arxiv: 2605.12188 · v1 · submitted 2026-05-12 · 🌌 astro-ph.GA

Recognition: no theorem link

Proximate damped Lyman-α systems as tracers of quasar feedback

Patrick Petitjean

Authors on Pith no claims yet

Pith reviewed 2026-05-13 04:21 UTC · model grok-4.3

classification 🌌 astro-ph.GA

keywords quasarsdamped Lyman-alpha absorbersAGN feedbackinflowsoutflowsproximate absorberscold gas

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The pith

Proximate damped Lyman-alpha absorbers show cold gas cycling through inflows and outflows around quasars.

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

The paper classifies absorption systems close in redshift to quasars into types such as Ghostly, coronagraphic DLAs, standard DLAs, and sub-DLAs using features like Lyman-alpha strength, partial covering, and excited transitions like SiII*. These classes form a sequence with decreasing strengths in certain lines and dust content. Velocity distributions indicate that standard DLAs sit near the quasar redshift while Ghostly and excited systems spread to outflows of 2000 km/s and inflows of 1200 km/s. Stacked spectra point to dense, compact gas that only partially covers the quasar emission regions. The overall picture supports cold dense clouds moving in a dynamic inflow-outflow cycle near the quasar.

Core claim

Proximate absorption systems at the quasar redshift include Ghostly and coronagraphic classes that display strong excited species absorption and partial covering. Their kinematics show multiple components, with standard DLAs clustered within 1000 km/s of systemic velocity while Ghostly and SiII*-bearing systems extend to broad outflows reaching -2000 km/s and inflows up to +1200 km/s. Median stacked spectra confirm that the Ghostly and coronagraphic systems arise in dense compact gas. These observations indicate that cold dense clouds participate in a dynamic cycle of inflow and outflow in the vicinity of quasars.

What carries the argument

Classification of proximate absorption systems (ProxSys) into Ghostly, DLA-Cor, standard DLA, and sub-DLA types based on Lyman-alpha absorption strength, partial covering signatures, and presence of excited transitions such as SiII*, together with their velocity offsets from the quasar systemic redshift.

Load-bearing premise

The identified absorption systems are physically associated with the quasar and its feedback processes rather than being unrelated intervening absorbers.

What would settle it

High-resolution spectra showing that Ghostly and SiII*-bearing systems lack the partial covering signatures and excited absorption strengths reported here, or that their velocity distributions contain no net population of outflows beyond 1000 km/s.

Figures

Figures reproduced from arXiv: 2605.12188 by Patrick Petitjean.

**Figure 1.** Figure 1: Distributions of the H i Lymanα absorption line equivalent width for HighWSys (blue), LowWSys (orange), VeryLowWSys (yellow) and DLA-Cor (green) as defined in the text. We checked whether we recover the Ghostly DLAs detected by Fathivavsari (2020). Out of the 89 systems, we recover 58. Of the 31 missing systems, seven (7) are misidentified Ghostly DLAs, nine (9) are questionable (including three with … view at source ↗

**Figure 3.** Figure 3: Equivalent width of the Lyman-α trough measured for our systems versus the logarithm of the H i column density as measured by Chabanier et al. (2022). The blue, orange and red points correspond to High, Low and Verylow-W systems [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Distributions of the H i column densities for High- (blue) and Low- (orange) Wsyst for the 770 systems common to our and Chabanier’s samples. The fact that we recover 710 systems from the Chabanier’s catalogue implies that we recover a significant fraction (>20%) of bona-fide PDLAs. 3.2. Distribution with redshift In [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗

**Figure 2.** Figure 2: Distributions of the velocity difference between the system and the quasar for the HighWSys ProxSys (blue) and the PDLAs in Chabanier et al. (2022) (green). In [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 5.** Figure 5: Ratio of the number of PDLAs in Chabanier et al. (2022) [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗

**Figure 6.** Figure 6: Median spectra of the Ghost+Ghost? (green), HighWSys (blue) and HighWSys with Si ii∗ (orange) [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗

