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Exploring galaxy dark matter halos across redshifts with strong quasar absorbers

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Damped Lyman-α absorbers trace their host halos' dark matter potentials, with velocity-width ratios favoring steep inner density profiles over shallow ones.

desk verdict Interesting first attempt to turn DLA line widths into a dark-matter profile probe, but the steep-cusp preference rests on a loose Δv90≈σlos identification and per-model normalization, so the model ranking is not yet a measurement. read the letter →

arxiv 1908.05363 v1 pith:JHO7FOQY submitted 2019-08-14 astro-ph.GA

L. Christensen , P. M{o}ller , N. H. P. Rhodin , K. E. Heintz , J. P. U. Fynbo This is my paper · ORCID
classification astro-ph.GA
keywords DampedLyman-alphaabsorbersquasarabsorptionlinesdarkmatterhaloprofilesgalaxyvelocitydispersionimpactparametercircumgalacticmediumhigh-redshiftgalaxiesabundancematching
topics Dark Matter
open problems Dark Matter
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper tries to establish that the widths of quasar absorption lines in damped Lyman-α systems (DLAs) trace the gravitational potential of the host galaxy's dark matter halo along a single line of sight. Normalizing the absorption velocity width $\Delta v_{90}$ by the host galaxy's emission-line velocity dispersion $\sigma_{\rm em}$ removes the stellar-mass dependence, so the ratio can be compared with the projected line-of-sight velocity dispersions $\sigma_{\rm los}(R)$ predicted by different dark matter halo density profiles. The paper reports that $\Delta v_{90}/\sigma_{\rm em}$ decreases with impact parameter and is better matched by models with steep inner density slopes (Dehnen, Jaffe, Einasto) than by shallower profiles such as NFW and isothermal, although the scatter is large. If correct, the result turns absorption-line surveys into a redshift-resolved probe of the inner structure of dark matter halos, and it implies that most DLA gas remains gravitationally bound to its host halo.

What carries the argument

The central object is the mapping $\Delta v_{90}\approx\sigma_{\rm los}(R)$, where the absorption-line velocity width is treated as the line-of-sight velocity dispersion of a spherical dark matter halo at the projected impact parameter $R$. The machine starts with the Jeans equation and expresses $\sigma_{\rm los}(R)$ as an integral over the halo density profile, parameterized as a double power law $\rho(r)=\rho_0\,(r/r_s)^{-\gamma}[1+(r/r_s)^\alpha]^{-(\beta-\gamma)/\alpha}$, whose special cases are the Hernquist, Jaffe, Plummer, NFW, and isothermal profiles, plus the Dehnen and Einasto families. The comparison is placed on a common footing by normalizing each DLA's $\Delta v_{90}$ by its host's $\sigma_{\rm em}$ and each impact parameter by the halo scale radius $r_s$ computed from abundance-matching halo masses, so that galaxies spanning nearly three orders of magnitude in stellar mass can be compared with one predicted profile at a time. The ratio $\Delta v_{90}/V_{\rm vir}$ with virial velocities computed from $V_{\rm vir}^3=10GM_{\rm halo}H(z)$ provides a second, simulation-based comparison.

What would settle it

Measure $\Delta v_{90}$ for a DLA with several quasar sightlines through the same halo, for example a gravitationally lensed quasar or a close quasar pair, and check whether the per-sightline widths follow the $\sigma_{\rm los}(R)$ curve of a single spherical halo at the corresponding impact parameters; if the widths are set by outflow or unbound gas, they will scatter independently of $R$ and the steep-profile preference will disappear. A more direct test is to compare a DLA host with a measured rotation curve at $z\sim0.7$: the observed $\Delta v_{90}$ at the impact parameter should equal the $\sigma_{\rm los}$ computed from the rotation curve, not exceed it by the outflow velocity scale.

