The space densities and emissivities of AGNs at $z> 4$

A. Fontana; A. Grazian; D. Kodra; E. Giallongo; F. Fiore; J. A. Newman; K. Boutsia; L. Pentericci; M. Castellano; M. Dickinson

REVIEW 3 major objections 4 minor 121 references

The space densities and emissivities of AGNs at $z> 4$

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Faint active galactic nuclei at z≈4.5 are dense enough that their ultraviolet emission matches the intergalactic photoionization rate, and they can supply a major share of reionization at z≈5.6.

desk verdict New 7 Ms data and a transparent re-analysis make the z~4.5 faint-end AGN densities the paper's solid core; the z~5.6 emissivity extrapolation depends on an untested template choice. read the letter →

arxiv 1909.00702 v1 pith:W62QX7NK submitted 2019-09-02 astro-ph.GA astro-ph.CO

E. Giallongo , A. Grazian , F. Fiore , D. Kodra , T. Urrutia , M. Castellano , S. Cristiani , M. Dickinson

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A. Fontana N. Menci L. Pentericci K. Boutsia J. A. Newman S. Puccetti

This is my paper · ORCID

classification astro-ph.GAastro-ph.CO

keywords activegalacticnucleiAGNluminosityfunctionX-rayselectedphotometricredshiftsionizingemissivityintergalacticmediumcosmicreionizationLyman-alphaforest

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 argues that faint active galactic nuclei (AGNs) at $z\sim4.5$ were far denser than optical quasar surveys suggest, and tries to measure that population and its ionizing output. From the CANDELS galaxy catalogs in GOODS-South, GOODS-North, and EGS, the authors select 32 X-ray-detected AGN candidates at $4

What carries the argument

The carrying mechanism is the X-ray-plus-H-band selection: the parent sample is CANDELS galaxies at photometric $z>4$, and the AGN candidates are those with X-ray emission at their H-band positions in deep Chandra images, a route that finds faint active nuclei that optical color and morphology selection miss. The density estimates use the $1/V_{\mathrm{max}}$ estimator with corrections for H-band incompleteness and for the X-ray-flux limit as a function of H-band magnitude, and Monte Carlo draws from the photometric-redshift probability distributions check that broad PDFs do not bias the luminosity function. The final object is the rest-frame 1450 Å double power-law luminosity function, $\varphi(M)=\varphi^*/(10^{0.4(M_{\mathrm{break}}-M)(\beta-1)}+10^{0.4(M_{\mathrm{break}}-M)(\gamma-1)})$, whose integral over $-29<M_{1450}<-18$ with an adopted AGN spectrum and $f_{\mathrm{esc}}\simeq1$ yields the ionizing emissivity and photoionization rate.

What would settle it

Measure rest-frame optical or submillimeter redshifts for the eight candidates that also permit low-redshift, dusty AGN-template solutions; if several of those objects are confirmed at $z<4$, the claimed faint-end volume densities and the photoionization rate derived from them are overestimates.

Watch

Extended reading notes

Core claim

The paper's central claim is that the AGN ultraviolet luminosity function at $z\sim4.5$ has a flat faint end, $\beta\simeq1.7$, rather than continuing the steep decline of bright quasars. The new space densities are $\varphi\sim10^{-5}\,\mathrm{Mpc}^{-3}\,\mathrm{mag}^{-1}$ in $-21.5\lesssim M_{1450}\lesssim-18.5$, and they join the brighter COSMOS spectroscopic sample and SDSS quasars through a double power law with break $M_{\mathrm{break}}\sim-25.8$ and bright-end slope $\gamma\sim3.7$. These numbers imply an ionizing emissivity that, with an escape fraction near unity, produces a hydrogen photoionization rate consistent with the Lyman-$\alpha$ forest at $z\sim4.5$. At $z\sim5.6$ the sample is too sparse to fix the luminosity function shape, but if the slopes do not change, AGNs can provide more than half of the photoionization rate inferred from the IGM.

Load-bearing premise

The load-bearing assumption is that the distances to the 32 candidates, estimated by matching their colors with galaxy templates, are correct, and that the roughly 20% of objects which also fit low-redshift, dust-reddened AGN templates really are at $z>4$; if the low-redshift fits are right, a large fraction of the sample, especially in the $z=5$ to $6.1$ bin, is contamination and the faint-end densities and emissivities are overestimated.

Editorial extensions

If this is right

Most of the AGN ionizing emissivity at $z\sim4.5$ comes from intermediate-luminosity AGNs near $M_{1450}\sim-22$ to $-23$, not from the rare bright quasars that dominate optical surveys.
If these densities are right, standard optical color-selected surveys at $z>4$ are missing a substantial fraction of the AGN population; the paper estimates the discrepancy at roughly a factor 3-4 at $z\sim4.5$.
If the luminosity function shape holds to $z\sim5.6$, AGNs supply more than half of the IGM photoionization rate, making them competitive with star-forming galaxies during reionization.
The consistency with the Lyman-alpha forest requires escape fractions near unity for faint AGNs, so AGN-driven outflows become an essential part of the ionizing-photon budget at these redshifts.
At $z>5$ the data are too sparse to fix the luminosity function shape; the AGN contribution hinges on how the break and slopes evolve between $z\sim4.5$ and $z\sim5.6$.

