Pith. sign in

REVIEW 4 major objections 5 minor 159 references

Stacked reverberation mapping with sparse spectroscopy recovers high-redshift CIV lags and the radius–luminosity relation from DESI-style observations.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · deepseek-v4-flash

2026-08-01 06:24 UTC pith:UBDDI7ZA

load-bearing objection A careful mock-based feasibility study that convincingly shows the stacking method works under idealized DRW/top-hat conditions, but leaves the real-world case unproven because the simulations never break those assumptions. the 4 major comments →

arxiv 2607.21869 v1 pith:UBDDI7ZA submitted 2026-07-23 astro-ph.GA

Stacked Reverberation Mapping of High Redshift Quasars in DESI. I. Feasibility Analysis

Rahma Alfarsy , R.E.A. Canning , Eva-Maria Mueller , Jessica Aguilar , Steven Ahlen , David Alexander , Davide Bianchi , David Brooks
show 44 more authors
Peter Clark Todd Claybaugh Andrei Cuceu Tamara Davis Axel de la Macorra Saisrinivas Dhavala Victoria A. Fawcett Benjamin Floyd Andreu Font-Ribera Jaime Forero-Romero Enrique Gazta\~{n}aga Wei-Jian Guo Gaston Gutierrez Klaus Honscheid Richard Joyce Stephanie Juneau David Kirkby Theodore Kisner Anthony Kremin Claire Lamman Martin Landriau Laurent Le Guillou Paul Martini Hugh McDougall Aaron Meisner Ramon Miquel John Moustakas Seshadri Nadathur Nathalie Palanque-Delabrouille Zhiwei Pan Swayamtrupta Panda Ignasi P\'erez-R\`afols Francisco Prada Ragadeepika Pucha Graziano Rossi Eusebio Sanchez David Schlegel Michael Schubnell Tom Shanks Ma{\l}gorzata Siudek David Sprayberry Gregory Tarl\'e Benjamin Alan Weaver Hu Zou
This is my paper
classification astro-ph.GA
keywords stacked reverberation mappingCIV emission lineradius-luminosity relationquasar variabilitydamped random walkDESIbroad line regionhigh-redshift quasars
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

This paper tries to establish that ensemble reverberation mapping can work when individual quasars have only a handful of spectra, sometimes as few as two, provided the continuum is well sampled photometrically. Using mock light curves built to resemble quasar observations from DESI and ZTF-like photometry, the authors stack per-object lag posteriors from JAVELIN across luminosity–redshift bins. They report that 96% of the stacked lag measurements fall within 1σ of the simulated input lags, and the recovered radius–luminosity relation matches the input CIV relation to 1σ. If this holds, large cosmological surveys can double as reverberation-mapping machines, extending the R–L relation to high redshift and high luminosity without dedicated observing campaigns.

Core claim

The central claim is that additively stacking MCMC lag posteriors recovers average CIV lags for quasar ensembles from light curves with only 2–10 spectroscopic epochs at irregular cadence. The recovered relation log R[days] = (0.78 ± 0.035) + (0.51 ± 0.025) log(λL_1350/10^44) agrees with the input Hoormann et al. (2019) relation to 1σ. Accuracy improves with more quasars per stack up to a plateau near 400, with longer spectroscopic baselines mattering more than extra epochs, and with photometric baselines of roughly 1400 days. The method degrades at high luminosity and high redshift, and shows a systematic tendency to underestimate lags.

What carries the argument

The machinery is stacked Bayesian lag inference: each quasar's sparse CIV line light curve and dense photometric continuum light curve are fed to JAVELIN, which models the continuum as a damped random walk and the line response as a top-hat transfer function, producing a per-object lag posterior. These posteriors are additively stacked within equal-population luminosity–redshift bins, and the maximum a posteriori peak of the stacked distribution is the bin's lag. A cross-correlation peak distribution pipeline serves as an independent consistency check.

Load-bearing premise

The mocks assume the same damped random walk continuum and top-hat transfer function that JAVELIN uses to recover lags, and the input radius–luminosity relation has zero intrinsic scatter; real CIV lags show roughly 0.5 dex scatter and can involve outflows or BLR holidays, which would add noise and possibly bias the stacked peak.

What would settle it

Generate mock light curves with intrinsic R–L scatter of about 0.5 dex and variability that deviates from a damped random walk (or use empirical DESI+ZTF light curves with independently known lags), run the stacked pipeline, and check whether the recovered R–L slope and zero-point remain within 1σ of the input; a clear offset or smeared stacked peak would falsify the feasibility claim for realistic populations.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • DESI quasars with as few as two spectra can yield a CIV radius–luminosity relation out to z≈5 without new dedicated reverberation campaigns.
  • The method recovers average lags even when no single quasar's light curve is sufficient for an individual lag detection.
  • Stacking at least 400 quasars per bin and using photometric baselines of at least 1000 days are recommended design choices for future stacked RM programs.
  • Extending the spectroscopic baseline improves lag recovery more than adding extra epochs; a few spectra spread over years suffice.
  • The cross-correlation alternative also recovers lags but shows a stronger systematic underestimation, especially at low luminosity.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If real CIV scatter around the R–L relation (about 0.5 dex) and non-DRW variability are added to the mocks, the stacked peak may broaden and the recovered slope could shift; the paper leaves this misspecification untested.
  • The systematic low-lag overdensity visible in the averaged stacked posterior (MAP near 55 days versus an average input lag near 108 days) may contaminate real R–L fits at the low-luminosity end; separating this numerical bias from physical scatter is a natural next step.
  • The same stacking pipeline could be applied to MgII or Hβ where DESI's spectral coverage overlaps, giving cross-line consistency checks at high redshift.
  • The approach likely transfers to other wide-area programs with sparse multi-epoch spectroscopy, since the ≥2-epoch requirement is already satisfied by many existing survey designs.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. Using 250 mock C IV quasar light curves per luminosity–redshift bin, the authors simulate DESI sparse spectroscopy plus ZTF photometry, measure lags with JAVELIN, and stack the individual lag posteriors additively. They report 96% of stacked lags within 1σ and recover the input Hoormann et al. (2019) R–L relation as log R = (0.78±0.035)+(0.51±0.025)log(λL1350/10^44) (Eq. 6). They then map sensitivity to flux errors, photometric baseline, seasonal gaps, stack size, spectral epochs, and bin width, and cross-check with a CCF-based pipeline (Eq. 10). The abstract frames this as evidence that stacked RM with DESI-like data can extend the C IV R–L relation to high redshift.