**Figure 7.** Figure 7: Portions of the normalised median spectra of HighWSys [PITH_FULL_IMAGE:figures/full_fig_p006_7.png] view at source ↗

**Figure 8.** Figure 8: Median spectra for the DLA-Cor with (blue) and without (orange) Si [PITH_FULL_IMAGE:figures/full_fig_p007_8.png] view at source ↗

**Figure 9.** Figure 9: Median spectra for the HighWSys (blue), LowWSys (orange) and VeryLowWSys (green) with Si [PITH_FULL_IMAGE:figures/full_fig_p007_9.png] view at source ↗

**Figure 10.** Figure 10: Median spectra for the HighWSys (blue), HighWSys with Si [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗

**Figure 12.** Figure 12: g magnitude versus W2 magnitude for all DR16 quasars [PITH_FULL_IMAGE:figures/full_fig_p008_12.png] view at source ↗

**Figure 15.** Figure 15: Cumulative distribution of Eddington ratios for quasars [PITH_FULL_IMAGE:figures/full_fig_p009_15.png] view at source ↗

**Figure 14.** Figure 14: Bolometric luminosity versus black-hole mass for all [PITH_FULL_IMAGE:figures/full_fig_p009_14.png] view at source ↗

**Figure 17.** Figure 17: Velocity of systems versus black-hole mass. Black tri [PITH_FULL_IMAGE:figures/full_fig_p010_17.png] view at source ↗

**Figure 16.** Figure 16: Top panel: Black-hole mass distributions of quasars [PITH_FULL_IMAGE:figures/full_fig_p010_16.png] view at source ↗

read the original abstract

Active galactic nuclei (AGN) profoundly affect the interstellar medium of their host galaxies through intense radiation fields and powerful winds. Characterising this feedback is essential for understanding galaxy formation and evolution. Here we revisit the origin of proximate damped Lyman-$\alpha$ absorbers (PDLAs), which trace cold gas within 3000 km/s of the quasar redshift, and interpret their kinematics and physical properties within a unified framework. We searched the SDSS DR16 database for low-ionisation metal absorption-line systems at the quasar redshift (referred to as ProxSys). This approach enables us to identify and classify different types of proximate absorbers, including so-called Ghostly systems, coronagraphic DLAs (DLA-Cor), standard DLAs, and sub-DLAs, based on the presence of strong Lyman-alpha absorption, partial covering signatures, or excited atomic transitions such as SiII*. We find that about 13% of ProxSys belong to the Ghostly or DLA-Cor classes and exhibit strong absorption from excited species. The different classes of ProxSys form a continuous sequence characterised by decreasing SiII*, CIV, and NV absorption strengths and dust content. Their velocity distributions reveal multiple kinematic components. Standard DLAs cluster within 1000km/s of the quasar systemic redshift, consistent with gas in the host galaxy, whereas Ghostly and SiII* bearing systems display broader distributions, including outflows reaching -2000 km/s and a smaller population of inflowing clouds up to +1200 km/s. Median stacked spectra confirm that Ghostly and coronagraphic systems arise in dense, compact gas partially covering the quasar emission regions. These results support a scenario in which cold, dense clouds participate in a dynamic cycle of inflow and outflow in the vicinity of quasars, consistent with chaotic cold accretion.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note private letter to a colleague

The paper classifies proximate low-ionization absorbers in SDSS DR16 into Ghostly, DLA-Cor and standard types, reports a 13% fraction for the exotic classes plus velocity spreads, but the link to quasar feedback rests on an untested assumption that proximity equals association.

