Searching for Changing-state AGNs in Massive Data Sets. I. Applying Deep Learning and Anomaly-detection Techniques to Find AGNs with Anomalous Variability Behaviors
Open accessAbstract
The classic classification scheme for active galactic nuclei (AGNs) was recently challenged by the discovery of the so-called changing-state (changing-look) AGNs. The physical mechanism behind this phenomenon is still a matter of open debate and the samples are too small and of serendipitous nature to provide robust answers. In order to tackle this problem, we need to design methods that are able to detect AGNs right in the act of changing state. Here we present an anomaly-detection technique designed to identify AGN light curves with anomalous behaviors in massive data sets. The main aim of this technique is to identify CSAGN at different stages of the transition, but it can also be used for more general purposes, such as cleaning massive data sets for AGN variability analyses. We used light curves from the Zwicky Transient Facility data release 5 (ZTF DR5), containing a sample of 230,451 AGNs of different classes. The ZTF DR5 light curves were modeled with a Variational Recurrent Autoencoder (VRAE) architecture, that allowed us to obtain a set of attributes from the VRAE latent space that describes the general behavior of our sample. These attributes were then used as features for an Isolation Forest (IF) algorithm that is an anomaly detector for a “one class” kind of problem. We used the VRAE reconstruction errors and the IF anomaly score to select a sample of 8809 anomalies. These anomalies are dominated by bogus candidates, but we were able to identify 75 promising CSAGN candidates.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Sánchez-Sáez et al. (2021) used a Variational Recurrent Autoencoder to identify changing-look Active Galactic Nuclei.
“A Variational Recurrent Autoencoder is also used by Sánchez-Sáez et al. (2021) to identify changing-look Active Galactic Nuclei.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Sánchez-Sáez et al. (2021)
Sánchez-Sáez et al. (2021) applied the approach to real observations from the ZTF survey.
“The approach is analogous but it is applied to real observations from the ZTF survey.”
From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Sánchez-Sáez et al. (2021)
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249 · see citing works
The NumPy Array: A Structure for Efficient Numerical Computation
van der Walt, S., Colbert, S. C., & Varoquaux, G. P. (2011) · Computing in Science & Engineering · doi:10.1109/mcse.2011.37 · Cited by 10,445 · see citing works
The Sloan Digital Sky Survey: Technical Summary
York, D. G., Adelman-McCarthy, J., Anderson, J. E., Anderson, S. F., Annis, J., Bahcall, N. A., Bakken, J. A., Barkhouser, R. H., Bastian, S., Berman, E., et al. (2000) · The Astronomical Journal · doi:10.1086/301513 · Cited by 10,392 · see citing works
LSST: From Science Drivers to Reference Design and Anticipated Data Products
Ivezić, Ž., Kahn, S. M., Tyson, J. A., Abel, B., Acosta, E., Allsman, R., Alonso, D., AlSayyad, Y., Anderson, S. F., Andrew, J., et al. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab042c · Cited by 4,357 · see citing works
X-Ray Properties of Black-Hole Binaries
Remillard, R. A., & McClintock, J. E. (2006) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev.astro.44.051905.092532 · Cited by 2,419 · see citing works
The SIMBAD astronomical database
Wenger, M., Ochsenbein, F., Egret, D., Dubois, P., Bonnarel, F., Borde, S., Genova, F., Jasniewicz, G., Laloë, S., Lesteven, S., et al. (2000) · Astronomy & Astrophysics Supplement Series · doi:10.1051/aas:2000332 · Cited by 2,345 · see citing works
The Zwicky Transient Facility: System Overview, Performance, and First Results
Bellm, E. C., Kulkarni, S. R., Graham, M. J., Dekany, R., Smith, R. M., Riddle, R., Masci, F. J., Hélou, G., Prince, T. A., Adams, S. M., et al. (2018) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/aaecbe · Cited by 2,077 · see citing works
Galactic Nuclei as Collapsed Old Quasars
Lynden-Bell, D. (1969) · Nature · doi:10.1038/223690a0 · Cited by 1,494 · see citing works
Towards a unified model for black hole X-ray binary jets
Fender, R. P., Belloni, T., & Gallo, E. (2004) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2004.08384.x · Cited by 1,382 · see citing works
Modelling the behaviour of accretion flows in X-ray binaries
Done, C., Gierliński, M., & Kubota, A. (2007) · The Astronomy and Astrophysics Review · doi:10.1007/s00159-007-0006-1 · Cited by 1,319 · see citing works
FIRST RESULTS FROM THE CATALINA REAL-TIME TRANSIENT SURVEY
Drake, A. J., Djorgovski, S. G., Mahabal, A., Beshore, E., Larson, S., Graham, M. J., Williams, R., Christensen, E., Catelan, M., Boattini, A., et al. (2009) · The Astrophysical Journal · doi:10.1088/0004-637x/696/1/870 · Cited by 1,318 · see citing works
The Zwicky Transient Facility: Data Processing, Products, and Archive
Masci, F. J., Laher, R. R., Rusholme, B., Shupe, D. L., Groom, S. L., Surace, J., Jackson, E., Monkewitz, S., Beck, R., Flynn, D., et al. (2018) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/aae8ac · Cited by 1,269 · see citing works
Waskom, M., Botvinnik, O. B., O'Kane, D., Hobson, P., Ostblom, J., Lukauskas, S., Gemperline, D. C., Augspurger, T., Halchenko, Y., Cole, J. B., et al. (2014) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.592845 · Cited by 1,119 · see citing works
VARIABILITY OF ACTIVE GALACTIC NUCLEI
Ulrich, M.-H., Maraschi, L., & Urry, C. M. (1997) · Annual Review of Astronomy and Astrophysics · doi:10.1146/annurev.astro.35.1.445 · Cited by 940 · see citing works
ARE THE VARIATIONS IN QUASAR OPTICAL FLUX DRIVEN BY THERMAL FLUCTUATIONS?
