Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties
Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical va...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2024-01-01
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| Series: | The Astrophysical Journal |
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| Online Access: | https://doi.org/10.3847/1538-4357/ad2988 |
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| author | Joshua Fagin Ji Won Park Henry Best James H. H. Chan K. E. Saavik Ford Matthew J. Graham V. Ashley Villar Shirley Ho Matthew O’Dowd |
| author_facet | Joshua Fagin Ji Won Park Henry Best James H. H. Chan K. E. Saavik Ford Matthew J. Graham V. Ashley Villar Shirley Ho Matthew O’Dowd |
| author_sort | Joshua Fagin |
| collection | DOAJ |
| description | Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling. |
| first_indexed | 2024-04-24T11:16:29Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 1538-4357 |
| language | English |
| last_indexed | 2024-04-24T11:16:29Z |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj.art-d08cce76401f44abb5c576d3871c81f32024-04-11T08:01:07ZengIOP PublishingThe Astrophysical Journal1538-43572024-01-01965210410.3847/1538-4357/ad2988Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole PropertiesJoshua Fagin0https://orcid.org/0000-0001-8723-6136Ji Won Park1https://orcid.org/0000-0002-0692-1092Henry Best2https://orcid.org/0009-0009-6932-6379James H. H. Chan3https://orcid.org/0000-0001-8797-725XK. E. Saavik Ford4https://orcid.org/0000-0002-5956-851XMatthew J. Graham5https://orcid.org/0000-0002-3168-0139V. Ashley Villar6https://orcid.org/0000-0002-5814-4061Shirley Ho7Matthew O’Dowd8https://orcid.org/0009-0000-4476-5003The Graduate Center of the City University of New York , 365 Fifth Avenue, New York, NY 10016, USA ; jfagin@gradcenter.cuny.edu; Department of Astrophysics, American Museum of Natural History , Central Park West and 79th Street, NY 10024-5192, USA; Department of Physics and Astronomy, Lehman College of the CUNY , Bronx, NY 10468, USASLAC National Accelerator Laboratory , Menlo Park, CA 94025, USAThe Graduate Center of the City University of New York , 365 Fifth Avenue, New York, NY 10016, USA ; jfagin@gradcenter.cuny.edu; Department of Astrophysics, American Museum of Natural History , Central Park West and 79th Street, NY 10024-5192, USA; Department of Physics and Astronomy, Lehman College of the CUNY , Bronx, NY 10468, USADepartment of Astrophysics, American Museum of Natural History , Central Park West and 79th Street, NY 10024-5192, USA; Department of Physics and Astronomy, Lehman College of the CUNY , Bronx, NY 10468, USAThe Graduate Center of the City University of New York , 365 Fifth Avenue, New York, NY 10016, USA ; jfagin@gradcenter.cuny.edu; Department of Astrophysics, American Museum of Natural History , Central Park West and 79th Street, NY 10024-5192, USA; Department of Science, CUNY Borough of Manhattan Community College , 199 Chambers Street, New York, NY 10007, USA; Flatiron Institute , 162 Fifth Avenue, New York, NY 10010, USAFlatiron Institute , 162 Fifth Avenue, New York, NY 10010, USA; California Institute of Technology , 1200 E. California Boulevard, Pasadena, CA 91125, USACenter for Astrophysics ∣ Harvard & Smithsonian , 60 Garden Street, Cambridge, MA 02138-1516, USAFlatiron Institute , 162 Fifth Avenue, New York, NY 10010, USA; Department of Astrophysical Sciences, Princeton University , 4 Ivy Lane, Princeton, NJ 08544, USA; Department of Physics and Center for Data Science, New York University , 60 5th Avenue, New York, NY 10011, USA; Department of Physics, Carnegie Mellon University , 10 40th Street, Pittsburgh, PA 15201, USAThe Graduate Center of the City University of New York , 365 Fifth Avenue, New York, NY 10016, USA ; jfagin@gradcenter.cuny.edu; Department of Astrophysics, American Museum of Natural History , Central Park West and 79th Street, NY 10024-5192, USA; Department of Physics and Astronomy, Lehman College of the CUNY , Bronx, NY 10468, USAQuasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar’s brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often represented as a damped random walk described by a stochastic differential equation (SDE). Upcoming wide-field telescopes such as the Rubin Observatory Legacy Survey of Space and Time (LSST) are expected to observe tens of millions of AGN in multiple filters over a ten year period, so there is a need for efficient and automated modeling techniques that can handle the large volume of data. Latent SDEs are machine learning models well suited for modeling quasar variability, as they can explicitly capture the underlying stochastic dynamics. In this work, we adapt latent SDEs to jointly reconstruct multivariate quasar light curves and infer their physical properties such as the black hole mass, inclination angle, and temperature slope. Our model is trained on realistic simulations of LSST ten year quasar light curves, and we demonstrate its ability to reconstruct quasar light curves even in the presence of long seasonal gaps and irregular sampling across different bands, outperforming a multioutput Gaussian process regression baseline. Our method has the potential to provide a deeper understanding of the physical properties of quasars and is applicable to a wide range of other multivariate time series with missing data and irregular sampling.https://doi.org/10.3847/1538-4357/ad2988QuasarsActive galactic nucleiNeural networksTime series analysisIrregular cadence |
| spellingShingle | Joshua Fagin Ji Won Park Henry Best James H. H. Chan K. E. Saavik Ford Matthew J. Graham V. Ashley Villar Shirley Ho Matthew O’Dowd Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties The Astrophysical Journal Quasars Active galactic nuclei Neural networks Time series analysis Irregular cadence |
| title | Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties |
| title_full | Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties |
| title_fullStr | Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties |
| title_full_unstemmed | Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties |
| title_short | Latent Stochastic Differential Equations for Modeling Quasar Variability and Inferring Black Hole Properties |
| title_sort | latent stochastic differential equations for modeling quasar variability and inferring black hole properties |
| topic | Quasars Active galactic nuclei Neural networks Time series analysis Irregular cadence |
| url | https://doi.org/10.3847/1538-4357/ad2988 |
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