Watch

Extended reading notes

Core claim

Using a sample of 21 DLAs at $0.2<z<3.2$ with spectroscopically confirmed host galaxies, the paper finds that the normalized velocity ratio $\Delta v_{90}/\sigma_{\rm em}$ declines as the projected separation between the quasar sightline and the host galaxy increases. When impact parameters are scaled by the halo scale radii $r_s$ derived from abundance matching, the observed trend is best reproduced by the line-of-sight velocity dispersion profiles of dark matter halos with steep inner density profiles — the Dehnen profile with $\gamma=2.75$, Jaffe, and Einasto — rather than by the shallower NFW or isothermal profiles. The paper also compares $\Delta v_{90}/V_{\rm vir}$ for 26 DLAs with known halo masses to simulated DLAs at $z=3$, finding a Kolmogorov-Smirnov probability $P=0.98$ that the two distributions are drawn from the same parent distribution. Finally, comparing absorption–emission redshift offsets with the escape velocity at each impact parameter, it concludes that 23 of 26 DLA systems are gravitationally bound to their host halos, with only three systems marginally exceeding the escape velocity.

Load-bearing premise

Everything rests on identifying the measured absorption-line width $\Delta v_{90}$ with the line-of-sight velocity dispersion of a smooth, spherical dark matter halo at the impact parameter, an approximation calibrated only by comparing median values; if $\Delta v_{90}$ is significantly inflated by outflows, turbulence, or unrelated gas clouds, the comparison between data and halo models collapses.

Editorial extensions

If this is right

  • The decline of $\Delta v_{90}/\sigma_{\rm em}$ with impact parameter means single quasar sightlines can be stacked to map the shape of the gravitational potential of typical $z\sim1$ galaxies.
  • If the steeper Dehnen, Jaffe, and Einasto profiles really fit better, the inner regions of these halos are cuspy rather than cored, contradicting isothermal expectations at the probed halo masses.
  • The match between observed and simulated $\Delta v_{90}/V_{\rm vir}$ distributions implies that DLA gas moves in halos whose potentials reflect baryonic feedback, not just collisionless dark matter.
  • Because 23 of 26 systems have relative velocities below the local escape velocity, most DLA gas, including gas in outflows, remains bound to its host halo.

Reading between the lines

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

  • If the $\Delta v_{90}\approx\sigma_{\rm los}$ mapping holds, the same ratio applied to sub-DLAs and low-ionization Mg II absorbers would enlarge the sample enough to separate halo-profile shape from outflow-driven scatter.
  • A direct cross-check would come from gravitational lensing: lensing measures enclosed mass at the impact parameter while $\Delta v_{90}/\sigma_{\rm em}$ measures the dynamical state, so matched samples would test whether the inferred steep inner slopes survive without abundance-matching halo masses.
  • The mapping predicts that $\Delta v_{90}$ should not correlate with star-formation rate once $\sigma_{\rm em}$ and impact parameter are fixed; if such a correlation appears, the width is partly tracing outflows rather than the potential.
  • Above $z\approx3$, where the stellar-mass Tully-Fisher relation breaks down, the $\sigma_{\rm em}$ normalization probably fails, so future samples should use a mass estimator independent of emission-line dispersion.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper compiles a sample of 21 damped Lyman-alpha absorbers (DLAs) with spectroscopically confirmed host galaxies over 0.2 < z < 3.2, and studies how the ratio of the DLA velocity width Δv90 to the host galaxy emission-line velocity dispersion σem varies with projected impact parameter. The authors report that Δv90/σem decreases with impact parameter, and they compare the data with line-of-sight velocity dispersion profiles σlos(R) computed from a suite of dark matter halo density profiles (NFW, isothermal, Hernquist, Jaffe, Plummer, Dehnen, Einasto). The models are vertically normalized to minimize χ², and the paper concludes that steeper inner profiles (Dehnen, Jaffe, Einasto) fit the data better than shallower ones (NFW, isothermal), while cautioning that the scatter is large. The paper also compares Δv90 to halo virial velocities in numerical simulations, finding good agreement with a KS test, and argues that most DLAs are gravitationally bound to their host halos by comparing relative velocities to escape velocities.