Kelly, B. C., Bechtold, J., & Siemiginowska, A. (2009) · The Astrophysical Journal · doi:10.1088/0004-637x/698/1/895 · Cited by 814 · see citing works
MODELING THE TIME VARIABILITY OF SDSS STRIPE 82 QUASARS AS A DAMPED RANDOM WALK
MacLeod, C. L., Ivezić, Ž., Kochanek, C. S., Kozłowski, S., Kelly, B., Bullock, E., Kimball, A., Sesar, B., Westman, D., Brooks, K., et al. (2010) · The Astrophysical Journal · doi:10.1088/0004-637x/721/2/1014 · Cited by 700 · see citing works
Active galactic nuclei as scaled-up Galactic black holes
McHardy, I. M., Körding, E. G., Knigge, C., Uttley, P., & Fender, R. P. (2006) · Nature · doi:10.1038/nature05389 · Cited by 675 · see citing works
Active galactic nuclei: what’s in a name?
Padovani, P., Alexander, D. M., Assef, R. J., Marco, B. D., Giommi, P., Hickox, R. C., Richards, G. T., Smolčić, V., Hatziminaoglou, E., Mainieri, V., et al. (2017) · The Astronomy and Astrophysics Review · doi:10.1007/s00159-017-0102-9 · Cited by 674 · see citing works
STUDIES IN ASTRONOMICAL TIME SERIES ANALYSIS. VI. BAYESIAN BLOCK REPRESENTATIONS
Scargle, J. D., Norris, J. P., Jackson, B., & Chiang, J. (2013) · The Astrophysical Journal · doi:10.1088/0004-637x/764/2/167 · Cited by 662 · see citing works
Active galactic nuclei: what’s in a name?
Padovani, P., Alexander, D. M., Assef, R. J., Marco, B. D., Paolo Giommi, B., Hickox, R. C., Richards, G. T., Smolčić, V., Hatziminaoglou, E., Mainieri, V., et al. (2017) · MPG.PuRe (Max Planck Society) · Cited by 494 · see citing works
The Ensemble Photometric Variability of ∼25,000 Quasars in the Sloan Digital Sky Survey
Vanden Berk, D. E., Wilhite, B. C., Kron, R. G., Anderson, S. F., Brunner, R. J., Hall, P. B., Ivezić, Ž., Richards, G. T., Schneider, D. P., York, D. G., et al. (2004) · The Astrophysical Journal · doi:10.1086/380563 · Cited by 486 · see citing works
LaMassa, S. M., Cales, S., Moran, E. C., Myers, A. D., Richards, G. T., Eracleous, M., Heckman, T. M., Gallo, L., & Urry, C. M. (2015) · The Astrophysical Journal · doi:10.1088/0004-637x/800/2/144 · Cited by 451 · see citing works
The 5th edition of the Roma-BZCAT. A short presentation
Enrico Massaro, A., Maselli, A., Leto, C., Marchegiani, ¶., Perri, M., Paolo Giommi, B., & Piranomonte, S. (2015) · Astrophysics and Space Science · doi:10.1007/s10509-015-2254-2 · Cited by 347 · see citing works
A systematic search for changing-look quasars in SDSS
MacLeod, C. L., Ross, N. P., Lawrence, A., Goad, M., Horne, K., Burgett, W., Chambers, K. C., Flewelling, H., Hodapp, K., Kaiser, N., et al. (2016) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stv2997 · Cited by 323 · see citing works
Anomaly detection in the Zwicky Transient Facility DR3
Malanchev, K. L., Pruzhinskaya, M. V., Korolev, V., Aleo, P., Kornilov, M., Ishida, É. E. O., Krushinsky, V., Mondon, F., Sreejith, S., Volnova, A. A., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab316 · Cited by 241 · see citing works
Improving classification accuracy using data augmentation on small data sets
Moreno-Barea, F. J., Jerez, J. M., & Franco, L. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113696 · Cited by 234 · see citing works
Spectral Properties of Quasars from Sloan Digital Sky Survey Data Release 14: The Catalog
Rakshit, S., Stalin, C. S., & Kotilainen, J. K. (2020) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ab99c5 · Cited by 218 · see citing works
The Half Million Quasars (HMQ) Catalogue
Flesch, E. W. (2015) · Publications of the Astronomical Society of Australia · doi:10.1017/pasa.2015.10 · Cited by 198 · see citing works
The Automatic Learning for the Rapid Classification of Events (ALeRCE) Alert Broker
Förster, F., Cabrera-Vives, G., Castillo-Navarrete, E., Estévez, P. A., Sanchéz-Sáez, P., Arredondo, J., Bauer, F. E., Carrasco-Davis, R., Catelan, M., Elorrieta, F., et al. (2021) · The Astronomical Journal · doi:10.3847/1538-3881/abe9bc · Cited by 197 · see citing works
Self-citation1ES 1927+654: An AGN Caught Changing Look on a Timescale of Months
Trakhtenbrot, B., Arcavi, I., MacLeod, C. L., Ricci, C., Kara, E., Graham, M. L., Stern, D., Harrison, F. A., Burke, J., Hiramatsu, D., et al. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab39e4 · Cited by 192 · see citing works
A Mid-IR Selected Changing-look Quasar and Physical Scenarios for Abrupt AGN Fading