read the letter

The main thing to know is that this work reprocesses SDSS DR16 to pick out low-ionization metal systems within 3000 km/s of quasar redshifts, sorts them by Lyman-alpha strength, partial covering, and excited lines like SiII*, and then interprets the resulting kinematics and stacked spectra as evidence for cold clouds cycling in and out around the quasar in a chaotic accretion picture. They quote a 13% share for the Ghostly and coronagraphic classes and note outflows to -2000 km/s with some inflows to +1200 km/s, plus a sequence of decreasing absorption strengths and dust across the classes. That is the concrete new output they deliver from the public catalog. The stacked spectra are a clear plus, showing the special systems arise in dense gas that only partially covers the quasar continuum. The velocity clustering for ordinary DLAs near systemic redshift versus the broader spread for the others is also laid out plainly. Those pieces are straightforward observational results that build on earlier PDLA studies without obvious circularity. The soft spot is the central interpretive step. Defining the sample by a fixed velocity window around the quasar redshift does not automatically prove physical association; the paper gives no comparison of the observed incidence or kinematics against the expected rate of unrelated intervening DLAs in the same window, nor a control sample at offset redshifts. Without that check, the claimed dynamic cycle of inflow and outflow tied to feedback remains an assumption rather than a demonstrated excess. Sample selection criteria and uncertainties on the 13% figure are also thin in the abstract, though the full text may fill some of that in. This is the sort of paper that would interest people working on AGN feedback and cold gas in galaxy halos. A reader looking for new quantitative handles on proximate absorbers or stacked partial-covering signatures could pull useful numbers from it even if the feedback story needs more work. It is grounded enough in public data and direct measurements to deserve a serious referee rather than a desk reject, though the referees will almost certainly ask for the missing incidence test and clearer error analysis.

Referee Report

2 major / 1 minor

Summary. The manuscript analyzes proximate damped Lyman-alpha systems (PDLAs, termed ProxSys) identified within 3000 km/s of quasar redshifts in SDSS DR16 spectra. It classifies them into Ghostly systems, coronagraphic DLAs (DLA-Cor), standard DLAs, and sub-DLAs using criteria based on strong Lyman-alpha absorption, partial covering signatures, and excited transitions such as SiII*. Approximately 13% are reported in the Ghostly or DLA-Cor classes exhibiting strong excited-species absorption. The classes are described as forming a continuous sequence with decreasing SiII*, CIV, NV absorption and dust content. Velocity distributions show standard DLAs clustered within 1000 km/s (consistent with host-galaxy gas), while Ghostly and SiII*-bearing systems extend to outflows of -2000 km/s and inflows of +1200 km/s. Median stacked spectra are presented to confirm that Ghostly and coronagraphic systems arise in dense, compact gas with partial covering of the quasar continuum. These observations are interpreted as evidence that cold, dense clouds participate in a dynamic inflow/outflow cycle near quasars, consistent with chaotic cold accretion.

Significance. If the physical association with quasars holds, the work supplies a useful observational classification scheme and kinematic evidence linking cold gas absorbers to quasar feedback, potentially informing simulations of AGN-driven galaxy evolution and chaotic cold accretion models. Credit is due for leveraging public SDSS DR16 data to examine a large sample and for employing stacked spectra to demonstrate partial-covering signatures in the Ghostly and DLA-Cor classes.

major comments (2)

[Abstract] Abstract: The central claim that ProxSys trace quasar feedback and participate in inflow/outflow cycles rests on the assumption of physical association rather than chance projection. No comparison is provided between the observed incidence of these systems and the expected rate of intervening DLAs within the 3000 km/s window (using the known dN/dz or a control sample of non-proximate sightlines), leaving the kinematic interpretations and the continuous sequence in absorption properties vulnerable to selection biases or redshift uncertainties.
[Abstract] Abstract: The reported 13% fraction for Ghostly or DLA-Cor classes, the velocity distributions, and the stacked-spectra results are presented without details on the total sample size, precise selection criteria for the velocity cut, statistical uncertainties, or validation of the post-hoc classifications against false positives. These omissions make it difficult to assess whether the reported trends are robust or could arise from unrelated intervening absorbers.

minor comments (1)

[Abstract] The abstract introduces the term 'ProxSys' without an immediate definition, which could be clarified on first use for readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their careful and constructive review of our manuscript. We address the two major comments point by point below. While we maintain that the combination of excited-state absorption, partial covering, and kinematic clustering provides strong evidence for physical association, we agree that additional quantitative comparisons and clarifications will improve the robustness of the presentation. We will revise the manuscript accordingly.