Significance. If the central claim holds, the paper would provide a rare observational constraint on dark matter density profiles at intermediate redshifts using absorption-selected galaxies, complementing local kinematic studies. The authors deserve credit for assembling a unique sample of DLA host galaxies with both absorption and emission kinematics, for testing a broad range of standard halo profiles within a simple Jeans framework, and for honestly enumerating the assumptions and failure modes in Sections 6.2 and 6.3. The comparison with the Bird et al. (2015) simulations and the escape-velocity analysis are useful complementary checks. However, the headline conclusion about steeper dark matter profiles is currently not quantitatively established, because the model comparison is a shape-only test with heavily normalized and uniformly poor fits, and because the identification Δv90 ≈ σlos is validated only at the median level rather than as a point-to-point relation.

major comments (4)
  1. [Section 3 / Table 2] The model ranking in Table 2 is a shape-only comparison because every model curve is vertically normalized to minimize χ², as stated in Section 3 and the caption of Figure 1. The reduced χ² values for the scaled comparison in Table 2 range from 127 to 161, meaning no model is a good fit, and the differences between profiles are small relative to the overall badness of fit. The abstract's claim that models with steeper radial profiles 'provide a better fit' is therefore not established as a measurement of the dark matter inner slope; it is a statement about which normalized shape is least bad. The authors should fix the normalization using the absolute σlos calibration from Section 3, or apply a formal model comparison that accounts for the fitted normalization, and they should temper the abstract in line with Section 6.1's own admission that 'the currently known data sample does not allow us to rule out any of the models.'
  2. [Section 3 / Section 6.3] The central identification Δv90 ≈ σlos is validated only at the median level: σlos ~ 100–130 km/s for the median halo mass at r = rs versus a median Δv90 = 141 km/s. The shape comparison in Figures 1 and 2 requires Δv90(R) to track σlos(R) point by point, but Section 6.3 states that this is an assumption, and Section 6.2 lists mechanisms (outflows, group membership, a single dominant component) that can alter Δv90 without changing σlos. Because the dark-matter profile ranking depends entirely on this mapping, the authors should provide a direct test of the shape correspondence, for example by measuring Δv90 and σlos(R) in the Bird et al. (2015) simulations with known impact parameters, or by demonstrating that Δv90 scaled by host properties correlates with the expected σlos at the measured impact parameter.
  3. [Section 2 / Figure 1] The paper asserts that Δv90/σem decreases with impact parameter, but no quantitative significance is reported. Please provide a rank correlation coefficient (e.g., Spearman) with a p-value, and ideally a fit of log(Δv90/σem) versus log(b) with uncertainties. The large scatter and the leverage of a few high-ratio points at small impact parameters (e.g., DLA0918+1636 at b = 2.0 kpc with Δv90/σem ≈ 16) make such a test necessary to establish the observational foundation for the subsequent model comparison.
  4. [Section 4.1 / Section 6.2] The x-axis scaling of the data in Figure 2 depends on halo masses and scale radii derived from abundance matching and concentration–mass relations. Section 6.2 notes that individual halo masses can be underpredicted by up to a factor of 10, which shifts log(b/rs) by about –0.4 dex. This systematic uncertainty is not propagated into the χ² values in Table 2 and could affect the relative ranking of models. The authors should show how the ranking changes when rs is varied within the abundance-matching scatter, or at least discuss the sensitivity of the Table 2 values to these systematic shifts.
minor comments (5)
  1. [Table 1] Several systems lack σem measurements (e.g., 0439–433, 0738+313, 1127–145). Please clarify explicitly which systems enter the Δv90/σem analysis and which enter the virial-velocity comparison, and state the sample size used for each figure.
  2. [Figures 1–3] The data points are plotted without error bars. Please either add representative error bars on Δv90, σem, and impact parameter, or state in the captions the typical uncertainties and why they are omitted.
  3. [Section 5.2] The phrase 'computed from Equation 3 to represent the tangential velocity' appears to refer to the wrong equation, since Equation (3) is the radial velocity dispersion from the Jeans equation; please correct the cross-reference.
  4. [Section 6.2] The text states that outflows increase both Δv90 and σem, which would partly cancel in the ratio Δv90/σem; the abstract's statement that outflows 'may cause an increased scatter' should be reconciled with this cancellation or made more precise.
  5. [Section 4.1] Equations (14) and (15) give different scaling exponents for rs with halo mass and redshift; please state explicitly which concentration–mass relation is used to compute the scale radii in Table 1 and which variant is used in each panel of Figure 2, to avoid ambiguity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the DM-halo profile comparison rests on independent Jeans models, an explicit (not fitted) Δv90≈σlos assumption, and self-citations that are contextual rather than load-bearing.

