Multimessenger pulsar timing array constraints on supermassive black hole binaries traced by periodic light curves

TL;DR

This study uses a catalog of periodic light curves to predict gravitational wave signals from supermassive black hole binaries, demonstrating IPTA's improving sensitivity and potential for multimessenger detections by 2025 and beyond.

Contribution

It provides the first comprehensive prediction of gravitational wave detectability for a large sample of SMBHB candidates using pulsar timing arrays.

Findings

IPTA offers nearly uniform sky coverage and will significantly improve detection sensitivity.

By 2025, IPTA will detect three SMBHB candidates and 13 by the end of the decade.

Constraints on SMBH masses in specific galaxies, e.g., Mrk 504, are established.

Abstract

Supermassive black hole binary systems (SMBHBs) emitting gravitational waves may be traced by periodic light curves. We assembled a catalog of 149 such periodic light curves, and using their masses, distances, and periods, predicted the gravitational-wave strain and detectability of each binary candidate using all-sky detection maps. We found that the International Pulsar Timing Array (IPTA) provides almost uniform sky coverage -- a unique ability of the IPTA -- and by 2025 will improve NANOGrav's current minimum detectable strain by a factor of 6, and its volume by a factor of 216. Moreover, IPTA will reach detection sensitivities for three candidates by 2025, and 13 by the end of the decade, enabling us to constrain the underlying empirical relations used to estimate SMBH masses. We find that we can in fact already constrain the mass of a binary in Mrk 504 to $M<3.3\times 10^9~M_\odot$. We also identify 24 high-mass high-redshift galaxies which, according to our models, should not be able to host SMBHBs. Importantly the GW detection of even one of these candidates would be an essentially eternal multimessenger system, and identifying common false positive signals from non-detections will be useful to filter the data from future large-scale surveys such as LSST.