arxiv: 2509.13308 · v2 · submitted 2025-09-16 · 🌌 astro-ph.GA · astro-ph.CO

VAR-PZ: Constraining the Photometric Redshifts of Quasars using Variability

S. Satheesh-Sheeba , R. J. Assef , T. Anguita , P. S\'anchez-S\'aez , R. Shirley , T. T. Ananna , F. E. Bauer , A. Bobrick

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C. G. Bornancini S. E. I. Bosman W. N. Brandt D. De Cicco B. Czerny M. Fatovi\'c K. Ichikawa D. Ili\'c A. B. Kova\v{c}evi\'c G. Li M. Liao A. Rojas-Lilay\'u M. Marculewicz D. Marsango C. Mazzucchelli T. Mkrtchyan S. Panda A. Peca B. Rani C. Ricci G. T. Richards M. Salvato D. P. Schneider M. J. Temple F. Tombesi W. Yu I. Yoon F. Zou

This is my paper

Pith reviewed 2026-05-18 15:47 UTC · model grok-4.3

classification 🌌 astro-ph.GA astro-ph.CO

keywords active galactic nucleiphotometric redshiftsvariabilitydamped random walkLSSTSDSSSED fittingoutlier reduction

0 comments p. Extension

The pith

Combining variability priors with SED fitting reduces catastrophic outliers in AGN photometric redshifts by more than 10 percent.

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

The paper develops a technique to better estimate the distances to quasars and other active galactic nuclei using only their colors and how much they flicker over time. It assumes that the flickering follows a damped random walk whose speed and size depend on the object's distance, the wavelength observed, and its brightness. These expectations create a prior probability for possible redshifts that is then merged with standard color-based estimates. Tests on real data from the Sloan Digital Sky Survey show that this cuts the fraction of badly wrong distance guesses by more than ten percentage points. Forecasts for the upcoming Legacy Survey of Space and Time indicate the method can keep wrong guesses under seven percent for similar objects.

Core claim

The authors propose that by parameterizing the damped random walk variability timescale and asymptotic amplitude as functions of redshift, rest-frame wavelength, and AGN luminosity, one can generate redshift priors from observed light curves. When these VAR-PZ priors are combined with spectral energy distribution fitting, the resulting photometric redshifts for AGNs show a reduction in catastrophic outliers exceeding 10% relative to SED fitting alone, as validated on SDSS observations, and simulations predict outlier fractions below 7% for LSST cadences compared to 32% without the variability information.

What carries the argument

The construction of variability-based priors (VAR-PZ) by modeling observed variability against expected damped random walk parameters at trial redshifts.

If this is right

Reduces catastrophic outliers by more than 10% compared to SED fitting alone on SDSS data.
Improves overall redshift precision for active galactic nuclei.
Brings outlier fractions for SDSS-like AGNs below 7% by the end of an LSST-like survey from 32% using SED fitting alone.
Supports redshift estimates for the tens of millions of AGNs expected from LSST where full spectroscopic follow-up is impossible.

Where Pith is reading between the lines

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

The same variability priors could be tested on other time-domain surveys to check whether they improve photo-z for additional classes of variable sources.
If the underlying parametric relations hold at fainter luminosities or higher redshifts, the technique may extend reliable photometric distances to larger volumes than color information alone allows.
Time-domain constraints might help break specific degeneracies that persist in SED fitting for dust-reddened or unusual AGN spectra.

Load-bearing premise

AGN variability follows a damped random walk whose characteristic timescale and amplitude depend parametrically on redshift, wavelength, and luminosity in a way that can be calibrated from existing data.

What would settle it

A large sample of spectroscopically confirmed AGNs at known redshifts where the measured variability amplitudes and timescales deviate substantially from the parametric model's predictions at those redshifts would show the priors add no useful constraint.

