L[CII]>1.8x10^9 Lsol (median L[CII] of UV-bright QSOs), we identify 61 QSOs, including 13 which are UV-faint and 7 especially UV-faint. Using these selections and recent QSO luminosity functions (LFs), we present the first characterization of UV luminosity distribution for QSOs in massive host galaxies and quantify [CII] LFs for both UV-bright and UV-faint QSOs. While ~3% of massive-host QSOs are UV-bright (Muv<-26), >~85% are UV-faint (Muv>-24.5). This wide dispersion in UV luminosities reflects variations in dust obscuration, accretion efficiency, and black hole mass. Though spectroscopy is needed for definitive conclusions, black hole mass appears to be the dominant factor driving variations in the UV luminosity, based on 34 [CII]-luminous (L[CII]>1.8x10^9 Lsol) QSOs distributed across a ~3-mag baseline in UV luminosity and with measured MBH. At Muv~-23, the median extrapolated log10 (MBH/Msol) is 8.1+/-0.4, consistent with the local relation. SMBHs in UV-bright QSOs thus appear to be ~15(-9)(+25)x more massive than typical for massive host galaxies at z~6.

Figures

Figures reproduced from arXiv: 2506.24128 by the authors.

**Figure 1.** (upper panel) Measured [CII] luminosities vs. MUV luminosities for z > 5.9 QSOs from CISTERN (red circles) and from the literature (black circles). The solid downward pointing triangles indicate 5σ upper limits on the [CII] luminosities of QSO where no line is detected with ALMA. The black stars correspond to the Fujimoto et al. (2022) and Endsley et al. (2023) QSO that were identified in deep multiwavelength data… view at source ↗

**Figure 2.** Median [CII] luminosities (left), IR luminosities LIR (center), and [CII] FWHMs (right) vs. UV luminosity for QSOs in massive host galaxies, as inferred from their luminous (L[CII]>1.8×109 L⊙) [CII] emission. The plotted error bars are 1σ. Our Mdyn determinations are derived using the measured FWHMs of the [CII] line and a fitting formula from Neeleman et al. (2021). Measurements for the individual QSOs contributin… view at source ↗

**Figure 4.** [CII] luminosity functions inferred for UV-faint (−24.5 < MUV,AB < −22.0: red circles) and UV-bright QSOs (MUV,AB < −26.0: blue solid circles) at z∼6 based on the distribution of [CII] luminosities seen for QSOs at a given UV luminosity (largely from CISTERN, a new ALMA program) and the Schindler et al. (2023) z ∼ 6 UV LF. Uncertainties are computed by adding in quadrature the contribution from every QSO whose mea… view at source ↗

Figures from the paper (3 more)

**Figure 3.** Figure 3: (upper) Fraction (vertical axis) of massive host galaxies (with L[CII] > 1.8 × 109 L⊙: solid line) brighter than some UV luminosity (horizontal axis). Our analysis indicates only 15% and 3% of the QSOs in massive host galaxies have UV luminosities brighter than −24.5 m…

**Figure 5.** Figure 5: (left) Median measured MBH (solid black circles) from the literature for QSOs in Massive Host Galaxies with L[CII]>1.8×109 L⊙ vs. UV luminosity. The smaller solid grey points show measured MBH vs. MUV for the massive host galaxy sample. The MBH measurements for a MUV,A…

**Figure 6.** Figure 6: (left) Median inferred λEdd (solid black circles) from the literature for QSOs in Massive Host Galaxies with L[CII]>1.8×109 L⊙ vs. UV luminosity. The grey points and grey star are as in [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

Discussion (0). Continue with ORCID to comment.

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

Euclid: Discovery of 31 new quasars at $6.6 < z < 7.8$
astro-ph.GA 2026-07 accept novelty 7.0 of 10

Euclid imaging plus multi-telescope spectroscopy discovers 31 new quasars at 6.6 < z < 7.8, including a record-holder at z ≈ 7.77 and 12 objects at z ≥ 7.
Euclid: A UV-faint quasar in a highly luminous star-forming host galaxy at $z \approx 7.7$
astro-ph.GA 2026-07 accept novelty 6.5 of 10

The UV-faint z=7.7 quasar EUCL J1253+7054 hosts the brightest [CII] emission among known z≈7.5 quasars, with L_[CII]≈2e9 L⊙ and SFR>250 M⊙/yr.