Significance. Strengths: the work is transparent, builds mocks from DESI/ZTF survey characteristics, supplies public data/code, and conducts systematic parameter studies. The internal validation clearly shows the pipeline can recover lags under its assumed model. However, the feasibility claim is supported only in a closed loop: the mocks share the DRW/top-hat model assumed by JAVELIN and have zero intrinsic scatter, and the paper itself concedes these limitations. The CCF validation is valuable because it exposes a method-dependent 4σ offset (Eq. 10), indicating that realistic deviations could alter the conclusions. If the central claim can be demonstrated under misspecification, the impact is substantial.

major comments (4)
  1. [5.3, 6.1, 8.1] The recovery statistics in §7 (96% within 1σ; Eq. 6 vs Eq. 5) are internal-consistency checks: the mocks are generated with DRW continuum and top-hat transfer function (§5.3), the same parametric family JAVELIN assumes (§6.1). The input R–L relation is injected with zero dispersion (§5.3), whereas observed C IV lags show ~0.5 dex intrinsic scatter (Shen et al. 2024), and real C IV may have non-DRW variability, outflows/FeII contamination, or BLR holidays. Since §8.1 explicitly defers these to future work, the paper's central feasibility conclusion is conditional. I request either additional mock runs that inject 0.3–0.5 dex scatter and/or non-DRW variability, or a substantially softened claim of feasibility outside the model assumptions.
  2. [8.1, Fig. 21, Eq. 10] The pipeline has a systematic tendency to underestimate lags: the averaged stacked posterior peaks at 55 days versus an expected average of 108 days (Fig. 21), and the CCF pipeline returns an R–L relation 4σ below the input (Eq. 10). The authors attribute this to a low-lag overdensity but do not correct for it or quantify its effect on the fitted slope/intercept in Eq. (6). Because the same bias is present in the main pipeline, the quoted 1σ agreement with Hoormann et al. (2019) may partly reflect compensating errors rather than unbiased recovery. Please quantify and, if possible, model or correct this bias.
  3. [6.1, App. A] The base run fixes JAVELIN's damping timescale to 700 days, restricts the top-hat width to 3–40 days, and uses a lag prior (0–500 days) that brackets the maximum simulated lag of 383 days (§5.3). Appendix A shows that the recovered lag accuracy is sensitive to the lag prior range. This tuning is acceptable for a self-consistency test, but the paper should state more explicitly how the prior choices would be made in a blind application to DESI data, where such knowledge is unavailable. I would like to see the 0–1000 day prior case included in the R–L fit and discussed.
  4. [6.2] The stacking procedure is described as 'admittedly... not a mathematically robust way of combining posterior distributions,' and the authors note that the prior-independence assumption is violated by the binning design. The fact that additive stacking works on the mocks is reassuring, but it cannot validate the method for data where the true lags are unknown. Since later papers will apply this to DESI, I recommend replacing or supplementing the additive stack with a hierarchical model (e.g., Brewer & Elliott 2014), or providing a formal justification/simulation study of when additive stacking yields unbiased peaks.
minor comments (5)
  1. [Abstract, 5.3] The abstract quotes 2–10 spectral epochs; §5.3 gives a maximum of 13. These numbers should be aligned.
  2. [Fig. 8] The example in the text says the quasar has redshift 1.63, but the sample cut is z≥1.67; either the figure or the text is inconsistent.
  3. [5.3] The text says the median number of spectroscopic epochs is 'three days'; the unit should be epochs, not days.
  4. [9] 'Elveldt et al.' should be 'Eltvedt et al.' to match the reference used elsewhere.
  5. [5.1] The redshift range is reported inconsistently as 1.48<z<5.2 and 1.67<z<5.23; make this consistent after the ZTF-band cut.

Circularity Check

2 steps flagged

Closed-loop mocks: the recovered R-L relation is benchmarked against the same Eq. 5 used to assign input lags, and JAVELIN assumes the same DRW+top-hat model used to generate the light curves.

specific steps
  1. self definitional [Section 5.3 (Eq. 5) and Section 7 (Eq. 6)]
    "The assigned luminosity to the mock quasar is also used to calculate the input ‘observed’ time lag using the R-L relation from Hoormann et al. 2019, logR[days]=0.82+0.49log... (5) ... The recovered CIV R-L relation is logR[days]= (0.78±0.035) + (0.51±0.025) ... which is in agreement with the input relation given in Equation 5 to 1σ."

    The benchmark relation Eq. 6 is compared against Eq. 5, the very relation used to assign input lags to every mock quasar. With zero intrinsic scatter imposed, the true lags in each bin are deterministic functions of Eq. 5; an unbiased recovery must return Eq. 5 up to sampling noise. Therefore the 1σ agreement of Eq. 6 with Eq. 5 is a self-consistency check, not an independent constraint on the R-L relation. The paper explicitly defers testing with realistic scatter to future work.

  2. other [Sections 5.3 and 6.1]
    "The emission line light curves are then assumed to be the lagged, smoothed, and scaled versions of the continuum light curves. ... JAVELIN models the continuum light curve variability using a DRW and models the emission-line response with a parametrised top-hat transfer function. ... By using JAVELIN in our mock pipeline, we are assuming the same quasar variability and light curve model as those assumed in the light curve simulations."

    The generative model (DRW continuum + top-hat transfer function, Eq. 3) is identical to the model JAVELIN assumes for recovery. Recovery is therefore maximum-likelihood under the true generative model; the test demonstrates that the sparse-sampling/stacking pipeline can invert its own assumptions, but it does not probe misspecification such as non-DRW variability, outflows, FeII contamination, BLR holidays, or the ~0.5 dex intrinsic scatter of real CIV lags (Shen et al. 2024), all of which the paper defers to future work. This is an acknowledged but real closed loop.

full rationale

The paper is transparent about its mock setup and does not hide the closed-loop nature: the simulated lags come from Hoormann et al. (2019) Eq. 5, and the pipeline evaluates success by agreement with that same relation. The recovery of Eq. 6 in 1σ agreement with Eq. 5 is therefore an internal consistency test rather than an externally grounded prediction. Similarly, JAVELIN's assumed DRW+top-hat model matches the simulation model, so the test verifies that the pipeline can invert its own generative assumptions under ideal conditions. This is a legitimate feasibility sanity check, but the paper's broader conclusion—that stacked RM with DESI-like data can reliably constrain and extend the R-L relation to high redshift—goes beyond what the closed-loop mocks can establish. The CCF validation in Section 9 does not break the loop: it uses the same idealized mocks, and its recovered R-L relation is 4σ from the input (Eq. 10), indicating that method misspecification can substantially bias recovery. The authors explicitly note that real CIV lags have ~0.5 dex scatter, about 10 times larger than simulated, and defer testing with scatter or alternative variability models to future work. These are limitations and acknowledged assumptions, not hidden circularity; however, the central quantitative claim of recovering the input R-L relation is, by construction, the expected outcome of an unbiased pipeline on data generated from that same relation. Score 6 reflects partial circularity: the prediction reduces to the input by construction, even though the paper is candid and the pipeline testing is still useful.