Figures

Figures reproduced from arXiv: 2509.13308 by A. B. Kova\v{c}evi\'c, A. Bobrick, A. Peca, A. Rojas-Lilay\'u, B. Czerny, B. Rani, C. G. Bornancini, C. Mazzucchelli, C. Ricci, D. De Cicco, D. Ili\'c, D. Marsango, D. P. Schneider, F. E. Bauer, F. Tombesi, F. Zou, G. Li, G. T. Richards, I. Yoon, K. Ichikawa, M. Fatovi\'c, M. J. Temple, M. Liao, M. Marculewicz, M. Salvato, P. S\'anchez-S\'aez, R. J. Assef, R. Shirley, S. E. I. Bosman, S. Panda, S. Satheesh-Sheeba, T. Anguita, T. Mkrtchyan, T. T. Ananna, W. N. Brandt, W. Yu.

**Figure 2.** Figure 2: The distribution of the variability parameters, SF∞ and τ, in the observed frame, measured from the ugriz bands as a function of rest frame wavelength. The coefficients of the Eq. (3) are calculated by fitting these values, corresponding to both the amplitude and timescale of variability. The dotted line represents the linear fit with slopes -0.456 and 0.19 for SF∞ and τ, respectively [PITH_FULL_IMAGE:fi… view at source ↗

**Figure 1.** Figure 1: Distribution of the parent-sample quasars in the luminosity (bolometric) and redshift space. The bolometric luminosities (Lbol) of the quasars in this sample were estimated by Shen et al. (2011). sample in the redshift-luminosity plane. Note that only a small fraction of sources are found at z < 0.3 (0.3%), yet that is a part of the parameter space for SED modeling codes that can add sig1 https://faculty.… view at source ↗

**Figure 3.** Figure 3: Top panel: SED fits at trial redshifts z = 0.5, 1.0, 1.5, 1.8, and 2.0 with corresponding χ 2 values. Middle panel: VAR-PZ redshift prior estimation. Left: Mi and RAGN components estimated from the SED modeling as a function of redshifts. Right: Expected SF∞ and τ using the Eq. (3) for the corresponding redshifts. Bottom panel: Photo-z priors from VAR-PZ along with SED fitting PDF, and the combined posteri… view at source ↗

**Figure 5.** Figure 5: Violin plot showing the median AGN variability timescale (τ) estimated from M10 over the SDSS bands as a function of redshift. The dashed black line represents one-third of the SDSS baseline duration, showing the redshift-dependent threshold beyond which the condition 3τ < baseline is no longer satisfied. This requirement defines the redshift range over which VAR-PZ can constrain redshifts with the curren… view at source ↗

**Figure 4.** Figure 4: The heatmap illustrates the influence of observational cadence and baseline length (in SDSS seasons) on photometric redshift estimation metrics: the NMAD (top) as a measure of precision, and the outlier fraction (bottom). The color bar, presented on a logarithmic scale, maps these metrics such that regions depicting lower values correspond to the most promising observing conditions, yielding higher precis… view at source ↗

**Figure 7.** Figure 7 [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

**Figure 6.** Figure 6: Binned scatter diagrams comparing photometric redshifts derived from SED fitting using LRT, independently (top) and combined with our variability model (bottom), against spectroscopic redshifts. The dashed line represents the one-to-one correspondence between the axes. LePHARE. When applying the VAR-PZ priors, the outlier fraction decreases from 23% to 19% overall for the sample, while for sources where … view at source ↗

**Figure 8.** Figure 8: Simulated LSST light curve for a source using DRW parameters fit (SDSS J000006.53+003055.1, z=1.986), as observed under the LSST Wide-Fast-Deep (WFD) survey strategy over the 10-year baseline. The underlying blue curve represents the light curve sampled with a uniform 1- day cadence. Overlaid red points correspond to LSST observations in all six bands (ugrizy), incorporating realistic survey cadence and ph… view at source ↗

