Bayesian synthesis of astrometric wobble and total light curves in close binary supermassive black holes

TL;DR

This paper presents a Bayesian method to combine astrometric wobble data from next-generation interferometers with light curves from surveys like LSST to accurately determine the orbital parameters and individual fluxes of close binary supermassive black holes.

Contribution

It introduces a novel Bayesian synthesis approach that integrates multi-instrument data to extract detailed binary parameters and fluxes of CB-SMBH, enhancing observational strategies.

Findings

Reliable binary parameter and flux determination from combined data.

Method effective even with short period 'q-accrete' objects.

Potential to refine observational strategies for CB-SMBH detection.

Abstract

We test the potential of Bayesian synthesis of upcoming multi-instrument data to extract orbital parameters and individual light curves of close binary supermassive black holes (CB-SMBH) with subparsec separations. Next generation (ng) interferometers, will make possible the observation of astrometric wobbles in CB-SMBH. Combining them with periodic variable time-domain data from surveys like the Vera C. Rubin Legacy Survey of Space and Time (LSST), allows for a more information on CB-SMBH candidates compared to standalone observational methods. Our method reliably determines binary parameters and component fluxes from binary total flux across long-term, intermediate and short-term binary dynamics and observational configurations, assuming ten annual observations, even in short period "q-accrete" objects. Expected CB-SMBH astrometric wobbles constructed from binary dynamical parameters, might serve in refining observational strategies for CB-SMBH. Combination of inferred mass ratio, light curves of binary components, and observed photocenter wobbles can be a proxy for the activity states of CB-SMBH components.