Reading between the lines

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

A natural extension is to use near-infrared spectroscopy to measure redshifts for the objects whose spectral energy distributions are degenerate between high-$z$ galaxy templates and low-$z$ dusty AGN templates; this would settle the main ambiguity without relying on either template choice.
If the faint-end densities hold, the implied comoving space density of supermassive black holes at $z>4$ is a boundary condition for black-hole seed formation models, a census the present paper does not attempt.
Stacking X-ray emission from even fainter H-band-selected galaxies would test whether the flat faint-end slope continues below $M_{1450}\sim-18.5$ or turns over.

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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

The key thing to know: this is a legitimate update rather than a restatement. New 7 Ms Chandra imaging in GOODS-South, plus shallower data in GOODS-North and EGS, gives 32 AGN candidates at 4

The z~4.5 result is the solid part. The densities rest on X-ray detections, the Monte Carlo resampling of the photometric-redshift PDFs gives consistent LFs, and the emissivity from this LF agrees with IGM Lyman-alpha forest measurements. Yes, the consistency is not a prediction, since the same data feed both the LF and the emissivity integral, but the paper says 'consistent' rather than 'predicted,' and the agreement is reassuring given the different systematics.

The soft spot, as the stress-test notes, is the z>5 bin. About 20% of the sample has alternative low-redshift, dusty AGN-template solutions. The paper argues those are unlikely because they require pure AGN SEDs in dwarf galaxies, but that is an argument, not a test. The Monte Carlo check re-samples the galaxy-template PDFs, so it cannot validate the template choice itself. With only 3, 4, 1, 1 objects in the M=-19 to -22 bins at z=5-6.1, losing a couple of sources to low-z interlopers would substantially change the emissivity extrapolation. The incompleteness correction, derived from the same X/H distribution, is another acknowledged uncertainty, but it affects all bins, not just z>5.

Who is this for: anyone working on the AGN contribution to reionization or the high-redshift AGN luminosity function. It deserves a serious referee. The z~4.5 measurement is likely to stand, and the z>5 part is clearly flagged as extrapolation. My own verdict would be: accept with revisions, mostly asking for a fuller description of the PDF-combination method and a dedicated test of the template degeneracy, or at least a clearer statement of how much the z>5 emissivity depends on rejecting the low-z solutions.

Referee Report

3 major / 4 minor

Summary. The paper constructs a sample of 32 X-ray-detected AGN candidates at photometric redshifts 4<z<6.1 in the CANDELS GOODS-South, GOODS-North, and EGS fields, using the new 7 Ms Chandra image in GOODS-South and shallower Chandra data in the other fields. It derives 1/Vmax UV luminosity functions at z~4.5 and z~5.6 (Table 2), fits double power-law luminosity functions (Table 3) that combine the CANDELS points with the COSMOS spectroscopic sample and bright SDSS quasars, and converts the fitted luminosity functions into ionizing emissivities and photoionization rates (Table 3, Fig. 6). The paper's main results are faint-end densities phi~1e-5 Mpc^-3 mag^-1 at M1450~-18.5 to -21.5 at z~4.5, consistency of the predicted photoionization rate with IGM Ly-alpha forest measurements at z~4.5, and a model-dependent suggestion of a significant AGN contribution at z~5.6.

Significance. If the faint-end densities at z~4.5 hold, they substantially strengthen the case that faint AGNs contribute significantly to the ionizing photon budget at z>4, with implications for supermassive black hole growth and reionization models. The paper has several concrete strengths: the X-ray stacking of the 14 new sources gives S/N~10 (Appendix), the Monte Carlo propagation of photometric-redshift PDFs (Sect. 3.1, Table 2) directly addresses redshift-scatter biases, and the comparison with the IGM photoionization rate is an external consistency check rather than an in-sample prediction. The main caveat is that the z>5 densities and the model-4 extrapolation rest on a small number of objects with a known template degeneracy.

major comments (3)

[Section 2.3, Figure 8, Table 2] The 20% of candidates with low-redshift, dusty AGN-template solutions is the dominant systematic for the z=5-6.1 luminosity function. The Monte Carlo test in Sect. 3.1/Table 2 re-samples the adopted galaxy-template PDF(z) for each source and therefore cannot validate the template-set choice; it only assesses scatter around the assumed solutions. The argument that the low-z solutions imply implausible pure-AGN dwarf galaxies is reasonable but is not a quantitative rejection. I request a robustness test: refit the ambiguous candidates (including GDS11847, GDS33160, and the sources flagged in the Appendix as having broad PDFs) with two-component AGN+host-galaxy templates, or repeat the 1/Vmax analysis with those objects removed and report the resulting Table 2 densities and model-4 photoionization rate. If several of the nine objects in the z=5-6.1 bins are interlopers, phi_corr and the model-4 value Gamma=0.11(+0.41,-0.09) in Table 3 would be overestimated.
[Section 3.1] The X/H-based incompleteness correction is derived from the same 32 detected candidates, as the text acknowledges ('could be biased by selection effects'). Because the correction is a factor of ~2 at H>26 and directly affects the faintest bins that anchor the flat faint-end slope beta~1.7, the systematic uncertainty in the correction is not captured by the Poisson errors in Table 2 or by the Monte Carlo scatter in phi_MC. Please either validate the X/H distribution against an external sample or quote an additional systematic error term on phi_corr and on the derived emissivities and photoionization rates.
[Section 3.2, Table 3] The two solutions at z=5.6 bracket a wide range, with model 3 giving Gamma~0.07 and model 4 giving Gamma=0.11(+0.41,-0.09); model 4 is obtained by fixing the two slopes to the z=4.5 values, which is an assumption rather than a fit. Given that only nine objects define the z=5-6.1 CANDELS bins and the brightest bin contains one spectroscopic source (GDN3333), the abstract and conclusions should more prominently state that the z~5.6 contribution is an extrapolation under a shape-invariance assumption, not a direct measurement.