Reference graph

Works this paper leans on

112 extracted references · 64 canonical work pages · cited by 2 Pith papers

[1]

, " * write output.state after.block = add.period write newline

ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...
[2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....
[3]

B., Casey , C

Akins , H. B., Casey , C. M., Lambrides , E., et al. 2024, arXiv e-prints, arXiv:2406.10341

arXiv 2024
[4]

P., Mazzucchelli , C., et al

Ba \ n ados , E., Venemans , B. P., Mazzucchelli , C., et al. 2018, , 553, 473

2018
[5]

J., Smit , R., Schouws , S., et al

Bouwens , R. J., Smit , R., Schouws , S., et al. 2022, , 931, 160

2022
[6]

2022, , 134, 114501

CASA Team , Bean , B., Bhatnagar , S., et al. 2022, , 134, 114501

2022
[7]

M., Akins , H

Casey , C. M., Akins , H. B., Finkelstein , S. L., et al. 2025, arXiv e-prints, arXiv:2505.18873

arXiv 2025
[8]

C., Shanks , T., et al

Chehade , B., Carnall , A. C., Shanks , T., et al. 2018, , 478, 1649

2018

Show all 112 references

[9]

2015, , 576, A10

Ciesla , L., Charmandaris , V., Georgakakis , A., et al. 2015, , 576, A10

2015
[10]

H., & Kravtsov , A

Conroy , C., Wechsler , R. H., & Kravtsov , A. V. 2006, , 647, 201

2006
[11]

2013, , 766, 13

da Cunha , E., Groves , B., Walter , F., et al. 2013, , 766, 13

2013
[12]

B., Hennawi , J

Davies , F. B., Hennawi , J. F., & Eilers , A.-C. 2019, , 884, L19

2019
[13]

2014, , 568, A62

De Looze , I., Cormier , D., Lebouteiller , V., et al. 2014, , 568, A62

2014
[14]

2011, , 739, 56

De Rosa , G., Decarli , R., Walter , F., et al. 2011, , 739, 56

2011
[15]

P., Decarli , R., et al

De Rosa , G., Venemans , B. P., Decarli , R., et al. 2014, , 790, 145

2014
[16]

P., et al

Decarli , R., Walter , F., Venemans , B. P., et al. 2018, , 854, 97

2018
[17]

D., et al

Ding , X., Onoue , M., Silverman , J. D., et al. 2023, , 621, 51

2023
[18]

F., Davies , F

Eilers , A.-C., Hennawi , J. F., Davies , F. B., & Simcoe , R. A. 2021, , 917, 38

2021
[19]

F., Decarli , R., et al

Eilers , A.-C., Hennawi , J. F., Decarli , R., et al. 2020, , 900, 37

2020
[20]

2024, , 974, 275

Eilers , A.-C., Mackenzie , R., Pizzati , E., et al. 2024, , 974, 275

2024
[21]

P., Fan , X., et al

Endsley , R., Stark , D. P., Fan , X., et al. 2022, , 512, 4248

2022
[22]

P., Lyu , J., et al

Endsley , R., Stark , D. P., Lyu , J., et al. 2023, , 520, 4609

2023
[23]

Fan , X., Ba \ n ados , E., & Simcoe , R. A. 2023, , 61, 373

2023
[24]

K., Lupton , R

Fan , X., Narayanan , V. K., Lupton , R. H., et al. 2001, , 122, 2833

2001
[25]

A., Becker , R

Fan , X., Strauss , M. A., Becker , R. H., et al. 2006, , 132, 117

2006
[26]

2019, , 870, L11

Fan , X., Wang , F., Yang , J., et al. 2019, , 870, L11

2019
[27]

P., Arrigoni Battaia , F., Banados , E., et al

Farina , E. P., Arrigoni Battaia , F., Banados , E., et al. 2024, A 3D view of the first QSOs: A JWST/NIRSpec survey program , JWST Proposal. Cycle 3, ID. \#5645

2024
[28]

P., Schindler , J.-T., Walter , F., et al

Farina , E. P., Schindler , J.-T., Walter , F., et al. 2022, , 941, 106

2022
[29]

M., Miller , L., et al

Fine , S., Croom , S. M., Miller , L., et al. 2006, , 373, 613

2006
[30]

B., Watson , D., et al

Fujimoto , S., Brammer , G. B., Watson , D., et al. 2022, , 604, 261

2022
[31]

J., Duncan , K

Gloudemans , A. J., Duncan , K. J., Saxena , A., et al. 2022, , 668, A27

2022
[32]