read point-by-point responses

Referee: [Abstract] Abstract: The central claim that ProxSys trace quasar feedback and participate in inflow/outflow cycles rests on the assumption of physical association rather than chance projection. No comparison is provided between the observed incidence of these systems and the expected rate of intervening DLAs within the 3000 km/s window (using the known dN/dz or a control sample of non-proximate sightlines), leaving the kinematic interpretations and the continuous sequence in absorption properties vulnerable to selection biases or redshift uncertainties.

Authors: We acknowledge that an explicit calculation of the expected incidence of intervening DLAs within the 3000 km/s velocity window would provide a useful quantitative check on the physical association. The manuscript already emphasizes that the detection of Si II* and other excited transitions, together with partial-covering signatures in the stacked spectra, are highly improbable for unrelated intervening systems. Nevertheless, we agree that adding a comparison using the known dN/dz for DLAs (or a control sample of non-proximate sightlines) will strengthen the argument and reduce concerns about projection effects or redshift uncertainties. This calculation will be included in the revised manuscript. revision: yes
Referee: [Abstract] Abstract: The reported 13% fraction for Ghostly or DLA-Cor classes, the velocity distributions, and the stacked-spectra results are presented without details on the total sample size, precise selection criteria for the velocity cut, statistical uncertainties, or validation of the post-hoc classifications against false positives. These omissions make it difficult to assess whether the reported trends are robust or could arise from unrelated intervening absorbers.

Authors: The full manuscript (Section 2) specifies the parent sample of SDSS DR16 quasars, the precise velocity cut of 3000 km/s, and the criteria used to classify systems into Ghostly, DLA-Cor, standard DLA, and sub-DLA categories. The 13% fraction refers to the subset exhibiting strong Si II* absorption. However, we agree that the abstract is too terse on these points and that an explicit discussion of statistical uncertainties and possible false-positive rates would improve transparency. We will expand the abstract to report the total sample size and add a short paragraph in the methods or results section addressing the robustness of the classifications. revision: yes

Circularity Check

0 steps flagged

No significant circularity; observational classifications and interpretations are data-driven without self-referential reduction

full rationale

The paper defines ProxSys explicitly as low-ionisation systems within 3000 km/s of the quasar redshift in SDSS DR16, then classifies subtypes (Ghostly, DLA-Cor, standard DLAs) using independent spectral criteria such as Lyman-alpha strength, partial covering, and SiII* excitation. Velocity distributions, stacked spectra, and the continuous sequence in absorption strengths are reported directly from the data. No equations, parameter fits, or predictions are described that reduce to the input definitions by construction. The unified framework interpretation follows from the observed kinematics and properties rather than any self-citation chain or ansatz smuggling. This is a standard observational analysis with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based solely on the abstract, the analysis relies on standard spectroscopic interpretation without introducing new free parameters, axioms beyond domain conventions, or invented entities.

axioms (1)

domain assumption Absorption lines in quasar spectra can be reliably identified and their velocities measured to infer gas kinematics and physical conditions.
The entire classification and kinematic analysis depends on established methods for interpreting quasar spectra.

pith-pipeline@v0.9.0 · 5626 in / 1468 out tokens · 161898 ms · 2026-05-13T04:21:55.521199+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

73 extracted references · 73 canonical work pages

[1]

2020, ApJS, 249, 3

Ahumada, R., Allende Prieto, C., Almeida, A., et al. 2020, ApJS, 249, 3

work page 2020
[2]

2018, ApJ, 857, 60

Arav, N., Liu, G., Xu, X., et al. 2018, ApJ, 857, 60

work page 2018
[3]

A., & Plesha, R

Arav, N., Xu, X., Miller, T., Kriss, G. A., & Plesha, R. 2020, ApJS, 247, 37

work page 2020
[4]