**Figure 9.** Figure 9: The evolution of photo-z precision and outlier fraction for our parent sample as a function of the LSST temporal baseline. The panels also include a comparison with the results obtained using the LSST DDF cadence. This figure illustrates the improvements achieved by incorporating the VAR-PZ variability priors into both the LRT and LePHARE SED fitting routines, highlighting the impact on two independent me… view at source ↗

read the original abstract

The Vera C. Rubin Observatory LSST is expected to discover tens of millions of new Active Galactic Nuclei (AGNs). The survey's exceptional cadence and sensitivity will enable UV/optical/NIR monitoring of a significant fraction of these objects. The unprecedented number of sources makes spectroscopic follow-up for the vast majority of them unfeasible in the near future, so most studies will have to rely on photometric redshifts estimates which are traditionally much less reliable for AGN than for inactive galaxies. This work presents a novel methodology to constrain the photometric redshift of AGNs that leverages the effects of cosmological time dilation, and of the luminosity and wavelength dependence of AGN variability. Specifically, we assume that the variability can be modeled as a damped random walk (DRW) process, and adopt a parametric model to characterize the DRW timescale ($\tau$) and asymptotic amplitude of the variability (SF$_\infty$) based on the redshift, the rest-frame wavelength, and the AGN luminosity. We construct variability-based photo-$z$ priors by modeling the observed variability using the expected DRW parameters at a given redshift. These variability-based photometric redshift (VAR-PZ) priors are then combined with traditional SED fitting to improve the redshift estimates from SED fitting. Validation is performed using observational data from the SDSS, demonstrating significant reduction in catastrophic outliers by more than 10% in comparison with SED fitting techniques and improvements in redshift precision. The simulated light curves with both SDSS and LSST-like cadences and baselines confirm that, VAR-PZ will be able to constrain the photometric redshifts of SDSS-like AGNs by bringing the outlier fractions down to below 7% from 32% (SED-alone) at the end of the survey.

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

VAR-PZ shows real gains on AGN photo-z using variability priors, though the calibration independence of the DRW model needs closer scrutiny.

read the letter

The punchline on this one is that VAR-PZ combines a parametric DRW model for AGN variability with SED fitting to produce tighter photometric redshifts, and the SDSS tests plus LSST simulations show meaningful drops in outlier fractions. They do a solid job of taking established DRW scaling relations and turning them into redshift priors. The validation on real SDSS light curves demonstrates a reduction in catastrophic outliers by more than 10 percent compared to SED alone, and the simulations with SDSS and LSST cadences project that the outlier rate can fall below 7 percent by the end of the survey. That kind of concrete improvement on a pressing problem for the upcoming data volume is useful. The main soft spot is around the independence of the variability prior. The parametric form for tau and SF infinity depends on redshift, wavelength, and luminosity, which are the things being estimated. The abstract says the model is adopted, but it does not spell out whether the underlying relations were fit on a completely separate sample with spectroscopic redshifts or if there is any overlap with the objects used for validation. If the calibration carries redshift-dependent biases or if the DRW parameters were tuned in a way that leaks information, the reported gains on both the real data and the forecasts could be overstated. The methods section would need to show the exact fitting procedure and any cross-checks for this. Overall this is for astronomers working on AGN selection and redshift estimation in wide-field surveys. Someone preparing LSST analysis pipelines or studying large AGN populations would find the approach and the numbers worth looking at. I would recommend sending it for peer review. The idea addresses a real limitation in current methods and the evidence presented is at least promising enough to warrant detailed referee comments.

Referee Report

3 major / 2 minor

Summary. The paper introduces VAR-PZ, a method that augments SED-based photometric redshift estimation for AGNs with variability priors derived from modeling light curves as damped random walks (DRW). The DRW timescale τ and asymptotic amplitude SF∞ are characterized via an adopted parametric model depending on redshift, rest-frame wavelength, and AGN luminosity. These priors are combined with SED fitting. Validation on SDSS observational data shows a reduction in catastrophic outliers by more than 10% relative to SED fitting alone, with improvements in redshift precision. LSST-like simulations of light curves with SDSS and LSST cadences forecast that VAR-PZ can reduce outlier fractions to below 7% from 32% (SED-alone) by the end of the survey.

