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

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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

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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 doi:10.1086/324541 , url =

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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

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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

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

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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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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

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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

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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?

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

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

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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

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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

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

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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

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This paper cites QUASAR OPTICAL VARIABILITY IN THE PALOMAR-QUEST SURVEY , volume=.

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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This paper cites The Optical Variability of QSO's.

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

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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

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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

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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

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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

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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

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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 A Survey of z>5.7 Quasars in the Sloan Digital Sky Survey II: Discovery of Three Additional Quasars at z>6

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

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

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Variable Point Sources in Sloan Digital Sky Survey Stripe 82. I. Project Description and Initial Catalog (0 h < R.A. < 4 h)

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features A Modified Magnitude System that Produces Well-Behaved Magnitudes, Colors, and Errors Even for Low Signal-to-Noise Ratio Measurements

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VAR-PZnn: A machine-learning framework for AGN photometric redshifts using color and variability-based features Burke and Xin Liu and Yue Shen and Qian Yang and Charles Gammie , title =

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

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