Bayesian inference for tidal heating with extreme mass ratio inspirals

TL;DR

This paper demonstrates that Bayesian analysis of EMRI gravitational wave signals can effectively constrain tidal heating effects, providing insights into black hole horizons with high precision.

Contribution

It introduces a comprehensive Bayesian framework for inferring tidal heating parameters in EMRIs, highlighting the potential for precise horizon tests.

Findings

Strong constraints on reflectivity parameter at 10^{-3}–10^{-4} level.

Neglecting tidal heating biases EMRI parameter estimation.

Strong-field regime yields smaller posterior uncertainties.

Abstract

Extreme mass ratio inspirals (EMRIs) provide unique probes of near-horizon dissipation through the tidal heating. We present a full Bayesian analysis of tidal heating in equatorial eccentric EMRIs by performing injection-recovery studies and inferring posterior constraints on the reflectivity parameter $|\mathcal{R}|^2$ while sampling in the full EMRI parameter space. We find that in the strong-field regime the posterior uncertainties are smaller, indicating a stronger constraining capability on the tidal heating. Using two-year signals with an optimal signal-to-noise ratio (SNR) of $\rho=50$, EMRIs can put bounds on $|\mathcal{R}|^2$ at the level of $10^{-3}$--$ 10^{-4}$ for a rapidly spinning central object. Moreover, we show that neglecting the tidal heating can induce clear systematic biases in the intrinsic parameters of the EMRI system. These results establish EMRIs as promising precision probes for detecting and constraining black hole event horizons.