Stern, D. K., McKernan, B., Graham, M. J., Ford, K. E. S., Ross, N. P., Meisner, A., Assef, R. J., Baloković, M., Brightman, M., Dey, A. T., et al. (2018) · The Astrophysical Journal · doi:10.3847/1538-4357/aac726 · Cited by 176 · see citing works
MacLeod, C. L., Green, P. J., Anderson, S. F., Bruce, A., Eracleous, M., Graham, M., Homan, D., Lawrence, A., LeBleu, A., Ross, N. P., et al. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab05e2 · Cited by 169 · see citing works
The Destruction and Recreation of the X-Ray Corona in a Changing-look Active Galactic Nucleus
Ricci, C., Kara, E., Loewenstein, M., Trakhtenbrot, B., Arcavi, I., Remillard, R. A., Fabian, A. C., Gendreau, K. C., Arzoumanian, Z., Li, R., et al. (2020) · The Astrophysical Journal Letters · doi:10.3847/2041-8213/ab91a1 · Cited by 168 · see citing works
A Catalog of Narrow Line Seyfert 1 Galaxies from the Sloan Digital Sky Survey Data Release 12
Rakshit, S., Stalin, C. S., Chand, H., & Zhang, X.-G. (2017) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/aa6971 · Cited by 160 · see citing works
Alert Classification for the ALeRCE Broker System: The Light Curve Classifier
Sanchéz-Sáez, P., Reyes, I., Valenzuela, C., Förster, F., Eyheramendy, S., Elorrieta, F., Bauer, F. E., Cabrera-Vives, G., Estévez, P. A., Catelan, M., et al. (2021) · The Astronomical Journal · doi:10.3847/1538-3881/abd5c1 · Cited by 137 · see citing works
Self-citationA NEW CATALOG OF TYPE 1 AGNs AND ITS IMPLICATIONS ON THE AGN UNIFIED MODEL
Oh, K., Yi, S. K., Schawinski, K., Koss, M., Trakhtenbrot, B., & Soto, K. T. (2015) · The Astrophysical Journal Supplement Series · doi:10.1088/0067-0049/219/1/1 · Cited by 133 · see citing works
Anomaly Detection Principles and Algorithms
Mehrotra, K. G., Mohan, C. K., & Huang, H. (2017) · Terrorism, security and computation · doi:10.1007/978-3-319-67526-8 · Cited by 133 · see citing works
A recurrent neural network for classification of unevenly sampled variable stars
Naul, B., Bloom, J. S., Pérez, F., & van der Walt, S. (2017) · Nature Astronomy · doi:10.1038/s41550-017-0321-z · Cited by 131 · see citing works
Ruan, J. J., Anderson, S. F., Eracleous, M., Green, P. J., Haggard, D., MacLeod, C. L., Runnoe, J. C., & Sobolewska, M. A. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab3c1a · Cited by 128 · see citing works
A new physical interpretation of optical and infrared variability in quasars
Ross, N. P., Ford, K. E. S., Graham, M. J., McKernan, B., Stern, D. K., Meisner, A., Assef, R. J., Dey, A. T., Drake, A. J., Jun, H. D., et al. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/sty2002 · Cited by 125 · see citing works
Understanding extreme quasar optical variability with CRTS – I. Major AGN flares
Graham, M. J., Djorgovski, S. G., Drake, A. J., Stern, D. K., Mahabal, A. A., Glikman, E., Larson, S., & Christensen, E. (2017) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx1456 · Cited by 121 · see citing works
OPTICAL VARIABILITY OF AGNs IN THE PTF/iPTF SURVEY
Čaplar, N., Lilly, S. J., & Trakhtenbrot, B. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/834/2/111 · Cited by 119 · see citing works
Understanding extreme quasar optical variability with CRTS – II. Changing-state quasars
Graham, M. J., Ross, N. P., Stern, D. K., Drake, A. J., McKernan, B., Ford, K. E. S., Djorgovski, S. G., Mahabal, A. A., Glikman, E., Larson, S., et al. (2019) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stz3244 · Cited by 119 · see citing works
Extreme variability quasars from the sloan digital sky survey and the dark energy survey
Marcos Vinícius da Silva Alves de, L. I. M. A. (2018) · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · doi:10.3847/1538-4357/aaa9b6/meta · Cited by 119 · see citing works
The Destruction and Recreation of the X-Ray Corona in a Changing-look Active Galactic Nucleus
Ricci, C., Kara, E., Loewenstein, M., Trakhtenbrot, B., Arcavi, I., Remillard, R. A., Fabian, A. C., Gendreau, K. C., Arzoumanian, Z., Li, R., et al. (2020) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 117 · see citing works
A New Class of Changing-look LINERs
Frederick, S., Gezari, S., Graham, M. J., Cenko, S. B., van Velzen, S., Stern, D. K., Blagorodnova, N., Kulkarni, S. R., Yan, L., De, K., et al. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab3a38 · Cited by 115 · see citing works
Tachibana, Y., & Miller, A. A. (2018) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/aae3d9 · Cited by 113 · see citing works
