Bayesian Methods for Multi-Messenger Analysis of Supermassive Black Hole Binaries: Pulsars and Quasars and Gravitational Waves, Oh My!

Supermassive black hole binaries (SMBHBs) can lurk, often unseen, in the centers of post-merger galaxies, and pulsar timing arrays (PTAs) are rapidly approaching the sensitivities required to detect nanohertz gravitational waves (GWs) from these giant pairs. Independently, numerous electromagnetic surveys are seeking evidence of these dynamic duos’ effects on their host galaxies by searching for periodicities in time-domain observations. Combining these two methods to use multi-messenger techniques allows us to learn more about these binaries than using one messenger alone. Throughout this thesis, I have created novel methods to reach new frontiers in low-frequency GW astrophysics and electromagnetic identification of SMBHB candidates. At this interface, my thesis work has driven efforts to bring low-frequency multi-messenger discoveries into reach within

the decade.

By using electromagnetic observations to identify an SMBHB candidate, we gain numerous pieces of information that also define the source’s GW emission. I developed the first multi-messenger techniques used by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav), and applied them to a well-known SMBHB candidate, 3C 66B. We placed the lowest chirp mass limit to date on an SMBHB within 3C 66B of M < 1.65 × 109M?. and showed that multi-messenger techniques can lead to dramatic improvements over “blind” searches.

Next, I analyzed the capabilities of Bayesian methods to search for electromagnetic signatures of these binaries in simulated time-domain data sets. I developed a Bayesian model selection technique to identify periodicities induced into a quasar light curve by the orbital motion of an SMBHB from within intrinsic red noise, and discovered that future surveys like LSST will identify more robust SMBHB candidates than current surveys.

Finally, I presented the results of searches for bright continuous GWs (CWs) from individual SMBHBs in NANOGrav’s 12.5-year data set. In this work, I searched for CWs alongside a common red noise process (which could be the first signs of an emerging stochastic GW background) for the first time in real PTA data, and developed necessary data-handling techniques which will be critical for the detection of a CW, which may come soon after the eventual detection of the GW background.