Axiom & Free-Parameter Ledger

4 free parameters · 6 axioms · 0 invented entities

The paper introduces no new physical entities. Its central claim rests on several domain assumptions about quasar variability and the BLR response, plus hand-chosen JAVELIN settings that are tuned to the simulated data. The most consequential choices are the closed-loop DRW/top-hat model and the zero-dispersion input R–L relation, both of which make the mock recovery easier than a real-data application.

free parameters (4)
  • Damping timescale τ_damp = 700 days
    Fixed to the median of the simulated light curves (Section 6.1); JAVELIN cannot constrain it from sparse DESI data, so it is set by hand to make the MCMC converge.
  • Top-hat transfer function width prior = 3–40 days
    Flat prior range chosen to cover the simulation widths (w = 0.1τ) (Section 6.1).
  • Time-lag prior range = [0, 500] days observed frame
    Chosen because the maximum simulated lag is 383 days (Section 6.1); tighter priors change recovery accuracy (Appendix A).
  • Photometric baseline = 1400 days
    Selected to match current ZTF data availability; shortening to 600 days severely degrades recovery (Section 7.2).
axioms (6)
  • domain assumption DRW model describes quasar continuum variability
    Used to generate mocks (Section 5.3) and assumed by JAVELIN (Section 6.1); Section 8.1 acknowledges DRW does not hold universally.
  • domain assumption Top-hat transfer function describes C IV BLR response
    Simulated emission-line light curves use a top-hat with width 0.1τ (Section 5.3); JAVELIN assumes the same parametric form (Section 6.1).
  • domain assumption Zero intrinsic scatter in the input R–L relation
    Section 5.3 uses Hoormann et al. (2019) without adding dispersion; real C IV scatter is ~0.5 dex (Shen et al. 2024), which is not simulated.
  • domain assumption Hoormann et al. (2019) C IV R–L relation is correct
    Equation 5 sets the input lags; the recovered relation (Eq. 6) is compared against this same relation.
  • ad hoc to paper Additive stacking of MCMC posteriors is statistically valid
    Section 6.2 admits 'this is not a mathematically robust way of combining posterior distributions' and notes dependent priors; the method is justified empirically via simulations.
  • domain assumption Selected ZTF band is uncontaminated by broad lines
    Section 5.1 picks bands to avoid C IV and Mg II; other lines, FeII, and scattered BLR emission may still contaminate real photometry (Section 8.2).

pith-pipeline@v1.3.0-alltime-deepseek · 42408 in / 11882 out tokens · 108116 ms · 2026-08-01T06:24:07.747487+00:00 · methodology

0 comments
read the original abstract

The broad line region of quasars has long been probed by reverberation mapping techniques that measure time lags between continuum and broad emission line variations. Stacked reverberation mapping has been proposed as a less observationally expensive alternative to traditional methods. This ensemble approach also reduces biases from small-number statistics. The Dark Energy Spectroscopic Instrument (DESI) is conducting the most extensive spectroscopic survey of quasars to date. We create mock light curves emulating expected DESI quasar observations at redshifts $1.48<z<5.2$ and luminosities $ 44.68 \leq \log L_{1350} \lambda / \mathrm{erg\,s^{-1}} \leq 45.99 $ to test stacked reverberation mapping feasibility using sparse spectroscopic data paired with well-sampled photometric data. The pipeline, using the lag estimation code JAVELIN, successfully recovers the simulated C IV lags within one sigma of the true values using spectroscopic light curves composed of only a few spectral epochs (2-10) with irregular cadences. We investigate how observational factors, including C IV flux error magnitude, number of stacked quasars, and spectral epoch count, affect performance. This work motivates a pathway for future stacked reverberation mapping projects with large scale spectroscopic surveys of quasars having $\geq 2$ spectroscopic observations. Our results suggest an economical alternative for constraining and extending the radius-luminosity relation to higher redshifts and luminosities. Subsequently, this relation can be employed more reliably in single-epoch black hole mass measurements and quasar cosmology in these distant regimes.

Figures

Figures reproduced from arXiv: 2607.21869 by Aaron Meisner, Andrei Cuceu, Andreu Font-Ribera, Anthony Kremin, Axel de la Macorra, Benjamin Alan Weaver, Benjamin Floyd, Claire Lamman, David Alexander, David Brooks, Davide Bianchi, David Kirkby, David Schlegel, David Sprayberry, Enrique Gazta\~{n}aga, Eusebio Sanchez, Eva-Maria Mueller, Francisco Prada, Gaston Gutierrez, Graziano Rossi, Gregory Tarl\'e, Hugh McDougall, Hu Zou, Ignasi P\'erez-R\`afols, Jaime Forero-Romero, Jessica Aguilar, John Moustakas, Klaus Honscheid, Laurent Le Guillou, Ma{\l}gorzata Siudek, Martin Landriau, Michael Schubnell, Nathalie Palanque-Delabrouille, Paul Martini, Peter Clark, Ragadeepika Pucha, Rahma Alfarsy, Ramon Miquel, R.E.A. Canning, Richard Joyce, Saisrinivas Dhavala, Seshadri Nadathur, Stephanie Juneau, Steven Ahlen, Swayamtrupta Panda, Tamara Davis, Theodore Kisner, Todd Claybaugh, Tom Shanks, Victoria A. Fawcett, Wei-Jian Guo, Zhiwei Pan.