Pan-STARRS1 variability of XMM-COSMOS AGN
Simm, T., Salvato, M., Saglia, R. P., Ponti, G., Lanzuisi, G., Trakhtenbrot, B., Nandra, K., & Bender, R. (2015) · Astronomy and Astrophysics · doi:10.1051/0004-6361/201527353 · Cited by 113 · see citing works
A new class of flares from accreting supermassive black holes
Trakhtenbrot, B., Arcavi, I., Ricci, C., Tacchella, S., Stern, D., Netzer, H., Jonker, P. G., Horesh, A., Mejía-Restrepo, J. E., Hosseinzadeh, G., et al. (2019) · Nature Astronomy · doi:10.1038/s41550-018-0661-3 · Cited by 109 · see citing works
A recurrent neural network for classification of unevenly sampled variable stars
Naul, B., Bloom, J. S., Pérez, F., & van der Walt, S. (2018) · eScholarship (California Digital Library) · Cited by 101 · see citing works
Improving Damped Random Walk Parameters for SDSS Stripe 82 Quasars with Pan-STARRS1
Suberlak, K. L., Ivezić, Ž., & MacLeod, C. L. (2021) · The Astrophysical Journal · doi:10.3847/1538-4357/abc698 · Cited by 96 · see citing works
Villar, V. A., Hosseinzadeh, G., Berger, E., Ntampaka, M., Jones, D. O., Challis, P., Chornock, R., Drout, M. R., Foley, R. J., Kirshner, R. P., et al. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/abc6fd · Cited by 92 · see citing works
Dimensionality Reduction of SDSS Spectra with Variational Autoencoders
Portillo, S. K. N., Parejko, J. K., Vergara, J. R., & Connolly, A. J. (2020) · The Astronomical Journal · doi:10.3847/1538-3881/ab9644 · Cited by 91 · see citing works
Self-citationAstronomaly: Personalised active anomaly detection in astronomical data
Lochner, M., & Bassett, B. A. (2021) · Astronomy and Computing · doi:10.1016/j.ascom.2021.100481 · Cited by 84 · see citing works
Evidence for a change in the X-ray radiation mechanism in the hard state of Galactic black holes
Sobolewska, M. A., Papadakis, I. E., Done, C., & Malzac, J. (2011) · Monthly Notices of the Royal Astronomical Society · doi:10.1111/j.1365-2966.2011.19209.x · Cited by 75 · see citing works
A new class of flares from accreting supermassive black holes
Trakhtenbrot, B., Arcavi, I., Ricci, C., Tacchella, S., Stern, D. K., Netzer, H., Jonker, P. G., Horesh, A., Mejía-Restrepo, J. E., Hosseinzadeh, G., et al. (2019) · Repository for Publications and Research Data (ETH Zurich) · doi:10.17863/cam.101459 · Cited by 73 · see citing works
Sanchéz-Sáez, P., Lira, P., Mejía-Restrepo, J. E., Ho, L. C., Arévalo, P., Kim, M., Cartier, R., & Coppi, P. (2018) · The Astrophysical Journal · doi:10.3847/1538-4357/aad7f9 · Cited by 62 · see citing works
A Deep-learning Approach for Live Anomaly Detection of Extragalactic Transients
Villar, V. A., Cranmer, M., Berger, E., Contardo, G., Ho, S., Hosseinzadeh, G., & Lin, J. Y.-Y. (2021) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ac0893 · Cited by 49 · see citing works
Near-infrared Variability of Obscured and Unobscured X-Ray-selected AGNs in the COSMOS Field
Sánchez, P., Lira, P., Cartier, R., Pérez, V., Miranda, N., Yovaniniz, C., Arévalo, P., Milvang-Jensen, B., Fynbo, J., Dunlop, J., et al. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/aa9188 · Cited by 48 · see citing works
Active anomaly detection for time-domain discoveries
Ishida, É. E. O., Kornilov, M., Malanchev, K. L., Pruzhinskaya, M. V., Volnova, A. A., Korolev, V. S., Mondon, F., Sreejith, S., Malancheva, A., & Das, S. (2021) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202037709 · Cited by 47 · see citing works
Deep Modeling of Quasar Variability
Tachibana, Y., Graham, M. J., Kawai, N., Djorgovski, S. G., Drake, A. J., Mahabal, A. A., & Stern, D. K. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/abb9a9 · Cited by 37 · see citing works
High-redshift Extreme Variability Quasars from Sloan Digital Sky Survey Multiepoch Spectroscopy
Guo, H., Peng, J., Zhang, K., Burke, C. J., Liu, X., Sun, M., Wang, S., Kong, M., Sheng, Z., Wang, T., et al. (2020) · The Astrophysical Journal · doi:10.3847/1538-4357/abc2ce · Cited by 33 · see citing works
Oknyansky, V. L., Brotherton, M. S., Tsygankov, S. S., Додин, А. В., Bao, D.-W., Zhao, B.-X., Du, P., Burlak, M. A., Ikonnikova, N. P., Tatarnikov, A. M., et al. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab1138 · Cited by 33 · see citing works
The ZTF Source Classification Project. I. Methods and Infrastructure
