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EMRI_MC: A GPU-based code for Bayesian inference of EMRI waveforms
by Ippocratis D. Saltas, Roberto Oliveri
Submission summary
| Authors (as registered SciPost users): | Ippocratis Saltas |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2311.17174v1 (pdf) |
| Code repository: | https://zenodo.org/records/10204186 |
| Data repository: | https://zenodo.org/records/10204186 |
| Date submitted: | 2024-04-04 18:16 |
| Submitted by: | Saltas, Ippocratis |
| Submitted to: | SciPost Physics Codebases |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approach: | Computational |
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. Extending the code to more accurate schemes for the generation of the waveform is straightforward. Despite the known limitations of the kludge formalism, we believe that the code can provide a helpful resource for the community working on forecasts for interferometry missions in the milli-Hz scale, predominantly, the satellite-mission LISA.