D., Greene , J

Goulding , A. D., Greene , J. E., Setton , D. J., et al. 2023, , 955, L24

2023
[33]

E., Labbe , I., Goulding , A

Greene , J. E., Labbe , I., Goulding , A. D., et al. 2024, , 964, 39

2024
[34]

& Loeb , A

Haiman , Z. & Loeb , A. 2001, , 552, 459

2001
[35]

2023, , 959, 39

Harikane , Y., Zhang , Y., Nakajima , K., et al. 2023, , 959, 39

2023
[36]

E., Watson , D., Oesch , P

Heintz , K. E., Watson , D., Oesch , P. A., Narayanan , D., & Madden , S. C. 2021, , 922, 147

2021
[37]

2021 a , , 914, 36

Izumi , T., Matsuoka , Y., Fujimoto , S., et al. 2021 a , , 914, 36

2021
[38]

2019, , 71, 111

Izumi , T., Onoue , M., Matsuoka , Y., et al. 2019, , 71, 111

2019
[39]

2021 b , , 908, 235

Izumi , T., Onoue , M., Matsuoka , Y., et al. 2021 b , , 908, 235

2021
[40]

2018, , 70, 36

Izumi , T., Onoue , M., Shirakata , H., et al. 2018, , 70, 36

2018
[41]

2007, , 134, 1150

Jiang , L., Fan , X., Vestergaard , M., et al. 2007, , 134, 1150

2007
[42]

C., et al

Kaasinen , M., Venemans , B., Harrington , K. C., et al. 2024, , 684, A33

2024
[43]

2020, , 72, 84

Kato , N., Matsuoka , Y., Onoue , M., et al. 2020, , 72, 84

2020
[44]

2015, , 814, 9

Kirkpatrick , A., Pope , A., Sajina , A., et al. 2015, , 814, 9

2015
[45]

D., Onoue , M., Inayoshi , K., et al

Kocevski , D. D., Onoue , M., Inayoshi , K., et al. 2023, , 954, L4

2023
[46]

I., Greene , J

Kokorev , V., Caputi , K. I., Greene , J. E., et al. 2024, , 968, 38

2024
[47]

N., Duncan , K

Kondapally , R., Best , P. N., Duncan , K. J., et al. 2025, , 536, 554

2025
[48]

& Ho , L

Kormendy , J. & Ho , L. C. 2013, , 51, 511

2013
[49]

D., Walter , F., Fan , X., et al

Kurk , J. D., Walter , F., Fan , X., et al. 2007, , 669, 32

2007
[50]

E., Bezanson , R., et al

Labbe , I., Greene , J. E., Bezanson , R., et al. 2023, arXiv e-prints, arXiv:2306.07320

2023 arXiv
[51]

L., Finkelstein , S

Larson , R. L., Finkelstein , S. L., Kocevski , D. D., et al. 2023, , 953, L29

2023
[52]

R., Tremaine , S., Richstone , D., & Faber , S

Lauer , T. R., Tremaine , S., Richstone , D., & Faber , S. M. 2007, , 670, 249

2007
[53]

D., Izumi , T., et al

Li , J., Silverman , J. D., Izumi , T., et al. 2022, , 931, L11

2022
[54]

H., et al

Lyu , J., Alberts , S., Rieke , G. H., et al. 2024, , 966, 229

2024
[55]

C., Cormier , D., Hony , S., et al

Madden , S. C., Cormier , D., Hony , S., et al. 2020, , 643, A141

2020
[56]

2024, , 627, 59

Maiolino , R., Scholtz , J., Witstok , J., et al. 2024, , 627, 59

2024
[57]

2022, , 259, 18

Matsuoka , Y., Iwasawa , K., Onoue , M., et al. 2022, , 259, 18

2022
[58]

2019 a , , 883, 183

Matsuoka , Y., Iwasawa , K., Onoue , M., et al. 2019 a , , 883, 183

2019
[59]

2018 a , , 237, 5

Matsuoka , Y., Iwasawa , K., Onoue , M., et al. 2018 a , , 237, 5

2018
[60]

2023, , 949, L42

Matsuoka , Y., Onoue , M., Iwasawa , K., et al. 2023, , 949, L42

2023
[61]

2018 b , , 70, S35

Matsuoka , Y., Onoue , M., Kashikawa , N., et al. 2018 b , , 70, S35

2018
[62]