Figure 1
Figure 1. Figure 1: Plot showing the wavelengths at which the typically brighter broad emission lines found in the DESI spectrum Hα, Hβ, Mg II, and C IV are situated in the spectrum at a given redshift. The DESI observed wavelength range is from 3600Å to 9800Å. These lines also indicate the overall redshift (z) ranges the broad lines are visible in the spectrum. The shaded regions highlight the redshift ranges in which more t… view at source ↗
Figure 2
Figure 2. Figure 2: The sky coverage for the DESI quasar C IV RM sample used in this feasibility study (refer to the sample description in Section 5.1). The colour map represents the density of targets. The ZTF sky area for the r-band is shown in the purple block-shaped region with a declination range of −28°< dec < 66°. Only 1% of the quasar sample lies outside ZTF’s sky coverage. propose to use photometry data to construct … view at source ↗
Figure 3
Figure 3. Figure 3: The filter transmission functions for the g, r, and i photometric bands used in the ZTF survey. The solid black lines provide an example of the central locations of the observed frame C IV and Mg II emission lines for an object at a redshift of 2. In this case, it is important we avoid g and i bands which are contaminated by C IV and Mg II broad lines respectively. Rather, we pick the r-band to ensure the … view at source ↗
Figure 4
Figure 4. Figure 4: The luminosity-redshift distribution of the C IV RM sample is dis￾played by the purple colour map and is overlaid with the luminosity-redshift bins. The samples redshift range is 1.67 < z < 5.23. The redshift (z) lower limit at z = 1.67 is set by requiring the presence of the C IV broad line in the DESI spectrum and having a photometric band available without broad line contamination. The range of the rest… view at source ↗
Figure 5
Figure 5. Figure 5: Left: Distribution of the number of repeat observations on separate nights per target (≥2). Right: Distribution of the baseline of observations per target i.e. number of days between first observation and most recent observa￾tion. This spans the first three years of DESI data and we can start to see the observational seasons in the DESI survey. The targets observed within the first year are revisited in su… view at source ↗
Figure 6
Figure 6. Figure 6: A set of plots used to illustrate the spectroscopic and photometric cadence of the mock light curve suite. These plots have been generated from the cadences of a representative sample of 1000 mock light curve pairs. Left: A histogram of the time interval between the first and subsequent epochs of the mock spectroscopic light curves. Middle: A histogram of the time interval between the first and subsequent … view at source ↗
Figure 7
Figure 7. Figure 7: Distribution of ZTF (left) and C IV (right) fractional flux errors used to simulate the errors in the mock photometric and spectroscopic light curves respectively. Both distributions are fractional flux errors in units of 10−17 erg cm2 s −1 . the continuum and C IV light curves respectively. The ZTF error dis￾tribution is compiled from ZTF light curves of DESI counterparts, converted from r-band magnitude … view at source ↗
Figure 8
Figure 8. Figure 8: Example mock light curve pair. The solid blue lines are the underlying simulated light curves for the continuum and C IV emission line flux. The emission line light curve is the scaled and lagged continuum light curve. The orange points show how the observed light curves are sampled from the underlying light curve for ZTF photometry and DESI spectroscopy in the continuum and C IV light curves respectively.… view at source ↗
Figure 9
Figure 9. Figure 9: shows an example stacked time lag distribution with the input and recovered lag for that luminosity-redshift bin marked. Af￾ter the stacking procedure, the noisy multi-modal individual lag dis￾tributions build up to consistently reveal a clear lag peak that aligns with the input lag. The width of the lag peak is only partially ac￾counted for by the diversity of lags in the stack. In fact, there is no corre… view at source ↗
Figure 10
Figure 10. Figure 10: A flow chart summarising the mock stacked RM pipeline. The first half illustrates the various data sets needed to simulate the mock light curves. The second half presents the steps taken to measure a stacked lag from the mocks. MNRAS 000, 1–28 (2025) [PITH_FULL_IMAGE:figures/full_fig_p013_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: The recovered lag of the stacked lag posterior distributions from every luminosity-redshift bin plotted on the radius-luminosity plane. The rest frame lag is equivalent to the light travel time across the BLR. The luminosity of the points is the average luminosity of the objects used in the stack to produce that lag, this is why they roughly appear in columns centred on the middle of the luminosity bins. … view at source ↗
Figure 12
Figure 12. Figure 12: The SNR of the lag peak in the stacked lag posterior distributions of every luminosity-redshift bin as a function of luminosity (x-axis) and red￾shift (colour bar). The SNR has a strong inverse relation with luminosity. There is also an inverse relation with redshift. A best fit of the recovered lags tests whether the input R-L relation we simulated the lags with is successfully recovered. This is done by… view at source ↗
Figure 14
Figure 14. Figure 14: The absolute difference between the measured lag and the input lag of a bin against the SNR of the lag peak in the bins stacked posterior distribution. The results are shown for three runs in which the spectroscopic flux errors are multiplied by the factors: 0.5 (red circle), 1 (blue inverted triangle), and 2 (orange upright triangle). The height of the horizontal lines mark the average deviation from the… view at source ↗
Figure 16
Figure 16. Figure 16: R-L lines recovered for three runs in which the distribution of seasonal gap lengths is shifted by -100 days (dashed red), +100 days (dotted orange) and the original with no shift (solid blue). The method seems fairly resistant to the narrowing and widening the seasonal gaps. from ZTF. We probe the underlying mock light-curves with differ￾ent window-functions to investigate the impact it has on lag recov￾… view at source ↗
Figure 17
Figure 17. Figure 17: Left: Evolution of the averaged stacked lag posterior distribution as the number of objects stacked is increased from 150 to 550. The distributions have been translated so their minima align. As the number of objects stacked in the bin is increased the peak becomes more prominent, i.e. the SNR increases. Right: The evolution of the average deviation of the recovered lag from the input lag with the number … view at source ↗
Figure 18
Figure 18. Figure 18: The difference between the measured lag and the input lag of a bin against the SNR of the lag peak in the bins stacked posterior distribu￾tion. The results are shown for three runs in which the minimum number of spectroscopic points in the broad emission line light curve is increased from the base run with ≥ 2 (blue circle) observations up to ≥ 4 (grey inverted triangle), and ≥ 8 (orange upright triangle)… view at source ↗
Figure 20
Figure 20. Figure 20: The SNR of the lag peak in the stacked posterior distributions as we increase the width of the luminosity bin. A strong correlation is not present. we do not find a strong correlation between the SNR and the width of the luminosity bin. There is a shallow slope towards the expected trend that larger bins sizes reduce the SNR of the lag peak - this is due to the inclusion of a more diverse range of quasars… view at source ↗
Figure 21
Figure 21. Figure 21: Average of all the posterior distributions across all time-lag bins. The peak is shifted to shorter lags (maximum at 55 days) in comparison to the expected average of the peaks of the stacked posterior distributions (maximum at 108 days). This is a manifestation of the systematic overdensity at the lower lag end present in most of the stacked lag posterior distribution. The underlying distribution beneath… view at source ↗
Figure 22
Figure 22. Figure 22: A stacked cross-correlation peak distribution (CCPD) from 250 quasars in a bin with average luminosity at 1350Å of 6.1×1045ergs−1 and average redshift 2.76. The average input lag is 187 days (solid blue line) and the recovered lag is very close at 189 days (solid orange line). The stacked CCPD is purposely shown for a bin which is similar to the bin used to cre￾ate the stacked lag distribution in [PITH_F… view at source ↗
Figure 23
Figure 23. Figure 23: An adapted flow chart to show how stacked RM can be done using cross correlation functions. This first part of the pipeline that produces the mock light curves is identical to [PITH_FULL_IMAGE:figures/full_fig_p022_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: The recovered lag of each stacked CCPD plotted on the radius￾luminosity plane. Details same as [PITH_FULL_IMAGE:figures/full_fig_p023_24.png] view at source ↗
Figure 26
Figure 26. Figure 26: The recovered radius-luminosity relation using CCFs shown as a solid red line. Details same as [PITH_FULL_IMAGE:figures/full_fig_p024_26.png] view at source ↗

