pith. the verified trust layer for science. sign in

arxiv: 2602.11269 · v2 · submitted 2026-02-11 · 🌌 astro-ph.GA · astro-ph.CO

The Quasar Proximity Effect as an Alternative Probe of Quasar Pair Distances

Huanqing Chen , Camille Avestruz , Jakob Wiest This is my paper

Pith reviewed 2026-05-16 02:19 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.CO
keywords quasar pairsproximity zonesline-of-sight distancehigh-redshift quasarsabsorption spectrapeak findingsupermassive black hole mergers
0
0 comments X p. Extension

The pith

Proximity zone spectra distinguish line-of-sight distances in high-redshift quasar pairs to 0.2 pMpc accuracy for close sky separations

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

This paper proposes using the proximity zones around high-redshift quasars to constrain their relative distances along the line of sight. Synthetic spectra show that for small sky-plane separations of 10-100 pkpc, a simple peak finding algorithm on the absorption features can distinguish whether the line-of-sight distance is less than or greater than 1 pMpc. When the true distance is 3 pMpc or larger, the method recovers it with about 0.2 pMpc accuracy. For pairs with 1 pMpc sky separation the distinction holds around 4 pMpc with 0.5 pMpc precision. The work illustrates how this spectral approach can map the three-dimensional configuration of quasar pairs thought to be precursors to supermassive black hole mergers.

Core claim

For quasar pairs with sky-plane separations of 10-100 pkpc, a simple peak finding algorithm applied to synthetic proximity zone spectra can distinguish between line-of-sight distances less than or greater than 1 pMpc, and for true distances of 3 pMpc or larger, it estimates the distance with an accuracy of approximately 0.2 pMpc. For larger sky separations of 1 pMpc, it distinguishes below or above 4 pMpc with 0.5 pMpc accuracy.

What carries the argument

Synthetic proximity zone spectra for different line-of-sight separations, processed by a peak finding algorithm that locates the absorption features arising from the ionized regions around each quasar in the pair.

Load-bearing premise

The synthetic proximity zone spectra faithfully reproduce the observational signatures that would be measured in real data, without unmodeled systematics from IGM fluctuations, quasar variability, or instrumental effects that could alter the peak structure.

What would settle it

Apply the peak finding algorithm to real spectra of a quasar pair whose line-of-sight distance has been independently measured by another method and check whether the recovered distance matches the true value within the stated 0.2 pMpc accuracy.

Figures

Figures reproduced from arXiv: 2602.11269 by Camille Avestruz, Huanqing Chen, Jakob Wiest.

Figure 1
Figure 1. Figure 1: Top: Radiation profile in units of the ionization rate due to both quasars as a function of distance from the target quasar QA. Solid lines correspond to the scenario where QA is the brighter one with M1450 = −27 and the foreground quasar QB with M1450 = −26. Dash-dotted lines correspond to the inverted scenario where QA is the dimmer one with M1450 = −26 and the foreground quasar QB with M1450 = −27. We f… view at source ↗
Figure 2
Figure 2. Figure 2: Left: Dependence of predicted dl.o.s. on the true relative distance in the quasar pair configuration case of dsky = 0.01 pMpc and a brighter background target quasar. The boxes represent 50% of the dl.o.s. predictions while the bars represent 90% of the predictions. Peak finding on spectra with a smoothing kernel of 250 km/s is sufficient to measure dl.o.s. ≥ 2 pMpc with consistently tight percentage accur… view at source ↗
Figure 3
Figure 3. Figure 3: Same figure and quasar pair scenario as [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Same as the left panel of [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Upper panels: the left panel shows a simulated spectra with spectral resolution R = 8000 and SNR=30 per 10 km/s pixel. Using the same peak finding algorithm, the predicted dl.o.s. vs. true dl.o.s. result is shown on the right. Lower panels: same as the upper panels, except for simulated spectra with spectral resolution R = 2000 and SNR=10 per 10 km/s pixel. motivates the exploration of more complex methods… view at source ↗
read the original abstract

