EMRI_MC: A GPU-based Python code for Bayesian inference of EMRI waveforms
arXiv:2311.17174 · doi:10.21468/SciPostPhysCodeb.44
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.
v2: version to appear on SciPost Physics Codebases, code improved available at https://doi.org/10.5281/zenodo.10204186; v1: 14 pages, 2 figures
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