2024, MNRAS, 527, 12298

Aromal, P., Srianand, R., & Petitjean, P. 2024, MNRAS, 527, 12298

work page 2024
[5]

A., Ledoux, C., Noterdaeme, P., et al

Balashev, S. A., Ledoux, C., Noterdaeme, P., et al. 2023, MNRAS, 524, 5016

work page 2023
[6]

A., Ledoux, C., Noterdaeme, P., et al

Balashev, S. A., Ledoux, C., Noterdaeme, P., et al. 2020, MNRAS, 497, 1946

work page 2020
[7]

L., et al

Belli, S., Park, M., Davies, R. L., et al. 2024, Nature, 630, 54

work page 2024
[8]

Bluck, A. F. L., Piotrowska, J. M., & Maiolino, R. 2023, ApJ, 944, 108

work page 2023
[9]

Borguet, B. C. J., Arav, N., Edmonds, D., Chamberlain, C., & Benn, C. 2013, ApJ, 762, 49

work page 2013
[10]

& Yang, X.-H

Bu, D.-F. & Yang, X.-H. 2021, ApJ, 921, 100

work page 2021
[11]

M., Binney, J., et al

Cattaneo, A., Faber, S. M., Binney, J., et al. 2009, Nature, 460, 213

work page 2009
[12]

2022, ApJS, 258, 18

Chabanier, S., Etourneau, T., Le Goff, J.-M., et al. 2022, ApJS, 258, 18

work page 2022
[13]

M., Kraemer, S

Crenshaw, D. M., Kraemer, S. B., & George, I. M. 2003, ARA&A, 41, 117

work page 2003
[14]

2020, MNRAS, 495, 460

Dai, X., Bhatiani, S., & Chen, B. 2020, MNRAS, 495, 460

work page 2020
[15]

L., Belli, S., Park, M., et al

Davies, R. L., Belli, S., Park, M., et al. 2024, MNRAS, 528, 4976

work page 2024
[16]

S., Kneib, J.-P., Percival, W

Dawson, K. S., Kneib, J.-P., Percival, W. J., et al. 2016, AJ, 151, 44

work page 2016
[17]

2013, MNRAS, 433, 3297

Dubois, Y ., Gavazzi, R., Peirani, S., & Silk, J. 2013, MNRAS, 433, 3297

work page 2013
[18]

2016, MNRAS, 463, 3948

Dubois, Y ., Peirani, S., Pichon, C., et al. 2016, MNRAS, 463, 3948

work page 2016
[19]

L., Prochaska, J

Ellison, S. L., Prochaska, J. X., Hennawi, J., et al. 2010, MNRAS, 406, 1435

work page 2010
[20]

L., Prochaska, J

Ellison, S. L., Prochaska, J. X., & Mendel, J. T. 2011, MNRAS, 412, 448

work page 2011
[21]

L., Yan, L., Hook, I

Ellison, S. L., Yan, L., Hook, I. M., et al. 2002, A&A, 383, 91

work page 2002
[22]

C., Vasudevan, R

Fabian, A. C., Vasudevan, R. V ., Mushotzky, R. F., Winter, L. M., & Reynolds, C. S. 2009, MNRAS, 394, L89

work page 2009
[23]

2020, ApJ, 901, 123

Fathivavsari, H. 2020, ApJ, 901, 123

work page 2020
[24]

2017, MNRAS, 466, L58 Faucher-Giguère, C.-A

Fathivavsari, H., Petitjean, P., Zou, S., et al. 2017, MNRAS, 466, L58 Faucher-Giguère, C.-A. & Quataert, E. 2012, MNRAS, 425, 605 Filiz Ak, N., Brandt, W. N., Hall, P. B., et al. 2012, ApJ, 757, 114

work page 2017
[25]

2013, A&A, 558, A111 Förster Schreiber, N

Finley, H., Petitjean, P., Pâris, I., et al. 2013, A&A, 558, A111 Förster Schreiber, N. M., Übler, H., Davies, R. L., et al. 2019, ApJ, 875, 21

work page 2013
[26]