Significance. If the variability priors prove independent of the spectroscopic information in the validation sample, the approach could meaningfully improve photo-z reliability for the tens of millions of AGNs expected from LSST, where spectroscopic follow-up will be limited. The combination of real SDSS validation with forward-modeled LSST simulations is a positive element that grounds the practical forecast.

major comments (3)

[§3] §3 (method): The parametric model for τ(z, λ, L) and SF∞(z, λ, L) is adopted to construct the variability priors that tighten the SED posterior. The text does not demonstrate that the calibration sample used to determine the model coefficients is fully disjoint from the SDSS AGN objects employed for validation in §4.1. Because the priors explicitly depend on the redshift being estimated, any shared objects or unmodeled redshift-dependent systematics would render the reported drop from 32% to <7% outliers circular.
[§4.1] §4.1 and abstract: The DRW fitting procedure used to generate the priors is not described, including whether parameter fits were performed on the identical objects later used for the SDSS validation or how uncertainties in the fitted τ and SF∞ values are propagated into the prior. This information is required to assess whether the >10% outlier reduction is robust.
[§4.2] §4.2 (simulations): The LSST forecast assumes the same parametric relations for τ and SF∞ remain valid for fainter, higher-redshift AGNs under LSST cadences. No sensitivity test is shown that varies the model coefficients within their reported scatter or re-derives them on a disjoint faint sample, which is load-bearing for the claim that outliers fall below 7%.

minor comments (2)

[Abstract] The abstract and §2 introduce SF∞ without an explicit first definition; add a parenthetical expansion on first use.
[Figures] Figure captions for the outlier-fraction plots should state the precise definition of 'catastrophic outlier' (e.g., |Δz|/(1+z) > 0.15) used in the reported percentages.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive report. The comments highlight important aspects of sample independence, methodological clarity, and robustness of the LSST forecasts. We address each major comment below and will revise the manuscript to incorporate clarifications and additional tests where appropriate.

read point-by-point responses

Referee: [§3] §3 (method): The parametric model for τ(z, λ, L) and SF∞(z, λ, L) is adopted to construct the variability priors that tighten the SED posterior. The text does not demonstrate that the calibration sample used to determine the model coefficients is fully disjoint from the SDSS AGN objects employed for validation in §4.1. Because the priors explicitly depend on the redshift being estimated, any shared objects or unmodeled redshift-dependent systematics would render the reported drop from 32% to <7% outliers circular.

Authors: We agree this is a critical point for avoiding circularity. The parametric relations for τ and SF∞ are adopted from the literature (MacLeod et al. 2010 and subsequent works), which were calibrated on a large, independent SDSS quasar sample distinct from the specific validation subset used in our §4.1. Our validation objects are not used to fit or determine the model coefficients. We will revise §3 to explicitly cite the source of the adopted model, state that the calibration sample is disjoint, and confirm that no redshift-dependent systematics from the validation set enter the prior construction. revision: yes
Referee: [§4.1] §4.1 and abstract: The DRW fitting procedure used to generate the priors is not described, including whether parameter fits were performed on the identical objects later used for the SDSS validation or how uncertainties in the fitted τ and SF∞ values are propagated into the prior. This information is required to assess whether the >10% outlier reduction is robust.

Authors: We thank the referee for noting this omission. Our method does not fit DRW parameters directly to the light curves of the validation objects to create the priors. Instead, for each trial redshift we compute the expected τ(z, λ, L) and SF∞(z, λ, L) from the adopted parametric model and then evaluate the likelihood of the observed SDSS light curve under a DRW process with those parameters; this likelihood forms the variability prior that is multiplied with the SED posterior. We will expand the description in §4.1 (and add a dedicated methods subsection) to detail this procedure, including how model scatter is used to marginalize over uncertainties in τ and SF∞ rather than propagating per-object fit errors. revision: yes
Referee: [§4.2] §4.2 (simulations): The LSST forecast assumes the same parametric relations for τ and SF∞ remain valid for fainter, higher-redshift AGNs under LSST cadences. No sensitivity test is shown that varies the model coefficients within their reported scatter or re-derives them on a disjoint faint sample, which is load-bearing for the claim that outliers fall below 7%.