minor comments (4)

[Throughout] There are several typographical errors: 'integalactic' in the Introduction, 'Dahlen el al.' in Section 2, 'phtotometric' in Section 3.2, 'Form Table 2' in Section 3.1, and 'Bruzual & Carlot' in the Figure 7 caption and Appendix text.
[Appendix] In the list of sources stacked for the X-ray detection, GDS11287 appears twice; it should be listed once.
[Table 2] The table lists phi_MC with quoted uncertainties, but the text in Section 3.1 does not explicitly define whether these are the 16-84 percentile range, the standard deviation of the 1000 realizations, or something else; please clarify.
[Figure 6] The caption and text describe the IGM-inferred photoionization rates as open symbols, but some points in the figure appear filled; please ensure the symbol legend and text are consistent.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: fitted LF is compared against an external IGM benchmark, and the acknowledged self-corrections are stated limitations rather than reductions.

full rationale

Walked the paper's claimed derivation chain. The volume densities in Table 2 are 1/Vmax counts of the 32 X-ray-selected AGN candidates; the parametric luminosity functions in Table 3 are fitted to those CANDELS points plus the COSMOS sample (Boutsia et al. 2018), the NOAO sample (Glikman et al. 2011), and the bright SDSS QSO densities. The emissivities and photoionization rates in Table 3 and Figure 6 are integrals over these fitted LFs using an assumed AGN SED and an assumed escape fraction. The claimed 'consistency' with the IGM is therefore a comparison of the integrated AGN emissivity against an external Lyman-alpha forest ionization measurement; the LF parameters are not fitted to the IGM data, so the agreement is not forced by construction. The incompleteness correction in Section 3.1 does use the observed X/H distribution of the same 32 detected objects ("The incompleteness fraction is derived from the same X/H distribution observed above the X-ray flux threshold"), but the paper explicitly states this "could be biased by selection effects" — that is an admitted statistical assumption, not a prediction equivalent to its input. The photometric-redshift template degeneracy (Section 2.3, Figure 8), in which ~20% of candidates admit low-redshift dusty AGN-template solutions, is a genuine correctness risk for the z>5 densities, but the paper argues against those solutions from external SED analyses and from the resulting implausibly low host luminosities; the Monte Carlo PDF check is internal to the adopted galaxy-template set. These are robustness limitations, not circular reductions. Self-citations (G15 for the selection method, Boutsia et al. 2018 for the COSMOS sample, Grazian et al. 2018 for the escape-fraction measurement) are used as published data or methods with independent, externally checkable content, and no load-bearing claim reduces to an unverified self-citation. Accordingly, no circular step meeting the evidentiary threshold was found.

Assumptions & free parameters 6 free parameters · 8 assumptions · 0 invented entities

The central claims rest on the fitted LF parameters, the photometric redshift template choice, and the escape-fraction assumption. No new physical entities are introduced. The X/H incompleteness correction is an additional assumption that is partly self-referential because it uses the observed X/H distribution of the same detected sample.

free parameters (6)

Faint-end slope beta = 1.70, 1.74, 1.92, 1.74
Free parameter in the double power-law LF fit (Eq. 2) to CANDELS, COSMOS, and SDSS data; directly sets the faint-end densities and the emissivity integral.
Bright-end slope gamma = 3.71, 3.72, 3.09, 3.72
Second free slope in the fitted LF; controls the contribution of bright QSOs.
Break magnitude M_break = -25.81, -25.89, -25.06, -25.37
Location of the break between faint and bright ends in Eq. 2; fitted and highly influential for the z~5.6 extrapolation.
Normalization log phi* = -6.68, -6.76, -7.29, -7.05
Overall LF amplitude fitted to the data; enters all emissivity and photoionization rate numbers.
Escape fraction fesc = 1.0 adopted, 0.8 lower limit from Grazian et al. 2018
Multiplies the ionizing emissivity; photoionization rates scale linearly with it and it is not measured in this work.
X-ray photon index Gamma = 1.4 for faint sources
Assumed photon index used to convert 0.5-2 keV count rates to fluxes and luminosities; a different slope changes X-ray luminosities and mildly affects selection corrections.

assumptions (8)

standard math Cosmological parameters Omega_Lambda=0.7, Omega_m=0.3, h=0.7
Adopted in Section 1 for volume and density computations.
domain assumption H-band selection provides rest-frame UV selection at z>4
Section 2: H band corresponds to rest-frame UV (<3000 A) at z>4, so the sample is UV-selected.
domain assumption Photometric redshifts from galaxy templates are preferred; low-z dusty AGN solutions are rejected
Section 2.3 and Appendix: about 20% of sources have alternate low-z dusty AGN-template solutions; the paper argues they are unlikely, but this choice is load-bearing.
domain assumption X-ray detection at the H-band position indicates an AGN
Section 2.2: LX>1e43 erg/s is used as the AGN threshold, supported by Ranalli et al. 2003 because the implied star formation rates would be implausibly high.
domain assumption UV light at 1450 A is dominated by the AGN, not the host galaxy
Section 2.5: tested by comparing to the Lusso et al. 2010 LX-LUV relation; holds on average but with scatter.
domain assumption Escape fraction fesc~1 (or ~0.8) applies to faint AGNs down to M1450=-18
Section 4: extrapolated from bright quasars and one intermediate-luminosity sample; no direct fesc measurements exist for the faintest z~4 AGNs.
domain assumption The UV luminosity function has a double power-law shape
Section 3.2, Eq. 2: the assumed shape is used to fit the data and integrate emissivity.
ad hoc to paper The LF shape does not evolve between z~4.5 and z~5.6 (model 4)
Section 3.2 and Table 3: model 4 fixes the slopes to z=4.5 values; the 'important AGN contribution at z~5.6' conclusion depends on this untested assumption.