full rationale

The derivation chain is not circular. Section 3 computes line-of-sight velocity dispersions from standard Jeans equations (Eqs. 1–4) and standard DM density profiles (Eqs. 5–7); these are theoretical inputs that do not use the DLA measurements. The model curves are vertically normalized to minimize χ² (Fig. 1 and Fig. 2 captions, Table 2), so the model-data comparison is a shape comparison rather than an absolute prediction, but the profile shapes are independent of the data and the χ² ranking is not therefore constructed from the data. The calibration Δv90≈σlos is a median-level consistency check (σlos≈100–130 km/s versus median Δv90=141 km/s) using abundance-matching halo masses and the Tully–Fisher relation, not a parameter fitted to the same individual points. Scale radii in Table 1 are derived from SED-based stellar masses, abundance matching (Moster et al. 2013), and concentration-redshift relations (Eqs. 8–17), none of which involve Δv90. Section 5 compares observed Δv90/Vvir with Bird et al. simulations that are not fit to the data, and the paper explicitly notes the agreement could be coincidental. The paper's own Section 6.3 states the key assumption that Δv90 measures the projected velocity dispersion, and Section 6.2 lists outflows, group membership, and single-component contamination as sources of scatter; these are limitations and correctness risks, not circular reductions. The self-citation to Møller & Christensen (2019) supplies sample context and a prior scaling relation, but Table 1 contains the data and the trend is re-examined here, so the citation is not load-bearing. Overall, no step reduces a claimed prediction to an input by construction.

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

The central model comparison rests on standard halo profile theory plus several domain assumptions: the observable Δv90 is equated to the halo's line-of-sight velocity dispersion, halo masses are derived from abundance matching, scale radii come from concentration-mass relations, and the simulated DLAs are treated as comparable despite a mass mismatch. None of these are new entities; they are standard astrophysical assumptions with acknowledged uncertainties.

free parameters (6)
  • Model normalization factor (per dark matter profile) = not reported (set to minimize chi-square)
    In Figs 1 to 3, each dark matter halo model is vertically scaled to provide the smallest chi-square to the data (Section 3, Fig 1 caption, Table 2); this is a free parameter fitted to the observed Δv90/σem points.
  • Uniform scale radius rs = 10 kpc = 10 kpc
    Used for all models in Fig 1 to compare profiles before per-galaxy scaling (Section 3); a hand-chosen value.
  • Einasto index n = 1
    Fixed to n = 1 to represent a relatively steep Einasto profile (Section 3); chosen by hand, not fitted.
  • Dehnen inner slope gamma = 2.75
    Fixed to gamma = 2.75 to represent a very steep inner density profile (Section 3); chosen by hand, not fitted.
  • Concentration c for DLA1009-0026 = c ~ 7
    For the massive host of DLA1009-0026 the concentration-mass relation is inverted; the authors assume c ~ 7 by hand (Section 4.1).
  • Milky Way sigma_em normalization = 90 km/s
    Used to place Milky Way halo star data in the scaled system (Fig 2 caption); arbitrary normalization.
assumptions (6)
  • domain assumption Spherical symmetry and isotropy of the dark matter halo for the Jeans equations
    Equations 1 to 4 assume a spherical, isotropic velocity distribution (Binney & Tremaine 1987; Hernquist 1990). Section 6.2 acknowledges real halos need not be spherical.
  • domain assumption The absorption-line width Δv90 equals the line-of-sight velocity dispersion σlos of the halo
    The models predict σlos, and the paper sets Δv90 ≈ σlos, calibrated only by comparing median values (Section 3). This is the key mapping between observable and theory.
  • domain assumption Halo masses from abundance matching (Moster et al. 2013)
    Halo masses in Table 1 are computed from stellar masses via abundance matching (Section 4.1); per-object halo masses can be uncertain by a factor of 10 (Leauthaud et al. 2012, cited in Sec 6.2).
  • domain assumption Concentration-mass relations (Mo & Mao 2004; Klypin et al. 2011) with redshift evolution
    Equations 12 to 17 use these relations to compute scale radii and their redshift dependence; the paper notes differences between the two relations are insignificant relative to data scatter.
  • domain assumption Virial velocity definition V_vir^3 = 10 G M_halo H(z)
    Equation 18 assumes the circular velocity at r_vir = r_200 equals the virial velocity, standard in halo definitions.
  • domain assumption Simulations of Bird et al. (2015) are comparable to the observed DLA sample
    The KS test compares observed Δv90/V_vir to simulated DLAs at z = 3, but the observed sample has much higher median V_vir (145 vs 70 km/s); the paper acknowledges the match may be coincidental (Section 5.1).

