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Massive Black Hole Binaries as LISA Precursors in the Roman High Latitude Time Domain Survey

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arxiv 2306.14990 v1 pith:QQTDQP5E submitted 2023-06-26 astro-ph.HE astro-ph.COastro-ph.GAgr-qchep-th

classification astro-ph.HEastro-ph.COastro-ph.GAgr-qchep-th
keywords lisabinariesblackromanholemassiveduringgravitational
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abstract

With its capacity to observe $\sim 10^{5-6}$ faint active galactic nuclei (AGN) out to redshift $z\approx 6$, Roman is poised to reveal a population of $10^{4-6}\, {\rm M_\odot}$ black holes during an epoch of vigorous galaxy assembly. By measuring the light curves of a subset of these AGN and looking for periodicity, Roman can identify several hundred massive black hole binaries (MBHBs) with 5-12 day orbital periods, which emit copious gravitational radiation and will inevitably merge on timescales of $10^{3-5}$ years. During the last few months of their merger, such binaries are observable with the Laser Interferometer Space Antenna (LISA), a joint ESA/NASA gravitational wave mission set to launch in the mid-2030s. Roman can thus find LISA precursors, provide uniquely robust constraints on the LISA source population, help identify the host galaxies of LISA mergers, and unlock the potential of multi-messenger astrophysics with massive black hole binaries.

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 5 citations worldwide. Full citation record

  1. Identification of periodicities with arbitrary shapes in AGN light curves

    astro-ph.GA 2025-12 conditional novelty 6.0 of 10

    A GP with a flexible periodic kernel recovers non-sinusoidal periodicities in simulated AGN light curves better than cosine-kernel GPs, and better than Lomb-Scargle periodograms for ideal and LSST-like sampling, but n...

  2. Quantum Machine Learning for Identifying Transient Events in X-ray Light Curves

    astro-ph.HE 2025-07 conditional novelty 5.0 of 10

    A quantum LSTM trained on simulated AGN light curves detects 113 transient-event candidates in the XMM-Newton 4XMM-DR14 catalog, about 28 more than a classical LSTM.

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