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

159 extracted references · 30 canonical work pages · 3 internal anchors

  1. [1]

    Aigrain S., Foreman-Mackey D., 2023, @doi [ ] 10.1146/annurev-astro-052920-103508 , https://ui.adsabs.harvard.edu/abs/2023ARA&A..61..329A 61, 329

    doi:10.1146/annurev-astro-052920-103508 2023

  2. [2]

    arXiv:1302.1508

    Alexander T., 2013, @doi [arXiv e-prints] 10.48550/arXiv.1302.1508 , https://ui.adsabs.harvard.edu/abs/2013arXiv1302.1508A p. arXiv:1302.1508

    Pith/arXiv arXiv doi:10.48550/arxiv.1302.1508 2013

  3. [3]

    M., et al., 2023, @doi [ ] 10.3847/1538-3881/acacfc , https://ui.adsabs.harvard.edu/abs/2023AJ....165..124A 165, 124

    Alexander D. M., et al., 2023, @doi [ ] 10.3847/1538-3881/acacfc , https://ui.adsabs.harvard.edu/abs/2023AJ....165..124A 165, 124

    doi:10.3847/1538-3881/acacfc 2023

  4. [4]

    arXiv:2405.12518

    Bacon R., et al., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2405.12518 , https://ui.adsabs.harvard.edu/abs/2024arXiv240512518B p. arXiv:2405.12518

    Pith/arXiv arXiv doi:10.48550/arxiv.2405.12518 2024

  5. [5]

    N., Kozlovsky B.-Z., Salpeter E

    Bahcall J. N., Kozlovsky B.-Z., Salpeter E. E., 1972, @doi [ ] 10.1086/151300 , https://ui.adsabs.harvard.edu/abs/1972ApJ...171..467B 171, 467

    doi:10.1086/151300 1972

  6. [6]

    Bao D.-W., et al., 2022, @doi [ ] 10.3847/1538-4365/ac7beb , https://ui.adsabs.harvard.edu/abs/2022ApJS..262...14B 262, 14

    doi:10.3847/1538-4365/ac7beb 2022

  7. [7]

    J., et al., 2015, @doi [ ] 10.1088/0067-0049/217/2/26 , https://ui.adsabs.harvard.edu/abs/2015ApJS..217...26B 217, 26

    Barth A. J., et al., 2015, @doi [ ] 10.1088/0067-0049/217/2/26 , https://ui.adsabs.harvard.edu/abs/2015ApJS..217...26B 217, 26

    doi:10.1088/0067-0049/217/2/26 2015

  8. [8]

    C., et al., 2019, @doi [ ] 10.1088/1538-3873/aaecbe , https://ui.adsabs.harvard.edu/abs/2019PASP..131a8002B 131, 018002

    Bellm E. C., et al., 2019, @doi [ ] 10.1088/1538-3873/aaecbe , https://ui.adsabs.harvard.edu/abs/2019PASP..131a8002B 131, 018002

    doi:10.1088/1538-3873/aaecbe 2019

  9. [9]

    M., Eracleous M., 2025, @doi [ ] 10.3847/1538-4357/addec0 , https://ui.adsabs.harvard.edu/abs/2025ApJ...988...27B 988, 27

    Benati Gon c alves H., Panda S., Storchi Bergmann T., Cackett E. M., Eracleous M., 2025, @doi [ ] 10.3847/1538-4357/addec0 , https://ui.adsabs.harvard.edu/abs/2025ApJ...988...27B 988, 27

    work page doi:10.3847/1538-4357/addec0 2025

  10. [10]

    C., et al., 2008, @doi [ ] 10.1086/595719 , https://ui.adsabs.harvard.edu/abs/2008ApJ...689L..21B 689, L21

    Bentz M. C., et al., 2008, @doi [ ] 10.1086/595719 , https://ui.adsabs.harvard.edu/abs/2008ApJ...689L..21B 689, L21

    doi:10.1086/595719 2008

  11. [11]

    C., et al., 2013, @doi [ ] 10.1088/0004-637X/767/2/149 , https://ui.adsabs.harvard.edu/abs/2013ApJ...767..149B 767, 149

    Bentz M. C., et al., 2013, @doi [ ] 10.1088/0004-637X/767/2/149 , https://ui.adsabs.harvard.edu/abs/2013ApJ...767..149B 767, 149

    doi:10.1088/0004-637x/767/2/149 2013

  12. [12]

    C., Markham M., Rosborough S., Onken C

    Bentz M. C., Markham M., Rosborough S., Onken C. A., Street R., Valluri M., Treu T., 2023, @doi [ ] 10.3847/1538-4357/ad08b8 , https://ui.adsabs.harvard.edu/abs/2023ApJ...959...25B 959, 25

    work page doi:10.3847/1538-4357/ad08b8 2023

  13. [13]

    D., McKee C

    Blandford R. D., McKee C. F., 1982, @doi [ ] 10.1086/159843 , https://ui.adsabs.harvard.edu/abs/1982ApJ...255..419B 255, 419

    doi:10.1086/159843 1982

  14. [14]

    J., Elliott T

    Brewer B. J., Elliott T. M., 2014, @doi [ ] 10.1093/mnrasl/slt174 , https://ui.adsabs.harvard.edu/abs/2014MNRAS.439L..31B 439, L31

    doi:10.1093/mnrasl/slt174 2014

  15. [15]

    Brodzeller A., et al., 2023, @doi [ ] 10.3847/1538-3881/ace35d , https://ui.adsabs.harvard.edu/abs/2023AJ....166...66B 166, 66

    doi:10.3847/1538-3881/ace35d 2023

  16. [16]

    Chaussidon E., et al., 2023, @doi [ ] 10.3847/1538-4357/acb3c2 , https://ui.adsabs.harvard.edu/abs/2023ApJ...944..107C 944, 107

    doi:10.3847/1538-4357/acb3c2 2023

  17. [17]

    Chelouche D., Pozo-Nu \ n ez F., Zucker S., 2017, @doi [ ] 10.3847/1538-4357/aa7b86 , https://ui.adsabs.harvard.edu/abs/2017ApJ...844..146C 844, 146

    work page doi:10.3847/1538-4357/aa7b86 2017

  18. [18]

    Cirasuolo M., et al., 2020, @doi [The Messenger] 10.18727/0722-6691/5195 , https://ui.adsabs.harvard.edu/abs/2020Msngr.180...10C 180, 10

    doi:10.18727/0722-6691/5195 2020

  19. [19]

