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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features

As of 7 August 2026, this Paper Citation Record lists 100 of 195 outbound references and 0 inbound Pith citation observations for arXiv:2607.16434.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.16434 v1

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measured 100 of 195 reference resolution

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Reference resolution

100 of 195 outbound references displayed

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Outbound references

Observation 8f097963-da9d-479b-8fd7-8d97d0c605a7 · outbound

This paper cites The Ensemble Photometric Variability of ~25000 Quasars in the Sloan Digital Sky Survey.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features The Ensemble Photometric Variability of ~25000 Quasars in the Sloan Digital Sky Survey

Reference 1

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Observation d27a9483-5bc8-462a-8d1b-e39006ae2aa7 · outbound

This paper cites , keywords =.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features , keywords =

Reference 2

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Observation df7db799-6518-4062-a037-81ab7082b831 · outbound

This paper cites doi:10.1086/324541 , url =.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features doi:10.1086/324541 , url =

Reference 3

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Observation 5559c1a6-0707-4f26-809f-a97a71bb74aa · outbound

This paper cites Flexible and Scalable Methods for Quantifying Stochastic Variability in the Era of Massive Time-Domain Astronomical Data Sets.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Flexible and Scalable Methods for Quantifying Stochastic Variability in the Era of Massive Time-Domain Astronomical Data Sets

Reference 4

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Observation 451d45a8-1164-4e48-836a-7c1eb0fb8ec4 · outbound

This paper cites Multiwavelength Monitoring of the Dwarf Seyfert 1 Galaxy NGC 4395. I. A Reverberation-Based Measurement of the Black Hole Mass.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Multiwavelength Monitoring of the Dwarf Seyfert 1 Galaxy NGC 4395. I. A Reverberation-Based Measurement of the Black Hole Mass

Reference 5

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Observation 296650ba-25c9-46f6-abbf-f602fa996dcd · outbound

This paper cites Pereyra and Daniel E.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Pereyra and Daniel E

Reference 6

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Observation 9177eecb-f7b5-4098-834b-f5b6f4e80e5b · outbound

This paper cites , keywords =.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features , keywords =

Reference 7

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Observation d5695d51-c476-4661-8b03-91a3ab3dec40 · outbound

This paper cites The Effect of a Time-Varying Accretion Disk Size on Quasar Microlensing Light Curves.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features The Effect of a Time-Varying Accretion Disk Size on Quasar Microlensing Light Curves

Reference 8

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Observation dcafb7f7-ab68-41ac-9337-03c515398fa3 · outbound

This paper cites VAR-PZ: Constraining the Photometric Redshifts of Quasars using Variability.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features VAR-PZ: Constraining the Photometric Redshifts of Quasars using Variability

Reference 9

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Observation 9adfbaea-a502-4661-944b-a3719ede3363 · outbound

This paper cites Are the Variations in Quasar Optical Flux Driven by Thermal Fluctuations?.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Are the Variations in Quasar Optical Flux Driven by Thermal Fluctuations?

Reference 10

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Observation 9ae38af4-8d07-4d21-8223-5ad933c8a1d4 · outbound

This paper cites Peterson , title =.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Peterson , title =

Reference 11

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Observation e8bd6ed6-b09f-4143-b61e-28bf0ee2ec62 · outbound

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features , title = "

Reference 12

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Observation 21e4f6fe-b1e3-4cbf-84e8-702bc30fe027 · outbound

This paper cites Selecting Quasars by their Intrinsic Variability.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Selecting Quasars by their Intrinsic Variability

Reference 13

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This paper cites Quasar Variability Measurements With SDSS Repeated Imaging and POSS Data.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Quasar Variability Measurements With SDSS Repeated Imaging and POSS Data

Reference 14

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Observation 3414fc69-7314-4c43-acef-a4ceeeb7c199 · outbound

This paper cites Structure Function Analysis of Long Term Quasar Variability.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Structure Function Analysis of Long Term Quasar Variability

Reference 15

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Observation c35b51c4-2d78-487a-8654-bfda715cdc82 · outbound