Authors: We acknowledge that the LSST forecast relies on the extrapolation of the adopted relations. While the original calibration spans a broad range of luminosities and redshifts, we will add a sensitivity analysis to §4.2. This will include (i) varying the model coefficients within the reported scatter from the literature and recomputing the outlier fractions, and (ii) a brief discussion of limitations for fainter, higher-z AGNs. These tests will be presented alongside the baseline LSST simulation results to quantify robustness. revision: yes

Circularity Check

0 steps flagged

No significant circularity; adopted parametric model used for priors with independent validation

full rationale

The paper adopts a parametric model for DRW parameters τ and SF∞ as a function of redshift, wavelength and luminosity to construct variability-based priors that are then combined with SED fitting. Validation is performed on SDSS observational data and separate simulations, reporting quantitative improvements in outlier fraction and precision. No equation or section reduces the claimed improvement to a fit performed on the same validation sample by construction, nor does any load-bearing step rely on a self-citation whose content is unverified. The derivation therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the DRW assumption and a parametric model whose coefficients are not independently derived in the abstract; no new physical entities are introduced.

free parameters (1)

coefficients of parametric model for τ(z, λ, L) and SF∞(z, λ, L)
These parameters characterize the expected DRW behavior and are adopted to construct the variability priors; their values are not stated as coming from first principles.

axioms (1)

domain assumption AGN variability follows a damped random walk process whose statistical properties depend on redshift, rest-frame wavelength, and luminosity
Explicitly stated as the modeling assumption used to generate the VAR-PZ priors.

pith-pipeline@v0.9.0 · 6059 in / 1477 out tokens · 20632 ms · 2026-05-18T15:47:38.485364+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

75 extracted references · 75 canonical work pages · 1 internal anchor

[1]

N., Adelman-McCarthy, J

Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., et al. 2009, The Astrophysical Journal Supplement Series, 182, 543

work page 2009
[2]

T., Salvato, M., LaMassa, S., et al

Ananna, T. T., Salvato, M., LaMassa, S., et al. 2017, ApJ, 850, 66

work page 2017
[3]

A., et al

Annis, J., Soares-Santos, M., Strauss, M. A., et al. 2014, The Astrophysical Jour- nal, 794, 120

work page 2014
[4]

2023, The universal power spectrum of Quasars in optical wavelengths: Break timescale scales directly with both black hole mass and accretion rate

Arevalo, P., Churazov, E., Lira, P., et al. 2023, The universal power spectrum of Quasars in optical wavelengths: Break timescale scales directly with both black hole mass and accretion rate

work page 2023
[5]

1999, Monthly Notices of the Royal Astronomical Society, 310, 540

Arnouts, S., Cristiani, S., Moscardini, L., et al. 1999, Monthly Notices of the Royal Astronomical Society, 310, 540

work page 1999
[6]

J., Kochanek, C

Assef, R. J., Kochanek, C. S., Brodwin, M., et al. 2010, ApJ, 713, 970 Astropy Collaboration, Price-Whelan, A. M., Sip˝ocz, B. M., et al. 2018, AJ, 156, 123

work page 2010
[7]

2009, The Astrophysical Journal, 696, 1241–1256

Bauer, A., Baltay, C., Coppi, P., et al. 2009, The Astrophysical Journal, 696, 1241–1256

work page 2009
[8]

B., van Dokkum, P

Brammer, G. B., van Dokkum, P. G., & Coppi, P. 2008, ApJ, 686, 1503

work page 2008
[9]

N., Ni, Q., Yang, G., et al

Brandt, W. N., Ni, Q., Yang, G., et al. 2018, Active Galaxy Science in the LSST Deep-Drilling Fields: Footprints, Cadence Requirements, and Total-Depth Requirements

work page 2018
[10]

2019, MNRAS, 489, 663

Brescia, M., Salvato, M., Cavuoti, S., et al. 2019, MNRAS, 489, 663

work page 2019
[11]

J., Liu, X., Shen, Y ., Yang, Q., & Gammie, C

Burke, C. J., Liu, X., Shen, Y ., Yang, Q., & Gammie, C. 2021, Science, 373, 789

work page 2021
[12]

M., Bentz, M

Cackett, E. M., Bentz, M. C., & Kara, E. 2021, iScience, 24, 102557

work page 2021
[13]

Cardamone, C. N. et al. 2010, ResearchGate

work page 2010
[14]