van Roestel, J., Duev, D. A., Mahabal, A. A., Coughlin, M. W., Mróz, P., Burdge, K. B., Drake, A., Graham, M. J., Hillenbrand, L., Bellm, E. C., et al. (2021) · The Astronomical Journal · doi:10.3847/1538-3881/abe853 · Cited by 33 · see citing works
UNVEILING THE PHYSICS OF LOW-LUMINOSITY AGNs THROUGH X-RAY VARIABILITY: LINER VERSUS SEYFERT 2
Hernández-García, L., Masegosa, J., González-Martín, O., Márquez, I., & Perea, J. D. (2016) · The Astrophysical Journal · doi:10.3847/0004-637x/824/1/7 · Cited by 27 · see citing works
Self-citationEvidence of rapid optical variability in selected narrow-line Seyfert 1 galaxies
Miller, H. R., Ferrara, E. C., McFarland, J. P., Wilson, J. W., Daya, A. B., & Fried, R. E. (2000) · New Astronomy Reviews · doi:10.1016/s1387-6473(00)00094-4 · Cited by 22 · see citing works
Evidence of Rapid Optical Variability In Selected Narrow-Line Seyfert 1 Galaxies
Miller, H. R., Ferrara, E. C., McFarland, J. P., Wilson, J. W., Daya, A. B., & Fried, R. E. (2000) · CERN Document Server (European Organization for Nuclear Research) · Cited by 18 · see citing works
Characterization of optical light curves of extreme variability quasars over a ∼16-yr baseline
Luo, Y., Shen, Y. G., & Yang, Q. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa972 · Cited by 15 · see citing works
Flagged first, then by citations
2 of the 71 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys
Huertas-Company, M., & Lanusse, F. (2023) · Publications of the Astronomical Society of Australia · doi:10.1017/pasa.2022.55 · Cited by 102 · see citing works
Changing-look Active Galactic Nuclei. I. Tracking the Transition on the Main Sequence of Quasars
Panda, S., & Śniegowska, M. (2024) · The Astrophysical Journal Supplement Series · doi:10.3847/1538-4365/ad344f · Cited by 41 · see citing works
Generative adversarial nets for unsupervised outlier detection
Du, X., Chen, J., Yu, J., Shu, L., & Tan, Q. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121161 · Cited by 40 · see citing works
López-Navas, E., Martínez‐Aldama, M. L., Bernal, S., Sanchéz-Sáez, P., Arévalo, P., Graham, M. J., Hernández-García, L., Lira, P., & Lobos, P. A. R. (2022) · Monthly Notices of the Royal Astronomical Society Letters · doi:10.1093/mnrasl/slac033 · Cited by 38 · see citing works
Self-citationWoo, J.-H., Wang, S., Rakshit, S., Cho, H., Son, D., Bennert, V. N., Gallo, E., Hodges-Kluck, E. J., Treu, T., Barth, A. J., et al. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad132f · Cited by 36 · see citing works
Identifying Changing-look AGNs Using Variability Characteristics
Wang, S., Woo, J.-H., Gallo, E., Guo, H., Son, D., Kong, M., Mandal, A. K., Cho, H., Kim, C., & Shin, J. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad3049 · Cited by 36 · see citing works
“Large time-domain surveys offer an excellent opportunity to investigate the variability patterns of different AGN classes, and to identify bona fide CL AGNs (e.g., Gezari et al. 2017; Frederick et al. 2019; Graham et al. 2020; S´anchez-S´aez et al. 2021a, 2023; L´opez-Navas et al. 2022, 2023a,b).”
BackgroundInfluentialThe rise and fall of the iron-strong nuclear transient PS16dtm
Petrushevska, T., Leloudas, G., Ilić, D., Bronikowski, M., Charalampopoulos, P., Jaisawal, G. K., Paraskeva, E., Pursiainen, M., Rakić, N., Schulze, S., et al. (2022) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202244623 · Cited by 34 · see citing works
The SNAD Viewer: Everything You Want to Know about Your Favorite ZTF Object
Malanchev, K. L., Kornilov, M., Pruzhinskaya, M. V., Ishida, É. E. O., Aleo, P., Korolev, V., Lavrukhina, A. D., Russeil, E., Sreejith, S., Volnova, A. A., et al. (2023) · Publications of the Astronomical Society of the Pacific · doi:10.1088/1538-3873/acb292 · Cited by 30 · see citing works
“It has already motivated searches and studies of AGNs (e.g. Sánchez-Sáez et al. 2021), strongly lensed QSOs (e.g. Stern et al. 2021), microlensing (e.g. Rodriguez et al. 2022), variable stars (e.g. Chen et al. 2020; Kupfer et al. 2021), young stellar objects (e.g. Kuhn et al. 2021), eclipsing…”
BackgroundDeep Attention-based Supernovae Classification of Multiband Light Curves
Pimentel, O. M., Estévez, P. A., & Förster, F. (2022) · The Astronomical Journal · doi:10.3847/1538-3881/ac9ab4 · Cited by 25 · see citing works
“Infinite and NaN features, which are produced due to incorrect feature extraction and programming code exceptions, are replaced with an especial value of −999 (Sánchez-Sáez et al. 2021).”