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Pith. "Pith review of Exploring galaxy dark matter halos across redshifts with strong quasar absorbers." pith.science (2026). https://pith.science/paper/JHO7FOQY

@misc{pith2026190805363,
  author       = {Pith},
  title        = {Pith review of: Exploring galaxy dark matter halos across redshifts with strong quasar absorbers},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JHO7FOQY}},
  note         = {Machine review of arXiv:1908.05363}
}
abstract

Quasar lines of sight intersect intervening galaxy discs or circum-galactic environments at random impact parameters and potential well depths. Absorption line velocity widths ($\Delta v_{90}$) are known to scale with host galaxy stellar masses, and inversely with the projected separation from the quasar line of sight. Its dependence on stellar mass can be eliminated by normalising with the emission-line widths of the host galaxies, $\sigma_{em}$, so that absorbers with a range of $\Delta v_{90}$ values can be compared directly. Using a sample of DLA systems at 0.2 < z < 3.2 with spectroscopically confirmed host galaxies, we find that the velocity ratio $\Delta v_{90}/\sigma_{em}$ decreases with projected distances from the hosts. We compare the data with expectations of line-of-sight velocity dispersions derived for different dark matter halo mass distributions, and find that models with steeper radial dark matter profiles provide a better fit to the observations, although the scatter remains large. Gas outflows from the galaxies may cause an increased scatter, or scale radii of dark matter halo models may not be representative for the galaxies. We demonstrate by computing virial velocities, that metal-rich DLAs that belong to massive galaxy halos (M$_{halo} \approx 10^{12}$ M$_{\odot}$) mostly remain gravitationally bound to the halos.

Figures

Figures reproduced from arXiv: 1908.05363 by the authors.

Figure 1
Figure 1. Measured velocities for DLAs and their host galaxies as a func￾tion of impact parameters. The lines represent σlos from various DM mass distribution profiles. Based on computed real values from the models we argue that ∆v90≈ σlos. All models have a scale radius of 10 kpc, and are normalised to provide the smallest χ 2 with respect to the data. ney & Tremaine 1987). Other special cases of DM profiles are sug￾gested b… view at source ↗
Figure 2
Figure 2. Plot of the data versus line of sight velocity dispersions for differ￾ent DM profiles. All data point are scaled in the x-axis with their scale radii. The upper panel ignores the change of the scale radii with redshifts and as￾sumes z = 0 using equation 15. All models have rs = 1 kpc and have been normalised to provide the minimum chi square residuals with respect to the DLA data points. The grey line presents the r… view at source ↗
Figure 3
Figure 3. When adding a massive disc component, there is a large [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 5
Figure 5. Figure 5: The impact parameter as a function of the velocity ratios ∆v90/Vvir suggests a dependence that the higher fractions are only found at small radial separations from the host galaxies. shifts with known impact parameters from simulated galaxies that better match the obse…
Figure 4
Figure 4. Figure 4: The top panel shows a histogram of the fractional velocities for 26 DLAs with known stellar- and halo masses, versus simulated DLAs at z = 3 in Bird et al. (2015) illustrated by the orange curve. The models are not fit to the data, but simply scaled, and the vertical e…
Figure 6
Figure 6. Figure 6: Relative velocity offset between absorption and emission red￾shifts compared to the computed escape velocities from a Hernquist mass￾distribution model at the position equal to the impact parameter. The data points that lie above and below the straight dashed lines cor…

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Works this paper leans on

71 extracted references · 62 canonical work pages

  1. [1]

    2015, MNRAS, 446, 990

    Freudling, W., Savaglio, S., & Zafar, T. 2015, MNRAS, 446, 990

    work page 2015

  2. [2]

    2018, MNRAS, 478, 3120

    Augustin, R., et al. 2018, MNRAS, 478, 3120

    work page 2018

  3. [3]

    2005, MNRAS, 364, 433

    Battaglia, G., et al. 2005, MNRAS, 364, 433

    work page 2005

  4. [4]

    1987, Galactic dynamics

    Binney, J., & Tremaine, S. 1987, Galactic dynamics

    1987

  5. [5]