L., et al

Gaspari, M., McDonald, M., Hamer, S. L., et al. 2018, ApJ, 854, 167

work page 2018
[27]

& S˛ adowski, A

Gaspari, M. & S˛ adowski, A. 2017, ApJ, 837, 149

work page 2017
[28]

R., Jiang, L., Brandt, W

Gibson, R. R., Jiang, L., Brandt, W. N., et al. 2009, ApJ, 692, 758

work page 2009
[29]

N., McLaughlin, D

Gofford, J., Reeves, J. N., McLaughlin, D. E., et al. 2015, MNRAS, 451, 4169 Guimarães, R., Petitjean, P., Rollinde, E., et al. 2007, MNRAS, 377, 657

work page 2015
[30]

2022, ApJ, 927, L24

Gupta, N., Srianand, R., Momjian, E., et al. 2022, ApJ, 927, L24

work page 2022
[31]

2026, A&A, 705, A123

Han, S., Krogager, J.-K., Ledoux, C., et al. 2026, A&A, 705, A123

work page 2026
[32]

Hennawi, J. F. & Prochaska, J. X. 2007, ApJ, 655, 735

work page 2007
[33]

F., Prochaska, J

Hennawi, J. F., Prochaska, J. X., Kollmeier, J., & Zheng, Z. 2009, ApJ, 693, L49

work page 2009
[34]

2016, ApJ, 821, 1

Jiang, P., Zhou, H., Pan, X., et al. 2016, ApJ, 821, 1

work page 2016
[35]

C., Chakravorty, S., & Kembhavi, A

Laha, S., Guainazzi, M., Dewangan, G. C., Chakravorty, S., & Kembhavi, A. K. 2014, MNRAS, 441, 2613

work page 2014
[36]

S., Reeves, J., et al

Laha, S., Reynolds, C. S., Reeves, J., et al. 2021, Nature Astronomy, 5, 13

work page 2021
[37]

& Petitjean, P

Laloux, B. & Petitjean, P. 2021, MNRAS, 502, 3855

work page 2021
[38]

& Brandt, W

Laor, A. & Brandt, W. N. 2002, ApJ, 569, 641 Laužikas, M. & Zubovas, K. 2024, A&A, 690, A396

work page 2002
[39]

Leung, G. C. K., Coil, A. L., Aird, J., et al. 2019, ApJ, 886, 11

work page 2019
[40]

W., Higley, A

Lyke, B. W., Higley, A. N., McLane, J. N., et al. 2020, ApJS, 250, 8

work page 2020
[41]

2025, Nature Astronomy, 9, 907

Marconcini, C., Marconi, A., Cresci, G., et al. 2025, Nature Astronomy, 9, 907

work page 2025
[42]

Morganti, R., Oosterloo, T., Schulz, R., Tadhunter, C., & Raymond Oonk, J. B. 2020, in IAU Symposium, V ol. 342, Perseus in Sicily: From Black Hole to Cluster Outskirts, ed. K. Asada, E. de Gouveia Dal Pino, M. Giroletti, H. Na- gai, & R. Nemmen, 85–89

work page 2020
[43]

& Chiang, J

Murray, N. & Chiang, J. 1995, ApJ, 454, L105

work page 1995
[44]

2023, A&A, 673, A89

Noterdaeme, P., Balashev, S., Cuellar, R., et al. 2023, A&A, 673, A89

work page 2023
[45]

2019, A&A, 627, A32

Noterdaeme, P., Balashev, S., Krogager, J.-K., et al. 2019, A&A, 627, A32

work page 2019
[46]

C., et al

Noterdaeme, P., Petitjean, P., Carithers, W. C., et al. 2012, A&A, 547, L1

work page 2012
[47]

2018, ApJ, 863, 198 Pâris, I., Petitjean, P., Aubourg, É., et al

Pan, X., Zhang, S., Zhou, H., et al. 2018, ApJ, 863, 198 Pâris, I., Petitjean, P., Aubourg, É., et al. 2018, A&A, 613, A51

work page 2018
[48]

