Science with the TianQin observatory: Preliminary result on extreme-mass-ratio inspirals

TL;DR

This paper evaluates TianQin's capability to detect extreme-mass-ratio inspirals (EMRIs), showing it can observe sources up to redshift 2, with high parameter estimation accuracy, and discusses benefits of a detector network.

Contribution

It provides the first detailed estimates of EMRI detection rates and parameter estimation capabilities for TianQin, including the benefits of a multi-detector network.

Findings

TianQin can observe EMRIs up to redshift ~2.

Detection rates could reach tens or hundreds per year.

Parameter estimation errors are within fractional errors of 10^{-6}.

Abstract

Systems consisting of a massive black hole and a stellar-origin compact object (CO), known as extreme-mass-ratio inspirals (EMRIs), are of great significance for space-based gravitational-wave detectors, as they will allow for testing gravitational theories in the strong field regime, and for checking the validity of the black hole no-hair theorem. In this work, we present a calculation of the EMRI rate and parameter estimation capabilities of the TianQin observatory, for various astrophysical models for these sources. We find that TianQin can observe EMRIs involving COs with mass of 10$M_\odot$ up to redshift $\sim2$. We also find that detections could reach tens or hundreds per year in the most optimistic astrophysical scenarios. Intrinsic parameters are expected to be recovered to within fractional errors of $\sim 10^{-6}$, while typical errors on the luminosity distance and sky localization are 10% and 10 deg$^2$, respectively. TianQin observation of EMRIs can also constrain possible deviations from the Kerr quadrupole moment to within fractional errors $\lesssim10^{-4}$. We also find that a network of multiple detectors would allow for improvements in both detection rates (by a factor $\sim 1.5$ -$3$) and in parameter estimation precision (20-fold improvement for the sky localization and fivefold improvement for the other parameters).