BackgroundMethodResultInfluentialSelf-citationSanchéz-Sáez, P., Arredondo, J., Bayo, A., Arévalo, P., Bauer, F. E., Cabrera-Vives, G., Catelan, M., Coppi, P., Estévez, P. A., Förster, F., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202346077 · Cited by 23 · see citing works
Self-citationATAT: Astronomical Transformer for time series and Tabular data
Cabrera-Vives, G., Moreno-Cartagena, D., Astorga, N., Reyes, I., Förster, F., Huijse, P., Arredondo, J., Muñoz-Arancibia, A., Bayo, A., Catelan, M., et al. (2024) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202449475 · Cited by 19 · see citing works
Self-citationDormancy and Reawakening over Years: Eight New Recurrent Changing-look AGNs
Wang, S., Woo, J.-H., Gallo, E., Son, D., Yang, Q., Jin, J., Guo, H., & Kong, M. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adadf3 · Cited by 17 · see citing works
“…searches that significantly increased the sample size (e.g., MacLeod et al. 2016, 2019; Runnoe et al. 2016; Yang et al. 2018, 2024; Graham et al. 2020; S´anchez-S´aez et al. 2021; Guo et al. 2020a; Green et al. 2022; Hon et al. 2022; Guo et al. 2024a,b; Zeltyn et al. 2024; Dong et al. 2024).”
Long-term optical spectral monitoring of a changing-look active galactic nucleus NGC 3516
Popović, L. Č., Ilić, D., Burenkov, A. N., Patiño-Álvarez, V., Marčeta-Mandić, S., Kovačević, J., Shablovinskaya, E. S., Kovačević, A. B., Marziani, P., Chavushyan, V., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202345949 · Cited by 16 · see citing works
Fagin, J., Park, J. W., Best, H., Chan, J. H. H., Ford, K. E. S., Graham, M. J., Villar, V. A., Ho, S., & O’Dowd, M. J. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad2988 · Cited by 15 · see citing works
Searching for Anomalies in the ZTF Catalog of Periodic Variable Stars
Chan, H.-S., Villar, V. A., Cheung, S.-H., Ho, S., O’Grady, A. J. G., Drout, M. R., & Renzo, M. (2022) · The Astrophysical Journal · doi:10.3847/1538-4357/ac69d4 · Cited by 12 · see citing works
“…models (Groenewegen & Jurkovic 2017), Galactic metallicity (Jurkovic 2018), the physics of accretion and mass transfer (Gautschy & Saio 2017; Kimura et al. 2021), the physics of Active Galactic Nuclei (Sánchez-Sáez et al. 2021), and even the formation of low mass white dwarf (Masuda et al. 2019).”
BackgroundSupernova search with active learning in ZTF DR3
Pruzhinskaya, M. V., Ishida, É. E. O., Novinskaya, A. K., Russeil, E., Volnova, A. A., Malanchev, K. L., Kornilov, M., Aleo, P., Korolev, V. S., Krushinsky, V., et al. (2023) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202245172 · Cited by 12 · see citing works
Alert Classification for the ALeRCE Broker System: The Anomaly Detector
Pérez-Carrasco, M., Cabrera-Vives, G., Hernández-García, L., Förster, F., Sanchéz-Sáez, P., Muñoz-Arancibia, A., Arredondo, J., Astorga, N., Bauer, F. E., Bayo, A., et al. (2023) · The Astronomical Journal · doi:10.3847/1538-3881/ace0c1 · Cited by 10 · see citing works
Self-citationConditional Neural Process for nonparametric modeling of active galactic nuclei light curves
Čvorović‐Hajdinjak, I., Kovačević, A. B., Ilić, D., Popović, L. Č., Dai, X., Jankov, I., Radović, V., Sanchéz-Sáez, P., & Nikutta, R. (2021) · Astronomische Nachrichten · doi:10.1002/asna.20210103 · Cited by 9 · see citing works
Panda, S. (2024) · Frontiers in Astronomy and Space Sciences · doi:10.3389/fspas.2024.1479874 · Cited by 8 · see citing works
Kuo, C.-T., Xu, D., & Friesen, R. (2025) · Universe · doi:10.3390/universe11120412 · Cited by 6 · see citing works
Detecting anomalies in space-object light curves using temporal-attention LSTM
Li, W., Zhang, Y., Wang, J., Guo, C., Zhi, H., 曾, X. 显. Z., Qiu, P., Wang, L., & Yin, J. (2025) · Space habitation. · doi:10.1016/j.spaceh.2025.100007 · Cited by 5 · see citing works
Fagin, J., Chan, J. H. H., Best, H., O’Dowd, M. J., Ford, K. E. S., Graham, M. J., Park, J. W., & Villar, V. A. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/addabc · Cited by 5 · see citing works
Learning novel representations of variable sources from multi-modal Gaia data via autoencoders
Huijse, P., Ridder, J. D., Eyer, L., Rimoldini, L., Holl, B., Chornay, N., Roquette, J., Nienartowicz, K., de Fombelle, G. J., Fritzewski, D. J., et al. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202554025 · Cited by 5 · see citing works
Deep Learning of Quasar Lightcurves in the LSST Era
Kovačević, A. B., Ilić, D., Popović, L. Č., Mitrović, N. A., Nikolić, M., Pavlović, M., Čvorović‐Hajdinjak, I., Knežević, M., & Savić, Đ. (2023) · Universe · doi:10.3390/universe9060287 · Cited by 5 · see citing works
Li, J. I.-H., Johnson, S. D., Avestruz, C., Jarugula, S., Shen, Y. G., Kesler, E., Liu, Z., & Mishra, N. (2024) · The Astrophysical Journal · doi:10.3847/1538-4357/ad900d · Cited by 5 · see citing works
“…recent works have turned to machine learning and deep learning techniques to provide more efficient algorithms for AGN science, e.g., modeling AGN variability (Tachibana et al. 2020), inferring BH parameters (Fa-gin et al. 2024), and detecting anomalous light curves (S´anchez-S´aez et al. 2021).”
