Sub-parsec Detectability Lifetimes for a Sample of Dual AGN with Known Mass Ratios

Abstract We analyze 84 double quasars from the Sloan Digital Sky Survey, selected by G. Foreman et al., to investigate the sub-parsec-scale detectability lifetimes of supermassive black hole (SMBH) binaries—key progenitors of low-frequency gravitational waves. We compute the time during which binaries with the same mass ratios as the G. Foreman et al. sample are likely to have observable signatures in variable broad line profiles. Our results are helpful for predicting the population of resolvable SMBH binaries detectable with future space-based interferometers such as the Laser Interferometer Space Antenna.

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Publication Details

Journal
Research Notes of the AAS
Published
2025-04-14
DOI
https://doi.org/10.3847/2515-5172/adcb3c
Citations
1
Primary Topic
Advanced Image Processing Techniques
Type
article
Field-Weighted Citation Impact
1.28
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article

Sub-parsec Detectability Lifetimes for a Sample of Dual AGN with Known Mass Ratios

Krista Lynne Smith, Colton M. Burross
1 citations
Research Notes of the AAS
Advanced Image Processing Techniques
1.28
article

Sub-parsec Detectability Lifetimes for a Sample of Dual AGN with Known Mass Ratios

Krista Lynne Smith, Colton M. Burross
article en
1 citations

Abstract

Abstract We analyze 84 double quasars from the Sloan Digital Sky Survey, selected by G. Foreman et al., to investigate the sub-parsec-scale detectability lifetimes of supermassive black hole (SMBH) binaries—key progenitors of low-frequency gravitational waves. We compute the time during which binaries with the same mass ratios as the G. Foreman et al. sample are likely to have observable signatures in variable broad line profiles. Our results are helpful for predicting the population of resolvable SMBH binaries detectable with future space-based interferometers such as the Laser Interferometer Space Antenna.

Research Notes of the AASVol. 9(4)
Mitchell Institute (US), Texas A&M University (US)
Openalex Percentile: Top 21%
Advanced Image Processing Techniques
1.28
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