    Clavel J., et al., 1991, @doi [ ] 10.1086/169540 , https://ui.adsabs.harvard.edu/abs/1991ApJ...366...64C 366, 64

    doi:10.1086/169540 1991

  20. [20]

    M., Vestergaard M., 2006, @doi [ ] 10.1051/0004-6361:20064878 , https://ui.adsabs.harvard.edu/abs/2006A&A...456...75C 456, 75

    Collin S., Kawaguchi T., Peterson B. M., Vestergaard M., 2006, @doi [ ] 10.1051/0004-6361:20064878 , https://ui.adsabs.harvard.edu/abs/2006A&A...456...75C 456, 75

    doi:10.1051/0004-6361:20064878 2006

  21. [21]

    Cristiani S., Trentini S., La Franca F., Andreani P., 1997, @doi [ ] 10.48550/arXiv.astro-ph/9610108 , https://ui.adsabs.harvard.edu/abs/1997A&A...321..123C 321, 123

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.astro-ph/9610108 1997

  22. [22]

    T., 1999, in Poutanen J., Svensson R., eds, Astronomical Society of the Pacific Conference Series Vol

    Czerny B., Janiuk A., R \'o za \'n ska A., Zycki P. T., 1999, in Poutanen J., Svensson R., eds, Astronomical Society of the Pacific Conference Series Vol. 161, High Energy Processes in Accreting Black Holes. p. 331

    1999

  23. [23]

    Czerny B., et al., 2023a, @doi [ ] 10.1007/s10509-023-04165-7 , https://ui.adsabs.harvard.edu/abs/2023Ap&SS.368....8C 368, 8

    doi:10.1007/s10509-023-04165-7

  24. [24]

    Czerny B., et al., 2023b, @doi [ ] 10.1051/0004-6361/202345844 , https://ui.adsabs.harvard.edu/abs/2023A&A...675A.163C 675, A163

    doi:10.1051/0004-6361/202345844

  25. [25]

    arXiv:1611.00036

    DESI Collaboration et al., 2016a, @doi [arXiv e-prints] 10.48550/arXiv.1611.00036 , https://ui.adsabs.harvard.edu/abs/2016arXiv161100036D p. arXiv:1611.00036

    Pith/arXiv arXiv doi:10.48550/arxiv.1611.00036

  26. [26]

    arXiv:1611.00037

    DESI Collaboration et al., 2016b, @doi [arXiv e-prints] 10.48550/arXiv.1611.00037 , https://ui.adsabs.harvard.edu/abs/2016arXiv161100037D p. arXiv:1611.00037

    Pith/arXiv arXiv doi:10.48550/arxiv.1611.00037

  27. [27]

    DESI Collaboration et al., 2022, @doi [ ] 10.3847/1538-3881/ac882b , https://ui.adsabs.harvard.edu/abs/2022AJ....164..207D 164, 207

    doi:10.3847/1538-3881/ac882b 2022

  28. [28]

    arXiv:2404.03000

    DESI Collaboration et al., 2024a, @doi [arXiv e-prints] 10.48550/arXiv.2404.03000 , https://ui.adsabs.harvard.edu/abs/2024arXiv240403000D p. arXiv:2404.03000

    Pith/arXiv arXiv doi:10.48550/arxiv.2404.03000

  29. [29]

    arXiv:2411.12021

    DESI Collaboration et al., 2024b, @doi [arXiv e-prints] 10.48550/arXiv.2411.12021 , https://ui.adsabs.harvard.edu/abs/2024arXiv241112021D p. arXiv:2411.12021

    Pith/arXiv arXiv doi:10.48550/arxiv.2411.12021

  30. [30]

    DESI Collaboration et al., 2024c, @doi [ ] 10.3847/1538-3881/ad0b08 , https://ui.adsabs.harvard.edu/abs/2024AJ....167...62D 167, 62

    doi:10.3847/1538-3881/ad0b08

  31. [31]

    DESI Collaboration et al., 2024d, @doi [ ] 10.3847/1538-3881/ad3217 , https://ui.adsabs.harvard.edu/abs/2024AJ....168...58D 168, 58

    doi:10.3847/1538-3881/ad3217

  32. [32]

    arXiv:2503.14745

    DESI Collaboration et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2503.14745 , https://ui.adsabs.harvard.edu/abs/2025arXiv250314745D p. arXiv:2503.14745

    Pith/arXiv arXiv doi:10.48550/arxiv.2503.14745 2025

  33. [33]

    Dalla Bont \`a E., et al., 2020, @doi [ ] 10.3847/1538-4357/abbc1c , https://ui.adsabs.harvard.edu/abs/2020ApJ...903..112D 903, 112

    doi:10.3847/1538-4357/abbc1c 2020

  34. [34]

    De Cicco D., et al., 2022, @doi [ ] 10.1051/0004-6361/202142750 , https://ui.adsabs.harvard.edu/abs/2022A&A...664A.117D 664, A117

    doi:10.1051/0004-6361/202142750 2022

  35. [35]

    De Rosa G., et al., 2015, @doi [ ] 10.1088/0004-637X/806/1/128 , https://ui.adsabs.harvard.edu/abs/2015ApJ...806..128D 806, 128

    doi:10.1088/0004-637x/806/1/128 2015

  36. [36]

    Dehghanian M., et al., 2019, @doi [ ] 10.3847/2041-8213/ab3d41 , https://ui.adsabs.harvard.edu/abs/2019ApJ...882L..30D 882, L30

    doi:10.3847/2041-8213/ab3d41 2019

  37. [37]

    D., et al., 2009, @doi [ ] 10.1088/0004-637X/704/2/L80 , https://ui.adsabs.harvard.edu/abs/2009ApJ...704L..80D 704, L80

    Denney K. D., et al., 2009, @doi [ ] 10.1088/0004-637X/704/2/L80 , https://ui.adsabs.harvard.edu/abs/2009ApJ...704L..80D 704, L80

    doi:10.1088/0004-637x/704/2/l80 2009

  38. [38]

    D., et al., 2010, @doi [ ] 10.1088/0004-637X/721/1/715 , https://ui.adsabs.harvard.edu/abs/2010ApJ...721..715D 721, 715

    Denney K. D., et al., 2010, @doi [ ] 10.1088/0004-637X/721/1/715 , https://ui.adsabs.harvard.edu/abs/2010ApJ...721..715D 721, 715

    doi:10.1088/0004-637x/721/1/715 2010

  39. [39]

    Dey A., et al., 2019, @doi [ ] 10.3847/1538-3881/ab089d , https://ui.adsabs.harvard.edu/abs/2019AJ....157..168D 157, 168

    doi:10.3847/1538-3881/ab089d 2019

  40. [40]