Recently discovered quasar pairs at high redshifts ($z\gtrsim$5) are likely precursors to supermassive black hole mergers, providing a promising window to high redshift quasar growth mechanisms. However, the large uncertainties on their relative distances along the line-of-sight ($d_{\rm l.o.s.}$) limits our ability to characterize quasar pairs. In this study, we explore synthetic quasar proximity zone spectra as an alternative method to constrain the line-of-sight distance of quasar pairs. We find that for small sky-plane separations ($d_{\rm sky}\approx 10-100$ pkpc), a simple peak finding algorithm can easily distinguish between scenarios of $d_{\rm l.o.s.} \lesssim1$ pMpc and $\gtrsim1$ pMpc. For cases where the true $d_{\rm l.o.s.} \geq 3$ pMpc, the accuracy of $d_{\rm l.o.s.}$ estimation is $\approx 0.2$ pMpc. Large sky-plane separations of $d_{\rm sky}=1$ pMpc have larger absolute uncertainties in $d_{\rm l.o.s.}$ estimates, but the method can still easily distinguish between scenarios where $d_{\rm l.o.s.}\lesssim4$ pMpc and $\gtrsim4$ pMpc. $d_{\rm l.o.s.}$ estimates have an uncertainty of $\approx$0.5 pMpc when true $d_{\rm l.o.s.} \gtrsim4$ pMpc. Our proof-of-concept study illustrates the potential use of quasar proximity zones to constrain the 3-dimensional quasar pair configuration, providing an avenue to characterize quasar pairs.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The manuscript presents a proof-of-concept study using synthetic quasar proximity zone spectra and a simple peak-finding algorithm to constrain the line-of-sight distances (d_l.o.s.) of high-redshift quasar pairs. For small sky-plane separations (d_sky ≈ 10-100 pkpc), it claims the method can distinguish d_l.o.s. ≲1 pMpc from ≳1 pMpc, with an accuracy of ≈0.2 pMpc when the true d_l.o.s. ≥3 pMpc; for d_sky=1 pMpc the corresponding thresholds are ≲4 pMpc vs ≳4 pMpc with ≈0.5 pMpc accuracy. The work illustrates the potential of proximity zones as an alternative to direct distance measurements for characterizing 3D quasar pair configurations.

Significance. If the central claims hold under more realistic conditions, the approach would supply a new observational handle on the three-dimensional separations of z≳5 quasar pairs, directly relevant to supermassive black hole merger rates and early growth. The synthetic demonstration shows clear separation in transmission-peak locations for different d_l.o.s. values, which is a strength of the proof-of-concept. However, the significance remains provisional because the results rest entirely on idealized forward models without demonstrated robustness to IGM fluctuations or observational systematics.

major comments (2)
  1. [Synthetic spectra modeling] Synthetic spectra section: the IGM transmission is generated from a single, presumably smooth model. The headline accuracy claims (0.2 pMpc for d_l.o.s. ≥3 pMpc) require testing on an ensemble of IGM realizations that include realistic 10-30% transmission fluctuations on 1-10 pMpc scales; without this, it is unclear whether peak locations remain stable enough to support the reported separation power and precision.
  2. [Results] Results and validation: the abstract and main text report quantitative accuracies derived solely from synthetic spectra, with no comparison to real high-z quasar spectra, no explicit error-budget breakdown, and no exploration of systematics such as quasar variability or instrumental resolution. These omissions make the accuracy statements load-bearing yet weakly supported.
minor comments (2)
  1. [Methods] The peak-finding algorithm is described only at a high level; explicit parameters, thresholds, and pseudocode would improve reproducibility.
  2. [Introduction] Add references to existing literature on quasar proximity zones at z>5 and on observed quasar pair statistics to place the new method in context.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their detailed and constructive report. The comments correctly identify key limitations of our proof-of-concept study, which relies on idealized synthetic spectra. We have revised the manuscript to better qualify our accuracy claims, add discussion of IGM fluctuations and systematics, and emphasize the preliminary nature of the results. Point-by-point responses follow.

read point-by-point responses

  1. Referee: Synthetic spectra section: the IGM transmission is generated from a single, presumably smooth model. The headline accuracy claims (0.2 pMpc for d_l.o.s. ≥3 pMpc) require testing on an ensemble of IGM realizations that include realistic 10-30% transmission fluctuations on 1-10 pMpc scales; without this, it is unclear whether peak locations remain stable enough to support the reported separation power and precision.

    Authors: We agree that an ensemble of IGM realizations with realistic fluctuations would strengthen the robustness assessment. Our analysis intentionally used a single smooth model to demonstrate the core principle in a controlled manner. In the revised manuscript we have added a dedicated paragraph in the methods and a new limitations subsection that discusses the expected impact of 10-30% fluctuations on peak locations. We note that the algorithm identifies the largest transmission peak, which is driven by the overall proximity-zone extent rather than small-scale structure, but we have tempered the quoted precision values and explicitly state that quantitative accuracy may degrade under realistic IGM conditions. A full ensemble test lies beyond the scope of this initial proof-of-concept. revision: partial

  2. Referee: Results and validation: the abstract and main text report quantitative accuracies derived solely from synthetic spectra, with no comparison to real high-z quasar spectra, no explicit error-budget breakdown, and no exploration of systematics such as quasar variability or instrumental resolution. These omissions make the accuracy statements load-bearing yet weakly supported.

