EMRI_MC: A GPU-based Python code for Bayesian inference of EMRI waveforms

TL;DR

EMRI_MC is a GPU-accelerated Python tool that efficiently performs Bayesian inference on gravitational wave signals from EMRIs, aiding future space-based interferometry missions like LISA.

Contribution

It introduces a simple, efficient Python code utilizing GPUs for Bayesian analysis of EMRI waveforms, incorporating cosmological effects and enabling rapid parameter estimation.

Findings

Fast waveform generation using GPU acceleration

Effective Bayesian inference with MCMC sampling

Inclusion of cosmological modifications in waveform analysis

Abstract

We describe a simple and efficient Python code to perform Bayesian forecasting for gravitational waves (GW) produced by Extreme-Mass-Ratio-Inspiral systems (EMRIs). The code runs on GPUs for an efficient parallelised computation of thousands of waveforms and sampling of the posterior through a Markov-Chain-Monte-Carlo (MCMC) algorithm. EMRI_MC generates EMRI waveforms based on the so--called kludge scheme, and propagates it to the observer accounting for cosmological effects in the observed waveform due to modified gravity/dark energy. The code provides a helpful resource for forecasts for interferometry missions in the milli-Hz scale, e.g the satellite-mission LISA.