This paper cites Variable Faint Optical Sources Discovered by Comparing POSS and SDSS Catalogs.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Variable Faint Optical Sources Discovered by Comparing POSS and SDSS Catalogs

Reference 17

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Observation 8df6041c-f018-4444-98cb-b758a6a02f0e · outbound

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features QUASAR OPTICAL VARIABILITY IN THE PALOMAR-QUEST SURVEY , volume=

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Unresolved cited work

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features , keywords =

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features , keywords =

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features , keywords =

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features , keywords =

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features , keywords =

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features , year = 1994, month = may, volume =

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features The Optical Variability of QSO's

Reference 26

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features The QSO variability-luminosity-redshift relation

Reference 27

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features doi:10.5281/zenodo.6878414 , url =

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Fabian , title =

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features American Astronomical Society Meeting Abstracts \#221 , year = 2013, series =

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Unresolved cited work

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Low Resolution Spectral Templates For AGNs and Galaxies From 0.03 -- 30 microns

Reference 33

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Kass and Adrian E

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features An Alternative Approach To Measuring Reverberation Lags in Active Galactic Nuclei

Reference 35

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Application of Stochastic Modeling to Analysis of Photometric Reverberation Mapping Data

Reference 36

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Quasar Accretion Disk Sizes From Continuum Reverberation Mapping From the Dark Energy Survey

Reference 37

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features , year = 1963, month = jul, volume =

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Gebhardt and R

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Ferrarese and D

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Kormendy and L

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Observation 30b2b24e-f64a-4bb4-911d-e6096f0b549f · outbound

This paper cites Bulletin of the American Astronomical Society , year = 2019, volume =.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Bulletin of the American Astronomical Society , year = 2019, volume =

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This paper cites The DESI Experiment Part I: Science,Targeting, and Survey Design.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features The DESI Experiment Part I: Science,Targeting, and Survey Design

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This paper cites Ground-based and airborne instrumentation for astronomy VI , volume=.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Ground-based and airborne instrumentation for astronomy VI , volume=

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features 4MOST: Project overview and information for the First Call for Proposals

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features 2012 , eprint=

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This paper cites The eROSITA X-ray telescope on SRG.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features The eROSITA X-ray telescope on SRG

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features and Ferguson, Henry C

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features LSST: from Science Drivers to Reference Design and Anticipated Data Products

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This paper cites Euclid preparation: I. The Euclid Wide Survey.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Euclid preparation: I. The Euclid Wide Survey

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This paper cites Photometric Redshifts for Next-Generation Surveys.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Photometric Redshifts for Next-Generation Surveys

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features and Afonso, J

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features and Salvato, M

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Photometric redshifts for X-ray-selected active galactic nuclei in the eROSITA era

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This paper cites The Multiwavelength Survey by Yale-Chile (MUSYC): Deep Medium-Band optical imaging and high quality 32-band photometric redshifts in the ECDF-S.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features The Multiwavelength Survey by Yale-Chile (MUSYC): Deep Medium-Band optical imaging and high quality 32-band photometric redshifts in the ECDF-S

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This paper cites Photometry and Photometric Redshift catalogs for the Lockman Hole Deep Field.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Photometry and Photometric Redshift catalogs for the Lockman Hole Deep Field

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This paper cites CANDELS/GOODS-S, CDFS, ECDFS: Photometric Redshifts For Normal and for X-Ray-Detected Galaxies.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features CANDELS/GOODS-S, CDFS, ECDFS: Photometric Redshifts For Normal and for X-Ray-Detected Galaxies

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This paper cites AGN Populations in Large Volume X-ray Surveys: Photometric Redshifts and Population Types found in the Stripe 82X Survey.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features AGN Populations in Large Volume X-ray Surveys: Photometric Redshifts and Population Types found in the Stripe 82X Survey

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This paper cites Photometric redshifts for the next generation of deep radio continuum surveys - I: Template fitting.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Photometric redshifts for the next generation of deep radio continuum surveys - I: Template fitting

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features , volume =

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This paper cites , volume =.

VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features , volume =

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features and Salvato, M

Reference 100

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features and Wang, S

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features The Pan-STARRS1 Surveys

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