Codebase release 1.0 for EMRI_MC
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.
Authors
- Roberto Oliveri (ORCID: https://orcid.org/0000-0002-7497-871X)
- Ippocratis D. Saltas (ORCID: https://orcid.org/0000-0002-0533-1285)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Czech Academy of Sciences (CZ)
- Université Paris Cité (FR)
- Université Paris Sciences et Lettres (FR)
- Observatoire de Paris (FR)
- Laboratoire Univers et Théories (FR)
- Sorbonne Paris Cité (FR)
Publication Details
- Journal
- SciPost Physics Codebases
- Published
- 2025-01-16
- DOI
- https://doi.org/10.21468/scipostphyscodeb.44-r1.0
- Citations
- 1
- Primary Topic
- Parallel Computing and Optimization Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.27
Funders
- Grantová Agentura České Republiky
- Conseil Régional, Île-de-France