EMRI data analysis with a phenomenological waveform

TL;DR

This paper introduces a phenomenological waveform approach for detecting and estimating parameters of EMRIs in gravitational wave data, addressing computational challenges with accurate models.

Contribution

It proposes a large-parameter-space phenomenological template family for EMRIs, enabling model-independent detection and model-dependent parameter estimation.

Findings

Successful detection of simulated EMRI signals using phenomenological templates.

Separation of detection and parameter estimation processes.

Mapping phenomenological parameters to physical EMRI parameters.

Abstract

Extreme mass ratio inspirals (EMRIs) (capture and inspiral of a compact stellar mass object into a Massive Black Hole (MBH)) are among the most interesting objects for the gravitational wave astronomy. It is a very challenging task to detect those sources with the accurate estimation parameters of binaries primarily due to a large number of the secondary maxima on the likelihood surface. Search algorithms based on the matched filtering require computation of the gravitational waveform hundreds of thousands of times, which is currently not feasible with the most accurate (faithful) models of EMRIs. Here we propose to use a phenomenological template family which covers a large range of EMRIs parameter space. We use these phenomenological templates to detect the signal in the simulated data and then, assuming a particular EMRI model, estimate the physical parameters of the binary. We have separated the detection problem, which is done in a model-independent way, from the parameter estimation. For the latter one, we need to adopt the model for inspiral in order to map phenomenological parameters onto the physical parameter characterizing EMRIs.