Fast and Flexible Inference Framework for Continuum Reverberation Mapping Using Simulation-based Inference with Deep Learning
Continuum reverberation mapping (CRM) of active galactic nuclei (AGN) monitors multiwavelength variability signatures to constrain accretion disk structure and supermassive black hole (SMBH) properties. The upcoming Vera Rubin Observatory’s Legacy Survey of Space and Time will survey tens of million...
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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/ad900d |
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| author | Jennifer I-Hsiu Li Sean D. Johnson Camille Avestruz Sreevani Jarugula Yue Shen Elise Kesler Zhuoqi (Will) Liu Nishant Mishra |
| author_facet | Jennifer I-Hsiu Li Sean D. Johnson Camille Avestruz Sreevani Jarugula Yue Shen Elise Kesler Zhuoqi (Will) Liu Nishant Mishra |
| author_sort | Jennifer I-Hsiu Li |
| collection | DOAJ |
| description | Continuum reverberation mapping (CRM) of active galactic nuclei (AGN) monitors multiwavelength variability signatures to constrain accretion disk structure and supermassive black hole (SMBH) properties. The upcoming Vera Rubin Observatory’s Legacy Survey of Space and Time will survey tens of millions of AGN over the next decade, with thousands of AGN monitored with almost daily cadence in the deep drilling fields. However, existing CRM methodologies often require long computation time and are not designed to handle such large amounts of data. In this paper, we present a fast and flexible inference framework for CRM using simulation-based inference (SBI) with deep learning to estimate SMBH properties from AGN light curves. We use a long short-term memory summary network to reduce the high dimensionality of the light curve data and then use a neural density estimator to estimate the posterior of SMBH parameters. Using simulated light curves, we find SBI can produce more accurate SMBH parameter estimation with 10 ^3 –10 ^5 times speed up in inference efficiency compared to traditional methods. The SBI framework is particularly suitable for wide-field CRM surveys as the light curves will have identical observing patterns, which can be incorporated into the SBI simulation. We explore the performance of our SBI model on light curves with irregular-sampled, realistic observing cadence and alternative variability characteristics to demonstrate the flexibility and limitation of the SBI framework. |
| format | Article |
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| institution | OA Journals |
| issn | 1538-4357 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astrophysical Journal |
| spelling | doaj-art-4c4e321623cb4d2191fc2ba96b352f0a2025-08-20T01:56:25ZengIOP PublishingThe Astrophysical Journal1538-43572024-01-01977222310.3847/1538-4357/ad900dFast and Flexible Inference Framework for Continuum Reverberation Mapping Using Simulation-based Inference with Deep LearningJennifer I-Hsiu Li0https://orcid.org/0000-0002-0311-2812Sean D. Johnson1https://orcid.org/0000-0001-9487-8583Camille Avestruz2https://orcid.org/0000-0001-8868-0810Sreevani Jarugula3https://orcid.org/0000-0002-5386-7076Yue Shen4https://orcid.org/0000-0003-1659-7035Elise Kesler5https://orcid.org/0000-0001-6846-9399Zhuoqi (Will) Liu6https://orcid.org/0000-0002-2662-9363Nishant Mishra7https://orcid.org/0000-0002-9141-9792Michigan Institute for Data Science, University of Michigan , Ann Arbor, MI 48109, USA; Department of Astronomy, University of Michigan , Ann Arbor, MI 48109, USA; Center for AstroPhysical Surveys, National Center for Supercomputing Applications, University of Illinois Urbana-Champaign , Urbana, IL, 61801, USADepartment of Astronomy, University of Michigan , Ann Arbor, MI 48109, USADepartment of Physics, University of Michigan , Ann Arbor, MI 48109, USA; Leinweber Center for Theoretical Physics, University of Michigan , Ann Arbor, MI 48109, USAFermi National Accelerator Laboratory , Batavia, IL 60510, USACenter for AstroPhysical Surveys, National Center for Supercomputing Applications, University of Illinois Urbana-Champaign , Urbana, IL, 61801, USA; Department of Astronomy, University of Illinois at Urbana-Champaign , Urbana, IL 61801, USADepartment of Astronomy, University of Michigan , Ann Arbor, MI 48109, USADepartment of Astronomy, University of Michigan , Ann Arbor, MI 48109, USADepartment of Astronomy, University of Michigan , Ann Arbor, MI 48109, USAContinuum reverberation mapping (CRM) of active galactic nuclei (AGN) monitors multiwavelength variability signatures to constrain accretion disk structure and supermassive black hole (SMBH) properties. The upcoming Vera Rubin Observatory’s Legacy Survey of Space and Time will survey tens of millions of AGN over the next decade, with thousands of AGN monitored with almost daily cadence in the deep drilling fields. However, existing CRM methodologies often require long computation time and are not designed to handle such large amounts of data. In this paper, we present a fast and flexible inference framework for CRM using simulation-based inference (SBI) with deep learning to estimate SMBH properties from AGN light curves. We use a long short-term memory summary network to reduce the high dimensionality of the light curve data and then use a neural density estimator to estimate the posterior of SMBH parameters. Using simulated light curves, we find SBI can produce more accurate SMBH parameter estimation with 10 ^3 –10 ^5 times speed up in inference efficiency compared to traditional methods. The SBI framework is particularly suitable for wide-field CRM surveys as the light curves will have identical observing patterns, which can be incorporated into the SBI simulation. We explore the performance of our SBI model on light curves with irregular-sampled, realistic observing cadence and alternative variability characteristics to demonstrate the flexibility and limitation of the SBI framework.https://doi.org/10.3847/1538-4357/ad900dReverberation mappingNeural networksSupermassive black holes |
| spellingShingle | Jennifer I-Hsiu Li Sean D. Johnson Camille Avestruz Sreevani Jarugula Yue Shen Elise Kesler Zhuoqi (Will) Liu Nishant Mishra Fast and Flexible Inference Framework for Continuum Reverberation Mapping Using Simulation-based Inference with Deep Learning The Astrophysical Journal Reverberation mapping Neural networks Supermassive black holes |
| title | Fast and Flexible Inference Framework for Continuum Reverberation Mapping Using Simulation-based Inference with Deep Learning |
| title_full | Fast and Flexible Inference Framework for Continuum Reverberation Mapping Using Simulation-based Inference with Deep Learning |
| title_fullStr | Fast and Flexible Inference Framework for Continuum Reverberation Mapping Using Simulation-based Inference with Deep Learning |
| title_full_unstemmed | Fast and Flexible Inference Framework for Continuum Reverberation Mapping Using Simulation-based Inference with Deep Learning |
| title_short | Fast and Flexible Inference Framework for Continuum Reverberation Mapping Using Simulation-based Inference with Deep Learning |
| title_sort | fast and flexible inference framework for continuum reverberation mapping using simulation based inference with deep learning |
| topic | Reverberation mapping Neural networks Supermassive black holes |
| url | https://doi.org/10.3847/1538-4357/ad900d |
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