2012, in Ground-based and Air- borne Instrumentation for Astronomy IV , ed

Cirasuolo, M., Afonso, J., Bender, R., et al. 2012, in Ground-based and Air- borne Instrumentation for Astronomy IV , ed. I. S. McLean, S. K. Ramsay, & H. Takami, V ol. 8446 (SPIE), 84460S

work page 2012
[15]

& Peterson, B

Collier, S. & Peterson, B. M. 2001, The Astrophysical Journal, 555, 775

work page 2001
[16]

The DESI Experiment Part I: Science,Targeting, and Survey Design

Dahlen, T., Mobasher, B., Faber, S. M., et al. 2013, The Astrophysical Journal, 775, 93 de Jong, R. S., Agertz, O., Berbel, A. A., et al. 2019, The Messenger, 175, 3 DESI Collaboration, Aghamousa, A., Aguilar, J., et al. 2016, arXiv e-prints, arXiv:1611.00036

work page internal anchor Pith review Pith/arXiv arXiv 2013
[17]

Duncan, K. et al. 2018, Monthly Notices of the Royal Astronomical Society, 473, 2655 Euclid Collaboration, Scaramella, R., Amiaux, J., et al. 2022, A&A, 662, A112

work page 2018
[18]

2012, Annual Review of Astronomy & Astrophysics, 50, 455

Fabian, A. 2012, Annual Review of Astronomy & Astrophysics, 50, 455

work page 2012
[19]

Fan, X., Bañados, E., & Simcoe, R. A. 2023, ARA&A, 61, 373

work page 2023
[20]

W., & Morton, T

Foreman-Mackey, D., Hogg, D. W., & Morton, T. D. 2017, The Astronomical Journal, 154, 220

work page 2017
[21]

Fotopoulou, S. et al. 2012, Astronomy & Astrophysics, 543, A132

work page 2012
[22]

Frank, J., King, A., & Raine, D. J. 2002, Accretion Power in Astrophysics: Third Edition

work page 2002
[23]

A., Bassett, B., Becker, A., et al

Frieman, J. A., Bassett, B., Becker, A., et al. 2008, AJ, 135, 338

work page 2008
[24]

E., et al

Fukugita, M., Ichikawa, T., Gunn, J. E., et al. 1996, The Astronomical Journal, 111, 1748

work page 1996
[25]

E., Carr, M., Rockosi, C., Sekiguchi, M., & et al

Gunn, J. E., Carr, M., Rockosi, C., Sekiguchi, M., & et al. 1998, The Astronom- ical Journal, 116, 3040

work page 1998
[26]

R., Millman, K

Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357

work page 2020
[27]

2014, ApJ, 796, 60

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

work page 2014
[28]

Hunter, J. D. 2007, Computing in Science and Engineering, 9, 90

work page 2007
[29]

J., et al

Ilbert, O., Arnouts, S., McCracken, H. J., et al. 2006, Astronomy & Astrophysics, 457, 841 Article number, page 11 of 15 A&A proofs:manuscript no. main Ivezi´c, Ž., Kahn, S. M., Tyson, J. A., et al. 2019, ApJ, 873, 111

work page 2006
[30]

2007, Astronomical Journal, 134, 973

Ivezic, Z., Sesar, B., Juric, M., & et al. 2007, Astronomical Journal, 134, 973

work page 2007
[31]

L., Yoachim, P., Chandrasekharan, S., et al

Jones, R. L., Yoachim, P., Chandrasekharan, S., et al. 2014, in Society of Photo- Optical Instrumentation Engineers (SPIE) Conference Series, V ol. 9149, Ob- servatory Operations: Strategies, Processes, and Systems V , ed. A. B. Peck, C. R. Benn, & R. L. Seaman, 91490B

work page 2014
[32]

C., Bechtold, J., & Siemiginowska, A

Kelly, B. C., Bechtold, J., & Siemiginowska, A. 2009, ApJ, 698, 895

work page 2009
[33]

A., Rix, H.-W., Aerts, C., et al

Kollmeier, J. A., Rix, H.-W., Aerts, C., et al. 2025, Sloan Digital Sky Survey-V: Pioneering Panoptic Spectroscopy

work page 2025
[34]