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Cite this review

Pith. "Pith review of The space densities and emissivities of AGNs at $z> 4$." pith.science (2026). https://pith.science/paper/W62QX7NK

@misc{pith2026190900702,
  author       = {Pith},
  title        = {Pith review of: The space densities and emissivities of AGNs at $z> 4$},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/W62QX7NK}},
  note         = {Machine review of arXiv:1909.00702}
}

abstract

The study of the space density of bright AGNs at $z>4$ has been subject to extensive effort given its importance for the estimate of the cosmological ionizing emissivity and growth of supermassive black holes. In this context we have recently derived high space densities of AGNs at $z\sim 4$ and $-25<M_{1450}<-23$ in the COSMOS field from a spectroscopically complete sample. In the present paper we attempt to extend the knowledge of the AGN space density at fainter magnitudes ($-22.5<M_{1450}<-18.5$) in the $4<z<6.1$ redshift interval by means of a multiwavelength sample of galaxies in the CANDELS GOODS-South, GOODS-North and EGS fields. We use an updated criterion to extract faint AGNs from a population of NIR (rest-frame UV) selected galaxies at photometric $z>4$ showing X-ray detection in deep Chandra images available for the three CANDELS fields. We have collected a photometric sample of 32 AGN candidates in the selected redshift interval, six of which having spectroscopic redshifts. Including our COSMOS sample as well as other bright QSO samples allows a first guess on the shape of the UV luminosity function at $z\sim 4.5$. The resulting emissivity and photoionization rate appear consistent with that derived from the photoionization level of the intergalactic medium at $z\sim 4.5$. An extrapolation to $z\sim 5.6$ suggests an important AGN contribution to the IGM ionization if there are no significant changes in the shape of the UV luminosity function.

Figures

Figures reproduced from arXiv: 1909.00702 by the authors.

**Figure 2.** Log(L(ν)2keV ) vs. Log(L(ν)2500) for our AGN candidates (filled squares). The Lusso et al. (2010) AGN COSMOS sample is also shown for comparison as small dots. The continuous and dotted lines represent the Lusso et al. (2010) best fit correlation with 1σ uncertainties in the offset parameter. contribution at least by 50-60% of the total UV flux has been found within a 25th percentile of the unobscured AGN sample for… view at source ↗

**Figure 1.** Spectra of two AGNs with emission line detection from the data cube by Urrutia et al. (2018). Fluxes are in arbitrary units. The Lyman α emitter GDS9945 (top panel) is included in the Herenz et al. (2017) paper. Here we show a slightly offsetted spectrum showing a flux-reduced, slightly asymmetric Lyα line. A weak NV possible emission feature 1500 km s−1 redward of the expected position is present. Tentative identif… view at source ↗

**Figure 3.** Adimensional X/H flux ratios as a function of the H-band magnitude for the GOODS-South (pentagons), GOODS-North (red triangles) and EGS (empty circles) AGN candidates. Straight lines represent the adopted X-ray flux limits of the three fields at 40% completeness level, 10−17 (continuous), 1.5 × 10−17 (dashed) and 3.4 × 10−17 (dotted) in erg cm−2 s −1 in the 0.5 − 2 keV band for GDS, GDN and EGS respectively. As in G… view at source ↗

Figures from the paper (12 more)

**Figure 4.** Figure 4: UV 1450 ˚A AGN luminosity functions in two redshift intervals. Different symbols represent 1/Vmax data points from different surveys as explained in the figure box. Green crosses are CANDELS points only corrected for incompleteness in the H band counts while red square…

**Figure 5.** Figure 5: Fractional ionizing emissivity at z = 4.5 for model 2. The fraction is relative to the emissivity computed for M1450 ≤ −18. Similar results can be derived for model 1. little evolution in the escape fraction with decreasing luminosities from M1450 . −27 down to M1450 …

**Figure 6.** Figure 6: Cosmic photoionization rate Γ−12 in units of 10−12 s −1 produced by AGNs as a function of redshift assuming hfi = 1. Red filled squares represent the predicted contribution at z = 4.5 and z = 5.6 by the global AGN luminosity functions shown in table 3 as models 2 and …

**Figure 7.** Figure 7: Spectral energy distributions and probability distribution functions of photometric redshifts for all the AGN candidates shown in [PITH_FULL_IMAGE:figures/full_fig_p021_7.png]

**Figure 8.** Figure 8: Histogram of redshift difference between estimates based on galaxy templates (BC) vs. AGN templates. Dust reddening is also included in the libraries. Small magellanic clouds extinction curves are adopted for AGN templates. The Calzetti law is also added in galaxy temp…