    Dietrich M., et al., 1993, @doi [ ] 10.1086/172599 , https://ui.adsabs.harvard.edu/abs/1993ApJ...408..416D 408, 416

    work page doi:10.1086/172599 1993

  41. [41]

    Donnan F., 2021, ROA: Running Optimal Average , Astrophysics Source Code Library, record ascl:2107.002

    2021

  42. [42]

    Du P., Wang J.-M., 2019, @doi [ ] 10.3847/1538-4357/ab4908 , https://ui.adsabs.harvard.edu/abs/2019ApJ...886...42D 886, 42

    doi:10.3847/1538-4357/ab4908 2019

  43. [43]

    Du P., et al., 2016, @doi [ ] 10.3847/0004-637X/825/2/126 , https://ui.adsabs.harvard.edu/abs/2016ApJ...825..126D 825, 126

    doi:10.3847/0004-637x/825/2/126 2016

  44. [44]

    A., Krolik J

    Edelson R. A., Krolik J. H., 1988, @doi [ ] 10.1086/166773 , https://ui.adsabs.harvard.edu/abs/1988ApJ...333..646E 333, 646

    doi:10.1086/166773 1988

  45. [45]

    M., Gelbord J., Horne K., Goad M., McHardy I., Vaughan S., Vestergaard M., 2024, @doi [ ] 10.3847/1538-4357/ad64d4 , https://ui.adsabs.harvard.edu/abs/2024ApJ...973..152E 973, 152

    Edelson R., Peterson B. M., Gelbord J., Horne K., Goad M., McHardy I., Vaughan S., Vestergaard M., 2024, @doi [ ] 10.3847/1538-4357/ad64d4 , https://ui.adsabs.harvard.edu/abs/2024ApJ...973..152E 973, 152

    doi:10.3847/1538-4357/ad64d4 2024

  46. [46]

    Fagin J., et al., 2024, @doi [ ] 10.3847/1538-4357/ad2988 , https://ui.adsabs.harvard.edu/abs/2024ApJ...965..104F 965, 104

    doi:10.3847/1538-4357/ad2988 2024

  47. [47]

    Fine S., et al., 2012, @doi [ ] 10.1111/j.1365-2966.2012.21248.x , https://ui.adsabs.harvard.edu/abs/2012MNRAS.427.2701F 427, 2701

    arXiv 2012

  48. [48]

    Fine S., et al., 2013, @doi [ ] 10.1093/mnrasl/slt069 , https://ui.adsabs.harvard.edu/abs/2013MNRAS.434L..16F 434, L16

    work page doi:10.1093/mnrasl/slt069 2013

  49. [49]

    Fonseca Alvarez G., et al., 2020, @doi [ ] 10.3847/1538-4357/aba001 , https://ui.adsabs.harvard.edu/abs/2020ApJ...899...73F 899, 73

    doi:10.3847/1538-4357/aba001 2020

  50. [50]

    W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    Foreman-Mackey D., Hogg D. W., Lang D., Goodman J., 2013, @doi [ ] 10.1086/670067 , https://ui.adsabs.harvard.edu/abs/2013PASP..125..306F 125, 306

    doi:10.1086/670067 2013

  51. [51]

    arXiv:2501.16311

    Frohmaier C., et al., 2025, @doi [arXiv e-prints] 10.48550/arXiv.2501.16311 , https://ui.adsabs.harvard.edu/abs/2025arXiv250116311F p. arXiv:2501.16311

    Pith/arXiv arXiv doi:10.48550/arxiv.2501.16311 2025

  52. [52]

    GRAVITY Collaboration et al., 2024a, @doi [ ] 10.1051/0004-6361/202348167 , https://ui.adsabs.harvard.edu/abs/2024A&A...684A.167G 684, A167

    doi:10.1051/0004-6361/202348167

  53. [53]

    GRAVITY Collaboration et al., 2024b, @doi [ ] 10.1051/0004-6361/202450746 , https://ui.adsabs.harvard.edu/abs/2024A&A...690A..76G 690, A76

    doi:10.1051/0004-6361/202450746

  54. [54]

    M., Peterson B

    Gaskell C. M., Peterson B. M., 1987, @doi [ ] 10.1086/191216 , https://ui.adsabs.harvard.edu/abs/1987ApJS...65....1G 65, 1

    doi:10.1086/191216 1987

  55. [55]

    Gravity Collaboration et al., 2018, @doi [ ] 10.1038/s41586-018-0731-9 , https://ui.adsabs.harvard.edu/abs/2018Natur.563..657G 563, 657

    doi:10.1038/s41586-018-0731-9 2018

  56. [56]

    J., et al., 2019, @doi [ ] 10.3847/1538-4357/ab4ea5 , https://ui.adsabs.harvard.edu/abs/2019ApJ...887...38G 887, 38

    Grier C. J., et al., 2019, @doi [ ] 10.3847/1538-4357/ab4ea5 , https://ui.adsabs.harvard.edu/abs/2019ApJ...887...38G 887, 38

    doi:10.3847/1538-4357/ab4ea5 2019

  57. [57]

    Guy J., et al., 2023, @doi [ ] 10.3847/1538-3881/acb212 , https://ui.adsabs.harvard.edu/abs/2023AJ....165..144G 165, 144

    doi:10.3847/1538-3881/acb212 2023

  58. [58]

    Haas M., Chini R., Ramolla M., Pozo Nu \ n ez F., Westhues C., Watermann R., Hoffmeister V., Murphy M., 2011, @doi [ ] 10.1051/0004-6361/201117325 , https://ui.adsabs.harvard.edu/abs/2011A&A...535A..73H 535, A73

    doi:10.1051/0004-6361/201117325 2011

  59. [59]

    Hawkins M. R. S., Taylor A. N., 1997, @doi [ ] 10.1086/310689 , https://ui.adsabs.harvard.edu/abs/1997ApJ...482L...5H 482, L5

    work page doi:10.1086/310689 1997

  60. [60]

    Homayouni Y., et al., 2020, @doi [ ] 10.3847/1538-4357/ababa9 , https://ui.adsabs.harvard.edu/abs/2020ApJ...901...55H 901, 55

    doi:10.3847/1538-4357/ababa9 2020

  61. [61]

    K., et al., 2019, @doi [ ] 10.1093/mnras/stz1539 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.3650H 487, 3650

    Hoormann J. K., et al., 2019, @doi [ ] 10.1093/mnras/stz1539 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.487.3650H 487, 3650

    doi:10.1093/mnras/stz1539 2019

  62. [62]

    C., Siegmund O

    Horne K., 2003, in Blades J. C., Siegmund O. H. W., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 4854, Future EUV/UV and Visible Space Astrophysics Missions and Instrumentation.. pp 262--273 ( @eprint arXiv astro-ph/0301250 ), @doi 10.1117/12.460261