    2015, MNRAS, 447, 1834 Bouch´ e, N., Murphy, M

    Bird, S., Haehnelt, M., Neeleman, M., Genel, S., V ogelsberg er, M., & Hernquist, L. 2015, MNRAS, 447, 1834 Bouch´ e, N., Murphy, M. T., Kacprzak, G. G., P´ eroux, C., Con - tini, T., Martin, C. L., & Dessauges-Zavadsky, M. 2013, Science, 341, 50

    work page 2015

  6. [6]

    L., & Norman, M

    Bryan, G. L., & Norman, M. L. 1998, ApJ, 495, 80

    1998

  7. [7]

    D., Zahedy, F

    Chen, H.-W., Boettcher, E., Johnson, S. D., Zahedy, F. S., Ru die, G. C., Cooksey, K. L., Rauch, M., & Mulchaey, J. S. 2019, ApJ, 878, L33

    work page 2019

  8. [8]

    C., Jr., & Rauch, M

    Chen, H.-W., Kennicutt, R. C., Jr., & Rauch, M. 2005, ApJ, 620 , 703

    work page 2005

Show all 71 references
  1. [9]

    2017, MNRAS, 470, 2599

    Christensen, L., & Hjorth, J. 2017, MNRAS, 470, 2599

    2017

  2. [10]

    Christensen, L., Møller, P ., Fynbo, J. P . U., & Zafar, T. 2014, MN- RAS, 445, 225

    2014

  3. [11]

    E., S´ anchez, S

    Christensen, L., Schulte-Ladbeck, R. E., S´ anchez, S. F., B ecker, T., Jahnke, K., Kelz, A., Roth, M. M., & Wisotzki, L. 2005, A&A, 429, 477

    2005

  4. [12]

    Crighton, N. H. M., et al. 2015, MNRAS, 452, 217 De Cia, A., Ledoux, C., Petitjean, P ., & Savaglio, S. 2018, A& A, 611, A76

    2015

  5. [13]

    1993, MNRAS, 265, 250

    Dehnen, W. 1993, MNRAS, 265, 250

    1993

  6. [14]

    S., D’Odorico, S., & McMahon, R

    Dessauges-Zavadsky, M., P´ eroux, C., Kim, T. S., D’Odorico, S., & McMahon, R. G. 2003, MNRAS, 345, 447

    2003

  7. [15]

    1965, Trudy Astrofizicheskogo Instituta Alma-A ta, 5, 87

    Einasto, J. 1965, Trudy Astrofizicheskogo Instituta Alma-A ta, 5, 87

    1965

  8. [16]

    L., Hennawi, J

    Ellison, S. L., Hennawi, J. F., Martin, C. L., & Sommer-Larse n, J. 2007, MNRAS, 378, 801

    2007

  9. [17]

    L., Kewley, L

    Ellison, S. L., Kewley, L. J., & Mall´ en-Ornelas, G. 2005, MN - RAS, 357, 354

    2005

  10. [18]

    Fynbo, J. P . U., et al. 2013, MNRAS, 436, 361

    2013

  11. [19]

    Fynbo, J. P . U., et al. 2018, MNRAS, 479, 2126

    2018

  12. [20]

    Fynbo, J. P . U., et al. 2010, MNRAS, 408, 2128

    2010

  13. [21]

    1986, ApJ, 303, 336

    Gehrels, N. 1986, ApJ, 303, 336

    1986

  14. [22]

    G., Steinmetz, M., & Rauch, M

    Haehnelt, M. G., Steinmetz, M., & Rauch, M. 1998, ApJ, 495, 647

    1998

  15. [23]

    1990, ApJ, 356, 359

    Hernquist, L. 1990, ApJ, 356, 359

    1990

  16. [24]

    P ., Giovanelli, R., & Brinchmann, J

    Huang, S., Haynes, M. P ., Giovanelli, R., & Brinchmann, J. 2012, ApJ, 756, 113

    2012

  17. [25]

    1983, MNRAS, 202, 995

    Jaffe, W. 1983, MNRAS, 202, 995

    1983

  18. [26]

    G., Murphy, M

    Kacprzak, G. G., Murphy, M. T., & Churchill, C. W. 2010, MN- RAS, 406, 445

    2010

  19. [27]