**Figure 9.** Figure 9: Multiwavelength UV-NIR distribution of all the GOODS-South candidates. From top left to bottom right the VIMOS U, HST (B,V,I,Z,Y,J,H), IRAC (3.6,4.5,5.8,8 µm) images are shown. The sizes are ∼ 9 × 6 arcsec2 . The targets are in the center of the circle in the H-band im…

**Figure 10.** Figure 10: Multiwavelength UV-NIR distribution of all the GOODS-North candidates. From top left to bottom right the LBT U, HST (B,V,I,Z,Y,J,H), IRAC (3.6,4.5,5.8,8 µ) images are shown. The sizes are ∼ 9 × 6 arcsec2 . The targets are in the center of the circle in the H-band imag…

**Figure 11.** Figure 11: Multiwavelength UV-NIR distribution of all the EGS candidates. From top left to bottom right the CFHT (U,G,R), HST V606, CFHT I, HST I814, CFHT (Z,Y) HST (J,H), IRAC (3.6, 4.5 µm) images are shown. The sizes are ∼ 9 × 6 arcsec2 . The targets are in the center of the c…

**Figure 12.** Figure 12: X-ray contours of the AGN candidates in the GOODS-South field overlaid with the HST H-band image. The AGN candidate is at the center of the H-band image. The green circle radius is 2 arcsec. The X-ray energy bands adopted for the detection are also shown for the new s…

**Figure 13.** Figure 13: Figure similar to the previous one for the X-ray GOODS-North sources. The X-ray energy bands adopted for the detection are also shown for the new sources. For the other known sources the energy band is 0.5-2 keV. Intensity contours are on a linear scale with a factor …

**Figure 14.** Figure 14: Figure similar to the previous one for the X-ray EGS sources. The X-ray energy bands adopted for the detection are also shown for the new sources. For the other known sources the energy band is 0.5-2 keV. Intensity contours are on a linear scale with a factor 2 of dyn…

**Figure 15.** Figure 15: X-ray stack image in the 0.8-3 keV energy band of the new 14 sources found in the present work. The photometric area is shown by a circle with a radius of ∼ 2 arcsec. See the Appendix for more details [PITH_FULL_IMAGE:figures/full_fig_p042_15.png]

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Works this paper leans on

121 extracted references · 71 canonical work pages

[1]

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thebibliography [1] 20pt to REFERENCES 6pt =0pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command Each re...

2019
[2]

2018, PASJ, 70, 34

Akiyama, M., He, W., Ikeda, H., et al. 2018, PASJ, 70, 34

2018
[3]

M., Bauer, F

Alexander, D. M., Bauer, F. E., Brandt, W. N. et al. 2003, AJ, 126, 539

2003
[4]

Ashby, M. L. N., Willner, S. P., Fazio, G. G., et al. 2013, ApJ, 769, 80

2013
[5]

2018, arXiv:1803.09747

Atek, H., Richard, J., Kneib, J.-P., & Schaerer, D. 2018, arXiv:1803.09747

arXiv 2018
[6]

Avni, Y., & Bahcall, J. N. 1980, ApJ, 235, 694

1980
[7]

Bacon, R., Brau-Nogu\' e , S., Caillier, P. et al. 2009, ASSP, 9, 331

2009
[8]

Balestra, I, Mainieri, V., Popesso, P. et al. 2010, A&A, 512, 12

2010

Show all 121 references

[9]

J., Cowie, L

Barger, A. J., Cowie, L. L., & Wang, W.-H. 2008, ApJ, 689, 687

2008
[10]

J., Cowie, L

Barger, A. J., Cowie, L. L., Chen, C.-C., et al. 2014, ApJ, 784, 9

2014
[11]

G., Cava, A

Barro, G., P \'e rez-Gonz \'a lez, P. G., Cava, A. et al. 2019, ApJS, 243, 41

2019
[12]

D., Bolton, J

Becker, G. D., Bolton, J. S., Haehnelt, M. G., & Sargent, W. L. W. 2011, MNRAS, 410, 1096

2011
[13]

D., & Bolton, J

Becker, G. D., & Bolton, J. S. 2013, MNRAS, 436, 1023

2013
[14]

Becker, G.D., Bolton, J. S. Madau, P. Pettini, M., Ryan-Weber, E. V., & VEnemans, B. P. 2015, MNRAS 447, 3402

2015
[15]

2017, ApJ, 837, L12

Bian, F., Fan , X., McGreer, I., Cai, Z., Jiang, L. 2017, ApJ, 837, L12

2017
[16]

Bongiorno, A., Maiolino, R., Brusa, M. et al. 2014, MNRAS, 443, 2077

2014
[17]

Bongiorno, A., Merloni, A., Brusa, M. et al. 2012, MNRAS, 427, 3103

2012
[18]

2018, ApJ, 869, 20

Boutsia, K., Grazian, A., Giallongo, E., Fiore, F., Civano, F. 2018, ApJ, 869, 20

2018
[19]

J., Illingworth, G

Bouwens, R. J., Illingworth, G. D., Oesch, P. A., et al. 2015, ApJ, 803, 34

2015
[20]

J., Oesch, P

Bouwens, R. J., Oesch, P. A., Illingworth, G. D., et al. 2017, ApJ, 843, 129

2017
[21]

2003, MNRAS, 344, 1000

Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000

2003
[22]

2011, MNRAS, 412, 2543

Calverley, A.P., Becker, G.D., Haehnelt, M.G., & Bolton, J.S. 2011, MNRAS, 412, 2543