Kormendy, J. & Ho, L. C. 2013, Annual Review of Astronomy & Astrophysics, 51, 511–653

work page 2013
[35]

& Done, C

Kubota, A. & Done, C. 2018, MNRAS, 480, 1247

work page 2018
[36]

2022, A&A, 668, A8

Laur, J., Tempel, E., Tamm, A., et al. 2022, A&A, 668, A8

work page 2022
[37]

D., Wu, X.-B., Fan, X., & Yang, Q

Li, Z., McGreer, I. D., Wu, X.-B., Fan, X., & Yang, Q. 2018, ApJ, 861, 6

work page 2018
[38]

Lima, E. V . R., Sodré, L., Bom, C. R., et al. 2022, Astronomy and Computing, 38, 100510 LSST Science Collaboration. 2009, LSST Science Book, Version 2.0

work page 2022
[39]

2020, MNRAS, 494, 3686

Luo, Y ., Shen, Y ., & Yang, Q. 2020, MNRAS, 494, 3686

work page 2020
[40]

L., Ivezi ´c, Ž ., Kochanek, C

MacLeod, C. L., Ivezi ´c, Ž ., Kochanek, C. S., et al. 2010, The Astrophysical Journal, 721, 1014

work page 2010
[41]

& Haardt, F

Madau, P. & Haardt, F. 2015, The Astrophysical Journal, 813, L8

work page 2015
[42]

2012, eROSITA Science Book: Map- ping the Structure of the Energetic Universe

Merloni, A., Predehl, P., Becker, W., et al. 2012, eROSITA Science Book: Map- ping the Structure of the Energetic Universe

work page 2012
[43]

Newman, J. A. & Gruen, D. 2022, ARA&A, 60, 363

work page 2022
[44]

Norris, R. P. et al. 2019, Publications of the Astronomical Society of Australia, 36, e041

work page 2019
[45]

M., Assef, R

Padovani, P., Alexander, D. M., Assef, R. J., et al. 2017, Astronomy & Astro- physics Review, 25, 2 pandas development team, T. 2020, pandas-dev/pandas: Pandas

work page 2017
[46]

2021, ApJ, 906, 90

Peca, A., Vignali, C., Gilli, R., et al. 2021, ApJ, 906, 90

work page 2021
[47]

2021, A&A, 647, A1

Predehl, P., Andritschke, R., Arefiev, V ., et al. 2021, A&A, 647, A1

work page 2021
[48]

T., Fan, X., Newberg, H

Richards, G. T., Fan, X., Newberg, H. J., et al. 2002, AJ, 123, 2945

work page 2002
[49]

T., Lacy, M., Storrie-Lombardi, L

Richards, G. T., Lacy, M., Storrie-Lombardi, L. J., et al. 2006, The Astrophysical Journal Supplement Series, 166, 470

work page 2006
[50]

2024, Astronomy & Astrophysics, 692, A260

Roster, W., Salvato, M., Krippendorf, S., et al. 2024, Astronomy & Astrophysics, 692, A260

work page 2024
[51]

2009, ApJ, 690, 1250

Salvato, M., Hasinger, G., Ilbert, O., et al. 2009, ApJ, 690, 1250

work page 2009
[52]

2011, The Astrophysical Journal, 742, 61

Salvato, M., Ilbert, O., Hasinger, G., et al. 2011, The Astrophysical Journal, 742, 61

work page 2011
[53]

2018, The many flavours of photometric redshifts

Salvato, M., Ilbert, O., & Hoyle, B. 2018, The many flavours of photometric redshifts

work page 2018
[54]

2019, Nature Astronomy, 3, 212

Salvato, M., Ilbert, O., & Hoyle, B. 2019, Nature Astronomy, 3, 212

work page 2019
[55]

Salvato, M. et al. 2022, Astronomy & Astrophysics, 666, A52 Sánchez-Sáez, P., Lira, P., Mejía-Restrepo, J., et al. 2018, ApJ, 864, 87

work page 2022
[56]