X., et al

Perrotta, S., D’Odorico, V ., Prochaska, J. X., et al. 2016, MNRAS, 462, 3285

work page 2016
[49]

M., Bluck, A

Piotrowska, J. M., Bluck, A. F. L., Maiolino, R., & Peng, Y . 2022, MNRAS, 512, 1052

work page 2022
[50]

Prochaska, J. X. 2006, ApJ, 650, 272

work page 2006
[51]

X., Chen, H.-W., & Bloom, J

Prochaska, J. X., Chen, H.-W., & Bloom, J. S. 2006, ApJ, 648, 95

work page 2006
[52]

Prochaska, J. X. & Hennawi, J. F. 2009, ApJ, 690, 1558

work page 2009
[53]

X., Hennawi, J

Prochaska, J. X., Hennawi, J. F., & Herbert-Fort, S. 2008, ApJ, 675, 1002

work page 2008
[54]

A., Pettini, M., Steidel, C

Rix, S. A., Pettini, M., Steidel, C. C., et al. 2007, ApJ, 670, 15

work page 2007
[55]

B., et al

Rojas-Ruiz, S., Momjian, E., Davies, F. B., et al. 2025, ApJ, 985, 34

work page 2025
[56]

Rupke, D. S. N., Thomas, A. D., & Dopita, M. A. 2021, MNRAS, 503, 4748

work page 2021
[57]

Schawinski, K., Koss, M., Berney, S., & Sartori, L. F. 2015, MNRAS, 451, 2517

work page 2015
[58]

A., Bower, R

Schaye, J., Crain, R. A., Bower, R. G., et al. 2015, MNRAS, 446, 521

work page 2015
[59]

Silva, A. I. & Viegas, S. M. 2002, MNRAS, 329, 135

work page 2002
[60]

2005, MNRAS, 361, 776

Springel, V ., Di Matteo, T., & Hernquist, L. 2005, MNRAS, 361, 776

work page 2005
[61]

& Petitjean, P

Srianand, R. & Petitjean, P. 2000, A&A, 357, 414

work page 2000
[62]

J., Rankine, A

Temple, M. J., Rankine, A. L., Banerji, M., et al. 2024, MNRAS, 532, 424

work page 2024
[63]

N., et al

Tombesi, F., Cappi, M., Reeves, J. N., et al. 2013, MNRAS, 430, 1102

work page 2013
[64]

N., et al

Tombesi, F., Cappi, M., Reeves, J. N., et al. 2011, ApJ, 742, 44

work page 2011
[65]

& Wylezalek, D

Vivek, M. & Wylezalek, D. 2025, A&A, 695, L22

work page 2025
[66]

M., Ledoux, C., Smette, A., et al

Vreeswijk, P. M., Ledoux, C., Smette, A., et al. 2007, A&A, 468, 83

work page 2007
[67]

J., Chugai, N

Wampler, E. J., Chugai, N. N., & Petitjean, P. 1995, ApJ, 443, 586

work page 1995
[68]

J., Carswell, R

Weymann, R. J., Carswell, R. F., & Smith, M. G. 1981, ARA&A, 19, 41

work page 1981
[69]

M., Prochaska, J

Wolfe, A. M., Prochaska, J. X., & Gawiser, E. 2003, ApJ, 593, 215

work page 2003
[70]

& Shen, Y

Wu, Q. & Shen, Y . 2022, ApJS, 263, 42

work page 2022
[71]

2024, MNRAS, 532, 4703

Wu, S., Shi, X., Kalita, N., et al. 2024, MNRAS, 532, 4703

work page 2024
[72]

2018, ApJ, 858, 32

Xie, X., Zhou, H., Pan, X., et al. 2018, ApJ, 858, 32

work page 2018
[73]

L., Hamann, F., Pâris, I., et al

Zakamska, N. L., Hamann, F., Pâris, I., et al. 2016, MNRAS, 459, 3144 Article number, page 12

work page 2016