2011
[23]

Cappelluti, N., Comastri, A., Fontana, A. et al. 2016, ApJ, 823, 95

2016
[24]

Chardin, J., Puchwein, E., Haehnelt, M. G. 2017, MNRAS, 465, 3429

2017
[25]

C., Shanks, T

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

2018
[26]

Civano, F., Brusa, M., Comastri, A. et al. 2011 ApJ, 741, 91

2011
[27]

M., Fontantot, F., Vanzella, E., & Monaco, P

Cristiani, S., Serrano, L. M., Fontantot, F., Vanzella, E., & Monaco, P. 2016, MNRAS, 462, 2478

2016
[28]

B., Furlanetto, S

D'Aloisio, A., McQuinn, M., Davies, F. B., Furlanetto, S. R. MNRAS, 473, 560
[29]

M., et al

Dahlen, T., Mobasher, B., Faber, S. M., et al. 2013, ApJ, 775, 93

2013
[30]

B., Hennawi, J

Davies, F. B., Hennawi, J. F., Ba \ n ados, E., et al. 2018, ApJ, 864, 142

2018
[31]

A., Schneider, D

Fan, X., Strauss, M. A., Schneider, D. P. et al. 2001, AJ, 121, 54

2001
[32]

2006, ApJ, 132, 117

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

2006
[33]

2008, ApJ, 688, 85

Faucher-Gigu \`e re, C.-A., Linz, A., Hernquist, L., & Zaldarriaga, M. 2008, ApJ, 688, 85

2008
[34]

2009, ApJ, 703, 1416

Faucher-Gigu \`e re, C.-A., Lidz, A., Zaldarriaga, M., & Hernquist, L. 2009, ApJ, 703, 1416

2009
[35]

Finkelstein, S., D'Aloisio, A., Paardekooper, J-P. et al. 2019, arXiv:190202792

2019
[36]

Fiore, F., Puccetti, S., Grazian, A. et al. 2012, A&A, 537, 16

2012
[37]

Fiore, F. et al. (2018), in preparation

2018
[38]

N, Grazian A., & Mao, J

Fontanot, F., Cristiani, S., Monaco, P., Nonino, M., Vanzella, E., Brandt, W. N, Grazian A., & Mao, J. 2007, A&A 461, 39

2007
[39]

& Vanzella, E

Fontanot, F., Cristiani, S., Pfrommer, C., Cupani, G. & Vanzella, E. 2014, MNRAS, 438, 2097

2014
[40]

C., Allen, G

Fruscione, A., McDowell, J. C., Allen, G. E. et al. 2006, SPIE, 6270, 1

2006
[41]

S., Kim, T.-S., Leach, S., & Viel, M

Garzilli, A., Bolton, J. S., Kim, T.-S., Leach, S., & Viel, M. 2012, MNRAS, 424, 1723

2012
[42]

1986, ApJ, 303, 336

Gehrels, N. 1986, ApJ, 303, 336

1986
[43]

2012, ApJ, 755, 124

Giallongo, E., Menci, N., Fiore, F., Castellano, M., Fontana, A., Grazian, A., & Pentericci, L. 2012, ApJ, 755, 124

2012
[44]

Giallongo, E., Grazian, A., Fiore, F. et al. 2015, A&A, 578, 83, G15

2015
[45]

2007, A&A, 463, 79

Gilli, R., Comastri, A., Hasinger, G. 2007, A&A, 463, 79

2007
[46]

2011, ApJ, 728, L26

Glikman, E., Djorgovski, S.G., Stern, D., Dey, A., Jannuzi, B.T., & Lee, K.-S. 2011, ApJ, 728, L26

2011
[47]

Grazian, A., Giallongo, E., Paris, D. et al. 2017, A&A, 602, 18

2017
[48]

2018, A&A, 613, 44

Grazian, A., Giallongo, E., Boutsia, K. 2018, A&A, 613, 44

2018
[49]

C., Giavalisco, M., et al

Guo, Y., Ferguson, H. C., Giavalisco, M., et al. 2013, ApJS, 207, 24

2013
[50]

1996, ApJ, 461, 20

Haardt, F., & Madau, P. 1996, ApJ, 461, 20

1996
[51]

2012, ApJ, 746, 125

Haardt, F., & Madau, P. 2012, ApJ, 746, 125

2012
[52]

P., Malhotra, S., & Rhoads, J

Hathi, N. P., Malhotra, S., & Rhoads, J. E. 2008, ApJ, 673, 686

2008
[53]

C., Urrutia, T., Wisotzki, L

Herenz, E. C., Urrutia, T., Wisotzki, L. et al. 2017, A&A 606, 12

2017
[54]

Hinshaw, G., Larson, D., Komatsu, E. et al. 2013, ApJS, 208, 19

2013
[55]

F., et al

Hiss, H., Walther, M., Hennawi, J. F., et al. 2018, ApJ, 865, 42

2018
[56]

Hoag, A., Bradac, M., Huang, K. et al. 2019, ApJ, 878, 12

2019
[57]

Hsu, L.-T., Salvato, M., Nandra, K. et al. 2014, ApJ, 796, 60

2014
[58]

2018, ApJ, 854, 73

Ishigaki, M., Kawamata, R., Ouchi, M., Oguri, M., Kazuhiro Shimasaku, K., & Ono, Y. 2018, ApJ, 854, 73

2018
[59]