    X., & Ghosh, T

    Kanekar, N., Neeleman, M., Prochaska, J. X., & Ghosh, T. 2018 a, MNRAS, 473, L54

    2018

  20. [28]

    2014, MNRAS, 438, 2131 c© 0000 RAS, MNRAS 000, 000–000 10 Christensen, L

    Kanekar, N., et al. 2014, MNRAS, 438, 2131 c© 0000 RAS, MNRAS 000, 000–000 10 Christensen, L. et al

    2014

  21. [29]

    A., et al

    Kassin, S. A., et al. 2007, ApJ, 660, L35

    2007

  22. [30]

    A., Trujillo-Gomez, S., & Primack, J

    Klypin, A. A., Trujillo-Gomez, S., & Primack, J. 2011, ApJ, 7 40, 102

    2011

  23. [31]

    2011, ApJS, 192, 18

    Komatsu, E., et al. 2011, ApJS, 192, 18

    2011

  24. [32]

    2013, MNRAS, 433, 3091

    Krogager, J.-K., et al. 2013, MNRAS, 433, 3091

    2013

  25. [33]

    Krogager, J.-K., Fynbo, J. P . U., Noterdaeme, P ., Zafar, T.,Møller, P ., Ledoux, C., Kr¨ uhler, T., & Stockton, A. 2016, MNRAS, 455, 2698

    2016

  26. [34]

    Krogager, J.-K., Møller, P ., Fynbo, J. P . U., & Noterdaeme, P . 2017, MNRAS, 469, 2959 Kr¨ uhler, T., et al. 2015, A&A, 581, A125

    2017

  27. [35]

    Khare, P ., & Lauroesch, J. T. 2012, ApJ, 749, 176

    2012

  28. [36]

    2012, ApJ, 744, 159

    Leauthaud, A., et al. 2012, ApJ, 744, 159

    2012

  29. [37]

    Ledoux, C., Petitjean, P ., Fynbo, J. P . U., Møller, P ., & Srianand, R. 2006, A&A, 457, 71

    2006

  30. [38]

    C., & Newman, J

    Licquia, T. C., & Newman, J. A. 2015, ApJ, 806, 96

    2015

  31. [39]

    2008, A&A, 488, 463

    Maiolino, R., et al. 2008, A&A, 488, 463

    2008

  32. [40]

    D., Lauroesch, J

    Meiring, J. D., Lauroesch, J. T., Kulkarni, V . P ., P´ eroux, C., Khare, P ., & Y ork, D. G. 2009, MNRAS, 397, 2037

    2009

  33. [41]

    1987, ApJ, 313, 121

    Merritt, D. 1987, ApJ, 313, 121

    1987

  34. [42]

    J., & Mao, S

    Mo, H. J., & Mao, S. 2004, MNRAS, 353, 829

    2004

  35. [43]

    J., Mao, S., & White, S

    Mo, H. J., Mao, S., & White, S. D. M. 1998, MNRAS, 295, 319 Møller, P ., & Christensen, L. 2019, MNRAS submitted Møller, P ., et al. 2018, MNRAS, 474, 4039 Møller, P ., Fynbo, J. P . U., Ledoux, C., & Nilsson, K. K. 2013, MNRAS, 430, 2680

    1998

  36. [44]

    P ., Naab, T., & White, S

    Moster, B. P ., Naab, T., & White, S. D. M. 2013, MNRAS, 428, 3121

    2013

  37. [45]

    F., Frenk, C

    Navarro, J. F., Frenk, C. S., & White, S. D. M. 1997, ApJ, 490, 493

    1997

  38. [46]

    Dessauges-Zavadsky, M., Fynbo, J. P . U., Møller, P ., & Zwaan, M. A. 2018, ApJ, 856, L12

    2018

  39. [47]

    2016, ApJ, 820, L39

    Neeleman, M., et al. 2016, ApJ, 820, L39

    2016

  40. [48]

    M., Prochaska, J

    Neeleman, M., Wolfe, A. M., Prochaska, J. X., & Rafelski, M. 2013, ApJ, 769, 54

    2013

  41. [49]

    2018, MNRAS, 480, 4702 P´ eroux, C., Bouch´ e, N., Kulkarni, V

    Ribera, A., & Mas-Ribas, L. 2018, MNRAS, 480, 4702 P´ eroux, C., Bouch´ e, N., Kulkarni, V . P ., & Y ork, D. G. 2013,MN- RAS, 436, 2650 P´ eroux, C., Bouch´ e, N., Kulkarni, V . P ., Y ork, D. G., & Vladilo, G. 2011a, MNRAS, 410, 2237 P´ eroux, C., Bouch´ e, N., Kulkarni, V ....