2016, , 828, 26

Matsuoka , Y., Onoue , M., Kashikawa , N., et al. 2016, , 828, 26

2016
[63]

2019 b , , 872, L2

Matsuoka , Y., Onoue , M., Kashikawa , N., et al. 2019 b , , 872, L2

2019
[64]

A., Kashikawa , N., et al

Matsuoka , Y., Strauss , M. A., Kashikawa , N., et al. 2018 c , , 869, 150

2018
[65]

A., Price , III, T

Matsuoka , Y., Strauss , M. A., Price , III, T. N., & DiDonato , M. S. 2014, , 780, 162

2014
[66]

P., Brammer , G., et al

Matthee , J., Naidu , R. P., Brammer , G., et al. 2024, , 963, 129

2024
[67]

P., et al

Mazzucchelli , C., Ba \ n ados , E., Venemans , B. P., et al. 2017, , 849, 91

2017
[68]

J., Warren , S

Mortlock , D. J., Warren , S. J., Venemans , B. P., et al. 2011, , 474, 616

2011
[69]

1998, in Theory of Black Hole Accretion Disks, ed

Narayan , R., Mahadevan , R., & Quataert , E. 1998, in Theory of Black Hole Accretion Disks, ed. M. A. Abramowicz , G. Bj \"o rnsson , & J. E. Pringle , 148--182

1998
[70]

P., et al

Neeleman , M., Novak , M., Venemans , B. P., et al. 2021, , 911, 141

2021
[71]

Oke , J. B. & Gunn , J. E. 1983, , 266, 713

1983
[72]

2021, A Complete Census of Supermassive Black Holes and Host Galaxies at z=6 , JWST Proposal

Onoue , M., Ding , X., Izumi , T., et al. 2021, A Complete Census of Supermassive Black Holes and Host Galaxies at z=6 , JWST Proposal. Cycle 1, ID. \#1967

2021
[73]

D., et al

Onoue , M., Ding , X., Silverman , J. D., et al. 2024, arXiv e-prints, arXiv:2409.07113

2024 arXiv
[74]

2019, , 880, 77

Onoue , M., Kashikawa , N., Matsuoka , Y., et al. 2019, , 880, 77

2019
[75]

2017, Frontiers in Astronomy and Space Sciences, 4, 35

Padovani , P. 2017, Frontiers in Astronomy and Space Sciences, 4, 35

2017
[76]

F., Schaye , J., et al

Pizzati , E., Hennawi , J. F., Schaye , J., et al. 2024, , 534, 3155

2024
[77]

L., Banerji , M., Becker , G

Reed , S. L., Banerji , M., Becker , G. D., et al. 2019, , 487, 1874

2019
[78]

Reines , A. E. & Volonteri , M. 2015, , 813, 82

2015
[79]

C., Brotherton , M

Runnoe , J. C., Brotherton , M. S., & Shang , Z. 2012, , 422, 478

2012
[80]

2014, , 790, 15

Sargsyan , L., Samsonyan , A., Lebouteiller , V., et al. 2014, , 790, 15

2014
[81]

2023, , 943, 67

Schindler , J.-T., Ba \ n ados , E., Connor , T., et al. 2023, , 943, 67

2023
[82]

2015, , 579, A60

Schneider , R., Bianchi , S., Valiante , R., Risaliti , G., & Salvadori , S. 2015, , 579, A60

2015
[83]

2008, , 478, 311

Schramm , M., Wisotzki , L., & Jahnke , K. 2008, , 478, 311

2008
[84]

J., Greene , J

Setton , D. J., Greene , J. E., Spilker , J. S., et al. 2025, arXiv e-prints, arXiv:2503.02059

2025 arXiv
[85]

H., Marsden , C., et al

Shankar , F., Weinberg , D. H., Marsden , C., et al. 2020, , 493, 1500

2020
[86]

T., Strauss , M

Shen , Y., Richards , G. T., Strauss , M. A., et al. 2011, , 194, 45

2011
[87]

A., Oguri , M., et al

Shen , Y., Strauss , M. A., Oguri , M., et al. 2007, , 133, 2222

2007
[88]

2019, , 873, 35

Shen , Y., Wu , J., Jiang , L., et al. 2019, , 873, 35

2019
[89]

J., Hailey-Dunsheath , S., Ferkinhoff , C., et al

Stacey , G. J., Hailey-Dunsheath , S., Ferkinhoff , C., et al. 2010, , 724, 957

2010
[90]