2017, MNRAS, 468, 389

Japelj, J, Vanzella, E., Fontanot, F., et al. 2017, MNRAS, 468, 389

2017
[60]

H., Barger, A

Jones, L. H., Barger, A. J., Cowie, L. L., Oesch, P., Hu, E. M., Songaila, A., Naidu, R., P. 2018, ApJ, 862, 142

2018
[61]

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

2018
[62]

C., Weinberger, L

Keating, L. C., Weinberger, L. H., Kulkarni, G., Haehnelt, M. G., Chardin, J. 2019, arXiv:1905.12640

2019 arXiv
[63]

2018, ApJ, 855, 4

Kawamata, R., Ishigaki, M., Shimasaku, K., Oguri, M., Ouchi, M., Tanigawa, S. 2018, ApJ, 855, 4

2018
[64]

LaMassa, S., Glikman, E., Brusa, M. et al. 2017, ApJ, 847, 21

2017
[65]

S., Zitrin, A., Stark, D

Laporte, N., Nakajima, K., Ellis, R. S., Zitrin, A., Stark, D. P., Mainali, R., & Roberts-Borsani, G. W. 2017, ApJ, 851, 40

2017
[66]

2010, ApJ, 718, 199

Lidz, A., Faucher-Giguere, C.-A., Dall’Aglio, A., et al. 2010, ApJ, 718, 199

2010
[67]

C., Finkelstein, S

Livermore, R. C., Finkelstein, S. L., Lotz, J. M. 2017, ApJ, 835, 113

2017
[68]

E., Brandt, W

Luo, B., Bauer, F. E., Brandt, W. N., et al. 2008, ApJS 179, 19

2008
[69]

N., Xue, Y

Luo, B., Brandt, W. N., Xue, Y. Q. et al. 2017, ApJS, 228, 2

2017
[70]

2010, A&A, 512, 34

Lusso, E., Comastri, A., Vignali, C., et al. 2010, A&A, 512, 34

2010
[71]

F., Prochaska, J

Lusso, E., Worseck, G., Hennawi, J. F., Prochaska, J. X., Vignali, C., Stern, J., O'Meara, J. M. 2015, MNRAS, 449, 4204

2015
[72]

& Haardt, F

Madau, P. & Haardt, F. 2015, ApJ, 813, L8

2015
[73]

Mainali, R., Zitrin, A., Stark, D. P. et al. 2018, MNRAS, 479, 1180

2018
[74]

Marchesi, S., Civano, F., Elvis, M. et al. 2016, ApJ, 817, 23

2016
[75]

2017, A&A 601, 73

Marchi, F., Pentericci, L., Guaita, L., et al. 2017, A&A 601, 73

2017
[76]

2018, A&A, 615, 171

Marin, F. 2018, A&A, 615, 171

2018
[77]

A., Kashikawa, N

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

2018
[78]

Masters, D., Capak, P., Salvato, M. et al. 2012, ApJ, 755, 169

2012
[79]

D., Fan, X., Jiang, L, Cai, Z

McGreer, I. D., Fan, X., Jiang, L, Cai, Z. 2018, AJ, 155, 131

2018
[80]

Merloni, A., Bongiorno, A., Brusa, M. et al. 2014, MNRAS, 437, 3550

2014
[81]

M., Syphers, D., Kim, T.-S

Morrison, S., Pieri, M. M., Syphers, D., Kim, T.-S. 2019, MNRAS submitted, arXiv:1903.04510

2019 arXiv
[82]

J., Momjian, E., Condon, J

Murphy, E. J., Momjian, E., Condon, J. J., Chary, R-R., Dickinson, M., Inami, H., Taylor, A. R., Weiner, B. J. 2017, ApJ, 839, 35

2017
[83]

P., Forrset, B., Oesch, P

Naidu, R. P., Forrset, B., Oesch, P. A., Tran, K. H., Bradford, P. H. 2018, MNRAS, 478, 791

2018
[84]

S., Aird, J

Nandra, K., Laird, E. S., Aird, J. A. et al. 2015, ApJS, 220, 10

2015
[85]

J., et al

Onoue, M., Kashikawa, N., Willott, C. J., et al. 2017, ApJL, 847, L15

2017
[86]

S., & McLure R

Parsa, S., Dunlop, J. S., & McLure R. J. 2018, MNRAS, 474, 2904

2018
[87]

Pentericci, L., Vanzella, E., Fontana, A. et al. 2014, ApJ 793, 113,

2014
[88]

Planck collaboration; Aghanim, N. et al. 2018, arXiv:1807.06209

2018 arXiv
[89]

X., Worseck, G., & O'Meara, J

Prochaska, J. X., Worseck, G., & O'Meara, J. M. 2009, ApJ, 705, L113

2009
[90]

G., & Madau, P

Puchwein, E., Haardt, F., Haehnelt, M. G., & Madau, P. 2018, MNRAS, 485, 47

2018
[91]

2003, A&A, 399, 39

Ranalli, P., Comastri, A., Setti, G. 2003, A&A, 399, 39

2003
[92]

T., Hall, P

Richards, G. T., Hall, P. B., Vanden Berk, D. E. et al. 2003, AJ, 126 1131

2003
[93]

T., Strauss, M

Richards, G. T., Strauss, M. A., Fan, X. et al. 2006, AJ, 131 2766

2006
[94]

C., Fontana, A., et al

Santini, P., Ferguson, H. C., Fontana, A., et al. 2015, ApJ, 801, 97

2015
[95]