    2018

  42. [50]

    Plummer, H. C. 1911, MNRAS, 71, 460

    1911

  43. [51]

    2008, MNRAS, 390, 1349

    Pontzen, A., et al. 2008, MNRAS, 390, 1349

    2008

  44. [52]

    2014, MNRA S, 440, 610

    Posti, L., Nipoti, C., Stiavelli, M., & Ciotti, L. 2014, MNRA S, 440, 610

    2014

  45. [53]

    Zavadsky, M., & Bloom, J. S. 2008, ApJ, 672, 59

    2008

  46. [54]

    X., Gawiser, E., Wolfe, A

    Prochaska, J. X., Gawiser, E., Wolfe, A. M., Castro, S., & Djo r- govski, S. G. 2003, ApJ, 595, L9

    2003

  47. [55]

    X., & Wolfe, A

    Prochaska, J. X., & Wolfe, A. M. 1997, ApJ, 487, 73

    1997

  48. [56]

    Prochaska, J. X. 2014, ApJ, 782, L29

    2014

  49. [57]

    2018, MNRAS, 480, 5046

    Rahmani, H., et al. 2018, MNRAS, 480, 5046

    2018

  50. [58]

    2014, MNRAS, 438, 529

    Rahmati, A., & Schaye, J. 2014, MNRAS, 438, 529

    2014

  51. [59]

    Rhodin, N. H. P ., Christensen, L., Møller, P ., Zafar, T., & Fy nbo, J. P . U. 2018, A&A, 618, A129

    2018

  52. [60]

    C., Newman, A

    Rudie, G. C., Newman, A. B., & Murphy, M. T. 2017, ApJ, 843, 98 S´ anchez-Ram´ ırez, R., et al. 2016, MNRAS, 456, 4488

    2017

  53. [61]

    T., Aller, M

    Lauroesch, J. T., Aller, M. C., & Khare, P . 2015, ApJ, 806, 25

    2015

  54. [62]

    2016, MNRAS, 460,634

    Srianand, R., Hussain, T., Noterdaeme, P ., Petitjean, P ., K r¨ uhler, T., Japelj, J., Pˆ aris, I., & Kashikawa, N. 2016, MNRAS, 460,634

    2016

  55. [63]

    A., et al

    Tremonti, C. A., et al. 2004, ApJ, 613, 898

    2004

  56. [64]

    J., Warren, S

    Weatherley, S. J., Warren, S. J., Møller, P ., Fall, S. M., Fyn bo, J. U., & Croom, S. M. 2005, MNRAS, 358, 985

    2005

  57. [65]

    L., Mamon, G

    Wojtak, R., Łokas, E. L., Mamon, G. A., & Gottl¨ ober, S. 2009, MNRAS, 399, 812

    2009

  58. [66]

    M., Gawiser, E., & Prochaska, J

    Wolfe, A. M., Gawiser, E., & Prochaska, J. X. 2005, ARA&A, 43, 861

    2005

  59. [67]

    M., & Prochaska, J

    Wolfe, A. M., & Prochaska, J. X. 1998, ApJ, 494, L15

    1998

  60. [68]

    M., Turnshek, D

    Wolfe, A. M., Turnshek, D. A., Smith, H. E., & Cohen, R. D. 1986, ApJS, 61, 249

    1986

  61. [69]

    2017, MNRAS, 465, 1613

    Ledoux, C., & Deharveng, J.-M. 2017, MNRAS, 465, 1613

    2017

  62. [70]

    2013, A&A, 556, A141

    Zafar, T., P´ eroux, C., Popping, A., Milliard, B., Deharveng, J.-M., & Frank, S. 2013, A&A, 556, A141

    2013

  63. [71]

    H., Jing, Y

    Zhao, D. H., Jing, Y . P ., Mo, H. J., & B¨ orner, G. 2009, ApJ, 707, 354 c© 0000 RAS, MNRAS 000, 000–000

    2009

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