    Authors: We accept that the accuracy figures are derived exclusively from idealized synthetics and that a full error budget and real-data validation are absent. As this is framed as a proof-of-concept, we have revised the abstract, results, and conclusions to explicitly label the reported numbers as indicative only and to outline the main systematics (quasar variability, spectral resolution, and IGM fluctuations) that future work must address. A brief error-budget discussion has been added to the results section. No direct comparison to observed spectra was performed because the manuscript focuses on introducing the methodological concept rather than delivering a calibrated observational tool. revision: yes

Circularity Check

0 steps flagged

No significant circularity; forward-modeling validation is self-contained

full rationale

The paper constructs synthetic proximity-zone spectra for input d_l.o.s. values, then applies an external peak-finding routine to recover those values. This is standard forward-modeling validation with no fitted parameters, self-definitional equations, or load-bearing self-citations that reduce the reported accuracy to the inputs by construction. The derivation chain consists of generating model spectra and testing an independent algorithm on them; no step equates the output distance estimate to a fitted input or renames a known result.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no explicit free parameters, axioms, or invented entities are stated. The approach implicitly assumes standard quasar proximity-zone physics and IGM transmission models from prior literature.

pith-pipeline@v0.9.0 · 5632 in / 1251 out tokens · 59437 ms · 2026-05-16T02:19:20.662835+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

31 extracted references · 31 canonical work pages

  1. [1]

    C., & Ostriker, J

    Bajtlik, S., Duncan, R. C., & Ostriker, J. P. 1988, ApJ, 327, 570, doi: 10.1086/166217

    work page doi:10.1086/166217 1988

  2. [2]

    C., Blandford, R

    Begelman, M. C., Blandford, R. D., & Rees, M. J. 1980, Nature, 287, 307, doi: 10.1038/287307a0

    work page doi:10.1038/287307a0 1980

  3. [3]

    K., Balogh, M

    Bolton, J. S., & Haehnelt, M. G. 2007, MNRAS, 374, 493, doi: 10.1111/j.1365-2966.2006.11176.x

    work page doi:10.1111/j.1365-2966.2006.11176.x 2007

  4. [4]

    2020, All z¿5.7 quasars currently known, Zenodo, doi: 10.5281/zenodo.3634964

    Bosman, S. 2020, All z¿5.7 quasars currently known, Zenodo, doi: 10.5281/zenodo.3634964

    work page doi:10.5281/zenodo.3634964 2020

  5. [5]

    Bosman, S. E. I., Kakiichi, K., Meyer, R. A., et al. 2020, ApJ, 896, 49, doi: 10.3847/1538-4357/ab85cd

    work page doi:10.3847/1538-4357/ab85cd 2020

  6. [6]

    Bosman, S. E. I., ˇDurovˇ c´ ıkov´ a, D., Davies, F. B., & Eilers, A.-C. 2021, MNRAS, 503, 2077, doi: 10.1093/mnras/stab572

    work page doi:10.1093/mnras/stab572 2021

  7. [7]

    Chen, H., & Gnedin, N. Y. 2021a, ApJ, 911, 60, doi: 10.3847/1538-4357/abe7e7

    work page doi:10.3847/1538-4357/abe7e7

  8. [8]

    Chen, H., & Gnedin, N. Y. 2021b, ApJ, 916, 118, doi: 10.3847/1538-4357/ac0429

    work page doi:10.3847/1538-4357/ac0429

  9. [9]

    Chen, H., Speagle, J., & Rogers, K. K. 2023, arXiv e-prints, arXiv:2311.16238, doi: 10.48550/arXiv.2311.16238

    work page doi:10.48550/arxiv.2311.16238 2023

  10. [10]

    Chen, H., Eilers, A.-C., Bosman, S. E. I., et al. 2022, ApJ, 931, 29, doi: 10.3847/1538-4357/ac658d

    work page doi:10.3847/1538-4357/ac658d 2022

  11. [11]

    B., & Furlanetto, S

    Davies, F. B., & Furlanetto, S. R. 2016, MNRAS, 460, 1328, doi: 10.1093/mnras/stw931 De Rosa, A., Vignali, C., Bogdanovi´ c, T., et al. 2019, NewAR, 86, 101525, doi: 10.1016/j.newar.2020.101525

    work page doi:10.1093/mnras/stw931 2016

  12. [12]

    G., Stern, D., Mahabal, A

    Djorgovski, S. G., Stern, D., Mahabal, A. A., & Brunner, R. 2003, ApJ, 596, 67, doi: 10.1086/377524 D’Odorico, V., Ba˜ nados, E., Becker, G. D., et al. 2023, MNRAS, 523, 1399, doi: 10.1093/mnras/stad1468

    work page doi:10.1086/377524 2003

  13. [13]

    B., Hennawi, J

    Eilers, A.-C., Davies, F. B., Hennawi, J. F., et al. 2017, ApJ, 840, 24, doi: 10.3847/1538-4357/aa6c60

    work page doi:10.3847/1538-4357/aa6c60 2017

  14. [14]