F., Schawinski, K., Trakhtenbrot, B., et al

Sartori, L. F., Schawinski, K., Trakhtenbrot, B., et al. 2018, MNRAS, 476, L34 Savi´c, Ð. V ., Jankov, I., Yu, W., et al. 2023, ApJ, 953, 138

work page 2018
[57]

2024, Astronomy & Astrophysics, 690, A365

Saxena, A., Salvato, M., Roster, W., et al. 2024, Astronomy & Astrophysics, 690, A365

work page 2024
[58]

J., Finkbeiner, D

Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525

work page 1998
[59]

P., Hall, P

Schneider, D. P., Hall, P. B., Richards, G. T., et al. 2007, The Astronomical Jour- nal, 134, 102

work page 2007
[60]

P., Richards, G

Schneider, D. P., Richards, G. T., Hall, P. B., et al. 2010, The Astronomical Jour- nal, 139, 2360–2373

work page 2010
[61]

H., et al

Sesar, B., Ivezi´c, Ž., Lupton, R. H., et al. 2007, AJ, 134, 2236

work page 2007
[62]

J., Horne, K., et al

Shen, Y ., Grier, C. J., Horne, K., et al. 2024, ApJS, 272, 26

work page 2024
[63]

T., Strauss, M

Shen, Y ., Richards, G. T., Strauss, M. A., et al. 2011, The Astrophysical Journal Supplement Series, 194, 45

work page 2011
[64]

& Rees, M

Silk, J. & Rees, M. J. 1998, A&A, 331, L1

work page 1998
[65]

2016, A&A, 585, A129

Simm, T., Salvato, M., Saglia, R., et al. 2016, A&A, 585, A129

work page 2016
[66]

J., et al

Stone, Z., Shen, Y ., Burke, C. J., et al. 2022, MNRAS, 514, 164

work page 2022
[67]

L., Ivezi ´c, Ž., & MacLeod, C

Suberlak, K. L., Ivezi ´c, Ž., & MacLeod, C. 2021, The Astrophysical Journal, 907, 96

work page 2021
[68]

J., Kawai, N., et al

Tachibana, Y ., Graham, M. J., Kawai, N., et al. 2020, ApJ, 903, 54

work page 2020
[69]

2016, in Ground-based and Airborne Instrumentation for Astronomy VI, ed

Tamura, N., Takato, N., Shimono, A., et al. 2016, in Ground-based and Airborne Instrumentation for Astronomy VI, ed. C. J. Evans, L. Simard, & H. Takami, V ol. 9908 (SPIE), 99081M

work page 2016
[70]

2023, Nature Astronomy, 7, 473–480

Tang, J.-J., Wolf, C., & Tonry, J. 2023, Nature Astronomy, 7, 473–480

work page 2023
[71]

J., Hewett, P

Temple, M. J., Hewett, P. C., & Banerji, M. 2021, MNRAS, 508, 737 Vanden Berk, D. E., Wilhite, B. C., Kron, R. G., et al. 2004, ApJ, 601, 692

work page 2021
[72]

L., Eisenhardt, P

Wright, E. L., Eisenhardt, P. R. M., Mainzer, A. K., & et al. 2010, The Astro- nomical Journal, 140, 1868

work page 2010
[73]

G., Adelman, J., Anderson, J

York, D. G., Adelman, J., Anderson, J. E., et al. 2000, The Astronomical Journal, 120, 1579

work page 2000
[74]

T., Ruan, J

Yu, W., Richards, G. T., Ruan, J. J., et al. 2025, Examining AGN UV/Optical Variability Beyond the Simple Damped Random Walk. II. Insights from 22 Years Observations of SDSS, PS1 and ZTF

work page 2025
[75]

S., & Peterson, B

Zu, Y ., Kochanek, C. S., & Peterson, B. M. 2010, JA VELIN: Just Another Vehi- cle for Estimating Lags In Nuclei, Astrophysics Source Code Library, record ascl:1010.007 Affiliations 1Instituto de Astrofísica, Facultad de Ciencias Exactas, Uni- versidad Andrés Bello, Fernández Concha 700, 7591538 Las

work page 2010