TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhysCodeb.44-r1.0
TI  - Codebase release 1.0 for EMRI_MC
PY  - 2025/01/16
UR  - https://scipost.org/SciPostPhysCodeb.44-r1.0
JF  - SciPost Physics Codebases
JA  - SciPost Phys. Codebases
SP  - 44-r1.0
A1  - Saltas, Ippocratis
AU  - Oliveri, Roberto
AB  - 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.
ER  -

@Article{10.21468/SciPostPhysCodeb.44,
	title={{EMRI_MC: A GPU-based code for Bayesian inference of EMRI waveforms}},
	author={Ippocratis D. Saltas and Roberto Oliveri},
	journal={SciPost Phys. Codebases},
	pages={44},
	year={2025},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCodeb.44},
	url={https://scipost.org/10.21468/SciPostPhysCodeb.44},
}

@Article{10.21468/SciPostPhysCodeb.44-r1.0,
	title={{Codebase release 1.0 for EMRI_MC}},
	author={Ippocratis D. Saltas and Roberto Oliveri},
	journal={SciPost Phys. Codebases},
	pages={44-r1.0},
	year={2025},
	publisher={SciPost},
	doi={10.21468/SciPostPhysCodeb.44-r1.0},
	url={https://scipost.org/10.21468/SciPostPhysCodeb.44-r1.0},
}