    F., Decarli, R., et al

    Eilers, A.-C., Hennawi, J. F., Decarli, R., et al. 2020, ApJ, 900, 37, doi: 10.3847/1538-4357/aba52e

    work page doi:10.3847/1538-4357/aba52e 2020

  15. [15]

    Fan, X., Ba˜ nados, E., & Simcoe, R. A. 2023, ARA&A, 61, 373, doi: 10.1146/annurev-astro-052920-102455

    work page doi:10.1146/annurev-astro-052920-102455 2023

  16. [16]

    A., Schneider, D

    Fan, X., Strauss, M. A., Schneider, D. P., et al. 1999, AJ, 118, 1, doi: 10.1086/300944

    work page doi:10.1086/300944 1999

  17. [17]

    K., Lupton, R

    Fan, X., Narayanan, V. K., Lupton, R. H., et al. 2001, AJ, 122, 2833, doi: 10.1086/324111

    work page doi:10.1086/324111 2001

  18. [18]

    Gnedin, N. Y. 2014, ApJ, 793, 29, doi: 10.1088/0004-637X/793/1/29

    work page doi:10.1088/0004-637x/793/1/29 2014

  19. [19]

    Y., Becker, G

    Gnedin, N. Y., Becker, G. D., & Fan, X. 2017, ApJ, 841, 26, doi: 10.3847/1538-4357/aa6c24

    work page doi:10.3847/1538-4357/aa6c24 2017

  20. [20]

    Y., & Kaurov, A

    Gnedin, N. Y., & Kaurov, A. A. 2014, ApJ, 793, 30, doi: 10.1088/0004-637X/793/1/30

    work page doi:10.1088/0004-637x/793/1/30 2014

  21. [21]

    2024, MNRAS, 530, 3208, doi: 10.1093/mnras/stae1080

    Greig, B., Mesinger, A., Ba˜ nados, E., et al. 2024, MNRAS, 530, 3208, doi: 10.1093/mnras/stae1080

    work page doi:10.1093/mnras/stae1080 2024

  22. [22]

    F., Kist, T., Davies, F

    Hennawi, J. F., Kist, T., Davies, F. B., & Tamanas, J. 2025, MNRAS, 539, 2621, doi: 10.1093/mnras/staf643

    work page doi:10.1093/mnras/staf643 2025

  23. [23]

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

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

    work page doi:10.1146/annurev-astro-120419-014455 2020

  24. [24]

    J., et al

    Jeon, J., Liu, B., Taylor, A. J., et al. 2025, ApJ, 988, 110, doi: 10.3847/1538-4357/ade2e1

    work page doi:10.3847/1538-4357/ade2e1 2025

  25. [25]

    I.-H., Johnson, S

    Li, J. I.-H., Johnson, S. D., Avestruz, C., et al. 2024, ApJ, 977, 223, doi: 10.3847/1538-4357/ad900d

    work page doi:10.3847/1538-4357/ad900d 2024

  26. [26]

    F., et al

    Lusso, E., Worseck, G., Hennawi, J. F., et al. 2015, MNRAS, 449, 4204, doi: 10.1093/mnras/stv516

    work page doi:10.1093/mnras/stv516 2015

  27. [27]

    2024, ApJL, 965, L4, doi: 10.3847/2041-8213/ad35c7

    Matsuoka, Y., Izumi, T., Onoue, M., et al. 2024, ApJL, 965, L4, doi: 10.3847/2041-8213/ad35c7

    work page doi:10.3847/2041-8213/ad35c7 2024

  28. [28]

    D., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21871.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.426.3282M 426, 3282

    Runnoe, J. C., Brotherton, M. S., & Shang, Z. 2012, MNRAS, 422, 478, doi: 10.1111/j.1365-2966.2012.20620.x ˇDurovˇ c´ ıkov´ a, D., Katz, H., Bosman, S. E. I., et al. 2020, MNRAS, 493, 4256, doi: 10.1093/mnras/staa505

    work page doi:10.1111/j.1365-2966.2012.20620.x 2012

  29. [29]

    2021, ApJL, 921, L27, doi: 10.3847/2041-8213/ac31a9

    Yue, M., Fan, X., Yang, J., & Wang, F. 2021, ApJL, 921, L27, doi: 10.3847/2041-8213/ac31a9

    work page doi:10.3847/2041-8213/ac31a9 2021

  30. [30]

    2023, AJ, 165, 191, doi: 10.3847/1538-3881/acc2be

    Yue, M., Fan, X., Yang, J., & Wang, F. 2023, AJ, 165, 191, doi: 10.3847/1538-3881/acc2be

    work page doi:10.3847/1538-3881/acc2be 2023

  31. [31]

    2023, MNRAS, 518, 2123, doi: 10.1093/mnras/stac2633

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

    work page doi:10.1093/mnras/stac2633 2023