Long-Term Optical Monitoring of Broad-Line AGNs (LoTerm AGN): Case Study of NGC 3516
Ilić, D., Popović, L. Č., Burenkov, A. N., Shablovinskaya, E. S., Malygin, E. A., Uklein, R. I., Moiseev, A. V., Oparin, D. V., Patiño-Álvarez, V., Chavushyan, V., et al. (2023) · Physics · doi:10.3390/physics6010003 · Cited by 4 · see citing works
The ambiguous AT2022rze: changing-look AGN mimicking a supernova in a merging galaxy system
Pessi, P. J., Lunnan, R., Sollerman, J., Yan, L., Reste, A. L., Yao, Y., Nordblom, S., Sharma, Y. K., Гильфанов, М. Р., Sunyaev, R., et al. (2025) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staf1433 · Cited by 4 · see citing works
Möller, A., Ishida, É. E. O., Peloton, J., Velázquez, O. V., Soon, J., Martin, B., Cluver, M. E., Leoni, M., & Taylor, E. N. (2025) · Publications of the Astronomical Society of Australia · doi:10.1017/pasa.2025.20 · Cited by 4 · see citing works
“…classification (Möller et al. 2016; Möller & de Boissière 2020; Qu et al. 2021; Boone 2021; Gagliano et al. 2023), kilonovae (Biswas et al. 2023), variables (Sánchez-Sáez et al. 2021) or identifying simultaneously many transients and variable classes (Fraga et al. 2024; Cabrera-Vives et al. 2024).”
Interpreting the detection of anomalies in SDSS spectra
Ortiz, E., & Boquien, M. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202556339 · Cited by 3 · see citing works
Real-bogus scores for active anomaly detection
Semenikhin, T. A., Kornilov, M., Pruzhinskaya, M. V., Lavrukhina, A. D., Russeil, E., Gangler, É., Ishida, É. E. O., Korolev, V. S., Malanchev, K. L., Volnova, A. A., et al. (2024) · Astronomy and Computing · doi:10.1016/j.ascom.2024.100919 · Cited by 3 · see citing works
“Similarly, S´anchez-S´aez et al. (2021) encountered an overwhelming amount of bogus among the 8809 anomalies selected while searching for Changing-state AGNs in the ZTF DR5.”
BackgroundDataset of artefacts for machine learning applications in astronomy
Sreejith, S., Pruzhinskaya, M. V., Volnova, A. A., Krushinsky, V., Malanchev, K. L., Ishida, É. E. O., Lavrukhina, A. D., Semenikhin, T. A., Gangler, É., Kornilov, M., et al. (2025) · New Astronomy · doi:10.1016/j.newast.2025.102466 · Cited by 2 · see citing works
Modeling quasar variability through self-organizing map-based neural process
Čvorović‐Hajdinjak, I. (2024) · Serbian Astronomical Journal · doi:10.2298/saj2408017c · Cited by 2 · see citing works
Outlier Detection based on Transformations for Astronomical Time Series
Romero, M., & Estévez, P. A. (2022) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn55064.2022.9892590 · Cited by 2 · see citing works
“In [34] [15] recurrent variational AE and variational recurrent AE are used for anomaly detection for transient and stochastic light curves, respectively.”
MethodSelf-citationConvolutional deep denoising autoencoders for radio astronomical images
Gheller, C., & Vazza, F. (2021) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stab3044 · Cited by 1 · see citing works
Quantum Machine Learning for Identifying Transient Events in X-Ray Light Curves
Kawamuro, T., Yamada, S., Nagataki, S., Matsuura, S., Sakai, Y., & Yamada, S. (2025) · The Astrophysical Journal · doi:10.3847/1538-4357/adda43 · Cited by 1 · see citing works
Stream automatic detection with convolutional neural networks
Vera-Casanova, A., Gonzalez, N. M., Gómez, F. A., Arancibia, M. J., Fontirroig, V., Pallero, D., Pakmor, R., van de Voort, F., Grand, R. J. J., Bieri, R., et al. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202554688 · Cited by 1 · see citing works
Online Estimation Methods for Irregular Autoregressive Models
Elorrieta, F., Osses, L., Cáceres, M., Eyheramendy, S., & Palma, W. (2023) · Contributions to statistics · doi:10.1007/978-3-031-40209-8_1 · Cited by 1 · see citing works
Self-citationKliapets, M., Huijse, P., Tkachenko, A., Kemp, A. J., Fritzewski, D. J., Hey, D., & Aerts, C. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202556079 · Cited by 1 · see citing works
ATAT: Astronomical Transformer for time series And Tabular dataPreprint
Astorga, N., Reyes, I., Cabrera-Vives, G., Förster, F., Huijse, P., Arredondo, J., Moreno-Cartagena, D., Muñoz-Arancibia, A., Bayo, A., Catelan, M., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2395110/v1 · Cited by 1 · see citing works
Self-citationTemporal Stamp Classifier: Classifying Short Sequences of Astronomical Alerts
Daniel Neira, O., Estévez, P. A., & Förster, F. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650191
“The proposed temporal stamp classifier might be used to find outliers like [13, 14], e.g., using the model uncertainty operator proposed in [15] to find examples that are in the tail of the distributions.”