Urry , C. M. & Padovani , P. 1995, , 107, 803

1995
[91]

& Ostriker , J

Vale , A. & Ostriker , J. P. 2004, , 353, 189

2004
[92]

P., Verdoes Kleijn , G

Venemans , B. P., Verdoes Kleijn , G. A., Mwebaze , J., et al. 2015, , 453, 2259

2015
[93]

P., Walter , F., Neeleman , M., et al

Venemans , B. P., Walter , F., Neeleman , M., et al. 2020, , 904, 130

2020
[94]

R., Olsen , K

Vizgan , D., Greve , T. R., Olsen , K. P., et al. 2022 a , , 929, 92

2022
[95]

E., Greve , T

Vizgan , D., Heintz , K. E., Greve , T. R., et al. 2022 b , , 939, L1

2022
[96]

2003, , 582, 559

Volonteri , M., Haardt , F., & Madau , P. 2003, , 582, 559

2003
[97]

2021, Nature Reviews Physics, 3, 732

Volonteri , M., Habouzit , M., & Colpi , M. 2021, Nature Reviews Physics, 3, 732

2021
[98]

2009, , 457, 699

Walter , F., Riechers , D., Cox , P., et al. 2009, , 457, 699

2009
[99]

2024, , 968, 9

Wang , F., Yang , J., Fan , X., et al. 2024, , 968, 9

2024
[100]

L., et al

Wang , R., Wagg , J., Carilli , C. L., et al. 2013, , 773, 44

2013
[101]

J., Albert , L., Arzoumanian , D., et al

Willott , C. J., Albert , L., Arzoumanian , D., et al. 2010 a , , 140, 546

2010
[102]

J., Albert , L., Arzoumanian , D., et al

Willott , C. J., Albert , L., Arzoumanian , D., et al. 2010 b , , 140, 546

2010
[103]

J., Bergeron , J., & Omont , A

Willott , C. J., Bergeron , J., & Omont , A. 2015, , 801, 123

2015
[104]

J., Bergeron , J., & Omont , A

Willott , C. J., Bergeron , J., & Omont , A. 2017, , 850, 108

2017
[105]

J., Delorme , P., Reyl \'e , C., et al

Willott , C. J., Delorme , P., Reyl \'e , C., et al. 2010 c , , 139, 906

2010
[106]

J., Omont , A., & Bergeron , J

Willott , C. J., Omont , A., & Bergeron , J. 2013, , 770, 13

2013
[107]

J., Percival , W

Willott , C. J., Percival , W. J., McLure , R. J., et al. 2005, , 626, 657

2005
[108]

2022, , 517, 2659

Wu , J., Shen , Y., Jiang , L., et al. 2022, , 517, 2659

2022
[109]

A., Bing , L., et al

Xiao , M., Oesch , P. A., Bing , L., et al. 2025, arXiv e-prints, arXiv:2503.01945

2025 arXiv
[110]

2021, , 923, 262

Yang , J., Wang , F., Fan , X., et al. 2021, , 923, 262

2021
[111]

A., et al

Yue , M., Eilers , A.-C., Simcoe , R. A., et al. 2024, , 966, 176

2024
[112]

2018, , 481, 1976

Zanella , A., Daddi , E., Magdis , G., et al. 2018, , 481, 1976

2018

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.

[1] [1]

, " * write output.state after.block = add.period write newline

ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...

[2] [2]

write newline

" write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

[3] [3]

B., Casey , C

Akins , H. B., Casey , C. M., Lambrides , E., et al. 2024, arXiv e-prints, arXiv:2406.10341

arXiv 2024

[4] [4]

P., Mazzucchelli , C., et al

Ba \ n ados , E., Venemans , B. P., Mazzucchelli , C., et al. 2018, , 553, 473

2018

[5] [5]

J., Smit , R., Schouws , S., et al

Bouwens , R. J., Smit , R., Schouws , S., et al. 2022, , 931, 160

2022

[6] [6]

2022, , 134, 114501

CASA Team , Bean , B., Bhatnagar , S., et al. 2022, , 134, 114501

2022

[7] [7]

M., Akins , H

Casey , C. M., Akins , H. B., Finkelstein , S. L., et al. 2025, arXiv e-prints, arXiv:2505.18873

arXiv 2025

[8] [8]

C., Shanks , T., et al

Chehade , B., Carnall , A. C., Shanks , T., et al. 2018, , 478, 1649

2018