    Pith/arXiv arXiv 2003

  63. [63]

    Observational Requirements for High-Fidelity Reverberation Mapping

    Horne K., Peterson B. M., Collier S. J., Netzer H., 2002, @doi [arXiv e-prints] 10.48550/arXiv.astro-ph/0201182 , https://ui.adsabs.harvard.edu/abs/2002astro.ph..1182H pp astro--ph/0201182

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.astro-ph/0201182 2002

  64. [64]

    Horne K., et al., 2021, @doi [ ] 10.3847/1538-4357/abce60 , https://ui.adsabs.harvard.edu/abs/2021ApJ...907...76H 907, 76

    doi:10.3847/1538-4357/abce60 2021

  65. [65]

    Hu C., et al., 2015, @doi [ ] 10.1088/0004-637X/804/2/138 , https://ui.adsabs.harvard.edu/abs/2015ApJ...804..138H 804, 138

    work page doi:10.1088/0004-637x/804/2/138 2015

  66. [66]

    Ivezi \'c Z ., et al., 2019, @doi [ ] 10.3847/1538-4357/ab042c , https://ui.adsabs.harvard.edu/abs/2019ApJ...873..111I 873, 111

    doi:10.3847/1538-4357/ab042c 2019

  67. [67]

    K., Prince R., Pandey A., Naddaf M

    Jaiswal V. K., Prince R., Pandey A., Naddaf M. H., Czerny B., Panda S., Mandal A. K., Pozo Nunez F., 2024, @doi [arXiv e-prints] 10.48550/arXiv.2410.03597 , https://ui.adsabs.harvard.edu/abs/2024arXiv241003597J p. arXiv:2410.03597

    work page doi:10.48550/arxiv.2410.03597 2024

  68. [68]

    Jiang L., et al., 2016, @doi [ ] 10.3847/1538-4357/833/2/222 , https://ui.adsabs.harvard.edu/abs/2016ApJ...833..222J 833, 222

    doi:10.3847/1538-4357/833/2/222 2016

  69. [69]

    Jiang Y., Wu X.-B., Ma Q., Gu H., Wen Y., 2024, @doi [ ] 10.3847/1538-4357/ad36c0 , https://ui.adsabs.harvard.edu/abs/2024ApJ...966..149J 966, 149

    work page doi:10.3847/1538-4357/ad36c0 2024

  70. [70]

    Jin S., et al., 2024, @doi [ ] 10.1093/mnras/stad557 , https://ui.adsabs.harvard.edu/abs/2024MNRAS.530.2688J 530, 2688

    doi:10.1093/mnras/stad557 2024

  71. [71]

    M., Vestergaard M., Jannuzi B

    Kaspi S., Maoz D., Netzer H., Peterson B. M., Vestergaard M., Jannuzi B. T., 2005, @doi [ ] 10.1086/431275 , https://ui.adsabs.harvard.edu/abs/2005ApJ...629...61K 629, 61

    doi:10.1086/431275 2005

  72. [72]

    N., Maoz D., Netzer H., Schneider D

    Kaspi S., Brandt W. N., Maoz D., Netzer H., Schneider D. P., Shemmer O., 2007, @doi [ ] 10.1086/512094 , https://ui.adsabs.harvard.edu/abs/2007ApJ...659..997K 659, 997

    doi:10.1086/512094 2007

  73. [73]

    N., Maoz D., Netzer H., Schneider D

    Kaspi S., Brandt W. N., Maoz D., Netzer H., Schneider D. P., Shemmer O., Grier C. J., 2021, @doi [ ] 10.3847/1538-4357/ac00aa , https://ui.adsabs.harvard.edu/abs/2021ApJ...915..129K 915, 129

    doi:10.3847/1538-4357/ac00aa 2021

  74. [74]

    C., Bechtold J., Siemiginowska A., 2009, @doi [ ] 10.1088/0004-637X/698/1/895 , https://ui.adsabs.harvard.edu/abs/2009ApJ...698..895K 698, 895

    Kelly B. C., Bechtold J., Siemiginowska A., 2009, @doi [ ] 10.1088/0004-637X/698/1/895 , https://ui.adsabs.harvard.edu/abs/2009ApJ...698..895K 698, 895

    doi:10.1088/0004-637x/698/1/895 2009

  75. [75]

    L., Czerny B., Ratra B., 2021, @doi [ ] 10.1093/mnras/stab2807 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508.4722K 508, 4722

    Khadka N., Yu Z., Zaja c ek M., Martinez-Aldama M. L., Czerny B., Ratra B., 2021, @doi [ ] 10.1093/mnras/stab2807 , https://ui.adsabs.harvard.edu/abs/2021MNRAS.508.4722K 508, 4722

    doi:10.1093/mnras/stab2807 2021

  76. [76]

    M., Denney K

    Kilerci Eser E., Vestergaard M., Peterson B. M., Denney K. D., Bentz M. C., 2015, @doi [ ] 10.1088/0004-637X/801/1/8 , https://ui.adsabs.harvard.edu/abs/2015ApJ...801....8K 801, 8

    doi:10.1088/0004-637x/801/1/8 2015

  77. [77]

    L., et al., 2015, @doi [ ] 10.1093/mnras/stv1718 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.453.1701K 453, 1701

    King A. L., et al., 2015, @doi [ ] 10.1093/mnras/stv1718 , https://ui.adsabs.harvard.edu/abs/2015MNRAS.453.1701K 453, 1701

    doi:10.1093/mnras/stv1718 2015

  78. [78]

    A., et al., 2006, @doi [ ] 10.1086/505646 , https://ui.adsabs.harvard.edu/abs/2006ApJ...648..128K 648, 128

    Kollmeier J. A., et al., 2006, @doi [ ] 10.1086/505646 , https://ui.adsabs.harvard.edu/abs/2006ApJ...648..128K 648, 128

    doi:10.1086/505646 2006

  79. [79]

    T., Goad M

    Korista K. T., Goad M. R., 2019, @doi [ ] 10.1093/mnras/stz2330 , https://ui.adsabs.harvard.edu/abs/2019MNRAS.489.5284K 489, 5284

    doi:10.1093/mnras/stz2330 2019

  80. [80]

    C ., Dimitrijevi \'c M

    Kova c evi \'c J., Popovi \'c L. C ., Dimitrijevi \'c M. S., 2010, @doi [ ] 10.1088/0067-0049/189/1/15 , https://ui.adsabs.harvard.edu/abs/2010ApJS..189...15K 189, 15

    doi:10.1088/0067-0049/189/1/15 2010

Showing first 80 references.