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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.14621 |
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| _version_ | 1866913439143690240 |
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| author | Li, Jennifer I-Hsiu Johnson, Sean D. Avestruz, Camille Jarugula, Sreevani Shen, Yue Kesler, Elise Liu, Zhuoqi Will Mishra, Nishant |
| author_facet | Li, Jennifer I-Hsiu Johnson, Sean D. Avestruz, Camille Jarugula, Sreevani Shen, Yue Kesler, Elise Liu, Zhuoqi Will Mishra, Nishant |
| contents | 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 (LSST) 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 amount 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 (LSTM) 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 RM 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 | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14621 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast and Flexible Inference Framework for Continuum Reverberation Mapping using Simulation-based Inference with Deep Learning Li, Jennifer I-Hsiu Johnson, Sean D. Avestruz, Camille Jarugula, Sreevani Shen, Yue Kesler, Elise Liu, Zhuoqi Will Mishra, Nishant Astrophysics of Galaxies 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 (LSST) 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 amount 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 (LSTM) 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 RM 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. |
| title | Fast and Flexible Inference Framework for Continuum Reverberation Mapping using Simulation-based Inference with Deep Learning |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2407.14621 |