MethodSelf-citationDataset of Artefacts for Machine Learning Applications in AstronomyPreprint
Sreejith, S., Pruzhinskaya, M. V., Volnova, A. A., Krushinsky, V., Malanchev, K. L., Ishida, É. E. O., Lavrukhina, A. D., Semenikhin, T. A., Gangler, É., Kornilov, M., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5210529
Semisupervised learning for lensed quasar detection
Sweeney, D., Krone-Martins, A., Stern, D. K., Tuthill, P., Scalzo, R. A., Djorgovski, G., Ducourant, C., Mahabal, A., Teixeira, R., & Graham, M. J. (2026) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stag445
Toward model-free stellar chemical abundances
Signor, T., Jofré, P., Lira, H., Vitali, S., Martí, L., & Sánchez-Pi, N. (2025) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202555376
Self-citationReducing the Dimensions of Active Galactic Nuclei Light-curve Manifolds
Hemmati, S., Krick, J., Stern, D. K., Desai, V., Faisst, A. L., Martin-Garcia, L., Gorjian, V., Haghjoo, A., Nikakhtar, F., Raen, T., et al. (2026) · The Astrophysical Journal · doi:10.3847/1538-4357/ae38b8
“Deep learning approaches have also been applied to capture complex light-curve structure and distinguish CLAGNs from other variable AGN or transient populations ( e.g., P. Sánchez-Sáez et al. 2021 ) .”
A changing-look Seyfert discovered by eROSITA reveals a two-component broad-line region
Markowitz, A. G., Krumpe, M., Homan, D., Czerny, B., Gromazdki, M., Winkler, H., Wilms, J., Hämmerich, S., Lamer, G., Saha, T., et al. (2026) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202558834
A meta-learning framework for multitask reverberation mapping in active galactic nuclei
Raju, A. N., Kovačević, A. B., Ilić, D., Tombesi, F., Popović, L. Č., Slezak, E., Sánchez-Sáez, P., Pavlović, M., Čvorović‐Hajdinjak, I., Simić, S., et al. (2026) · Astronomy and Astrophysics · doi:10.1051/0004-6361/202554493
Self-citation
- Authors
- Sánchez-Sáez, P., Lira, H., Martí, L., Sánchez-Pi, N., Arredondo, J., Bauer, F. E., Bayo, A., Cabrera-Vives, G., Donoso-Oliva, C., Estévez, P. A., Eyheramendy, S., Förster, F., Hernández-García, L., Arancibia, A. M. M., Pérez-Carrasco, M., Sepúlveda, M., & Vergara, J. R.
- Affiliations
- Inria Chile (CL)
- Millennium Institute of Astrophysics (CL)
- Space Science Institute (US)
- Millenium Nucleus for Planet Formation (CL)
- University of Concepción (CL)
- Adolfo Ibáñez University (CL)
- University of Chile (CL)
- ORCID
- Venue
- The Astronomical Journal
- Publisher
- American Astronomical Society
- Published
- 2021-10-20
- Volume
- 162
- Issue
- 5
- Pages
- 206
- ISSN
- 0004-6256 (ISSN-L)
- 1538-3881
- OpenAlex
- W4212881834
- Cited by
- 47
- References
- 71
- Citations by year
- 2026: 4 · 2025: 14 · 2024: 10 · 2023: 11 · 2022: 6 · 2021: 2
- FWCI
- 3.90
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The Astronomical Journal (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- HAL (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1440341, AFB-170002, ICN12_009)
- California Institute of Technology
- University of Washington
- Corporación de Fomento de la Producción (10CEII-9157)
- Universiti Teknikal Malaysia Melaka
- Stockholms Universitet
- Agencia Nacional de Investigación y Desarrollo (FONDEQUIP-EQM180180, 1200495, 1200710, 10CEII-9157, 1171678, AFB170002, ICN12$\_$009, NCN19_171, EQM180180, 1190818, 11191130, 3200250)
- Deutsches Elektronen-Synchrotron
- Comisión Nacional de Investigación Científica y Tecnológica (ICN12_009, EQM180180, FONDEQUIP-EQM180180, 170002, 11191130, 10CEII-9157, AFB-170002, NCN19_171)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Astrophysical Phenomena and Observations
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Gamma-ray bursts and supernovae
- Keywords
- changing-look AGN, AGN variability, anomaly detection, AGN light curves, Isolation Forest, Zwicky Transient Facility, AGN classification, latent space features, reconstruction error
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