Schirber, M., & Bullock, J. S. 2003, , 584, 110

2003
[96]

1968, ApJ, 151, 393

Schmidt, M. 1968, ApJ, 151, 393

1968
[97]

C., Strom, A

Shapley, A., Steidel, C. C., Strom, A. L., Bogosavljevi \'c , M., Reddy, N. A., Siana, B., Mostradi, R. E., Rudie, G. C. 2016, ApJ, 826, L24

2016
[98]

Schindler, J-T., Fan, X., McGreer, I. D. et al. 2018, ApJ, 863, 144

2018
[99]

E., Kulas, K

Siana, B., Shapley, A. E., Kulas, K. R., et al. 2015, ApJ, 804, 17

2015
[100]

Sobral, D., Matthee, J., Brammer, G. et al. 2019, MNRAS, 482, 2422

2019
[101]

Songaila, A., & Cowie, L. L. 2010, ApJ, 721, 1448

2010
[102]

Stefanon, M., Yan, H., Mobasher, B. et al. 2017, ApJS, 229, 32

2017
[103]

C., Hunt, M

Steidel, C. C., Hunt, M. P., Shapley, A. E., Adelberger, K. L., Pettini, M., Dickinson, M., Giavalisco, M. 2002, ApJ, 576, 653

2002
[104]

C., Bogoslavljevi \'c , M., Shapley, A

, Steidel, C. C., Bogoslavljevi \'c , M., Shapley, A. E., Reddy, N. A., Rudie, G. C., Pettini, M., Trainor, R. F., Strom, A. L. 2018, arXiv:1805.06071v1

2018 arXiv
[105]

L., Shull, J

Stevans, M. L., Shull, J. M., Danforth, C. W., & Tilton, E. M. 2014, ApJ, 794, 75

2014
[106]

R., Fynbo, J

Tanvir, N. R., Fynbo, J. P. U., de Ugarte Postigo, A. et al. 2018, arXiv:1805.07318v1

2018 arXiv
[107]

C., Zheng, W

Telfer, R. C., Zheng, W. K., Gerard, A., & Davidsen, A. F. 2002, ApJ, 565, 773

2002
[108]

Tilvi, V., Pirzkal, N., Malhotra, S. et al. 2016, ApJ, 827, L14

2016
[109]

Urrutia, T., Wisotzki, L., Kerrut, J. et al. 2018, arXiv:181106549

2018
[110]

E., Richards, G

Vanden Berk, D. E., Richards, G. T., Bauer, A. et al. 2001, AJ, 122, 549

2001
[111]

Vanzella, E., Cristiani, S., Dickinson, M. et al. 2008, A&A, 478, 83

2008
[112]

2010, MNRAS, 404, 1672

Vanzella, E., Siana, B., Cristiani, S., & Nonino, M. 2010, MNRAS, 404, 1672

2010
[113]

Vanzella, E., de Barros, S., Vasei, K. et al. 2016, ApJ, 825, 41

2016
[114]

Vanzella, E., Nonino, M., Cupani, G. et al. 2018, MNRAS, in press

2018
[115]

N., Yang G., et al

Vito, F., Brandt, W. N., Yang G., et al. 2018, MNRAS, 473, 2378

2018
[116]

A., Cohen, S

Waddington, I., Windhorst, R. A., Cohen, S. H., Partridge, R. B., Spinrad, H., Stern D. 1999, ApJ, 526, L77

1999
[117]

2011, MNRAS, 412, 1926

Wyithe, J.S.B., & Bolton, J.S. 2011, MNRAS, 412, 1926

2011
[118]

X., O’Meara, J

Worseck, G., Prochaska, J. X., O’Meara, J. M., Becker, G. D., et al. 2014, MNRAS, 445, 1745

2014
[119]

X., Hennawi, J

Worseck, G., Prochaska, J. X., Hennawi, J. F., & Mcquinn, M. 2016, ApJ, 825, 144

2016
[120]

Q., Luo, B., Brandt, W

Xue, Y. Q., Luo, B., Brandt, W. N., et al. 2011, ApJS, 195, 10

2011
[121]

Q., Luo, B., Brandt, W

Xue, Y. Q., Luo, B., Brandt, W. N., Alexander, D. M., Bauer, F. E., Lehmer, B. D., Yang, G. 2016, ApJS, 224, 15

2016

Pith tools

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[1] [1]

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2019

[2] [2]

2018, PASJ, 70, 34

Akiyama, M., He, W., Ikeda, H., et al. 2018, PASJ, 70, 34

2018

[3] [3]

M., Bauer, F

Alexander, D. M., Bauer, F. E., Brandt, W. N. et al. 2003, AJ, 126, 539

2003

[4] [4]

Ashby, M. L. N., Willner, S. P., Fazio, G. G., et al. 2013, ApJ, 769, 80

2013

[5] [5]

2018, arXiv:1803.09747

Atek, H., Richard, J., Kneib, J.-P., & Schaerer, D. 2018, arXiv:1803.09747

arXiv 2018

[6] [6]

Avni, Y., & Bahcall, J. N. 1980, ApJ, 235, 694

1980

[7] [7]

Bacon, R., Brau-Nogu\' e , S., Caillier, P. et al. 2009, ASSP, 9, 331

2009

[8] [8]

Balestra, I, Mainieri, V., Popesso, P. et al. 2010, A&A, 512, 12

2010