MatBYIB: A Matlab-based code for Bayesian inference of extreme mass-ratio inspiral binary with arbitrary eccentricity
Gen-Liang Li, Shu-Jie Zhao, Huai-Ke Guo, Jing-Yu Su, Zhen-Heng Lin
TL;DR
MatBYIB tackles the challenge of Bayesian parameter estimation for gravitational waves from EMRIs with arbitrary eccentricity by marrying Computationally efficient Analytical Kludge waveforms with both Fisher Information Matrix forecasts and full Metropolis–Hastings MCMC posterior sampling. The framework enables rapid, near-analytic error estimates and robust, full posterior exploration, using Gelman–Rubin convergence across parallel chains to guarantee sampling adequacy. Its modular MATLAB implementation (waveform generation, detector response, FIM, and MCMC) is open-source and demonstrated on representative EMRI-like cases, showing strong agreement between FIM predictions and MCMC posteriors, with MatBYIB achieving convergence on standard hardware. This approach provides a practical, scalable tool for parameter estimation of eccentric EMRIs for current and future space-based detectors such as LISA and Taiji, and lays groundwork for incorporating more physics and higher-order waveform models.
Abstract
Accurate parameter estimation(PE) of gravitational waves(GW) is essential for GW data analysis. In extreme mass-ratio inspiral binary(EMRI) systems, orbital eccentricity is a critical parameter for PE. However, current software for for PE of GW often neglects the direct estimation of orbital eccentricity. To fill this gap, we have developed the MatBYIB, a MATLAB-based software package for PE of GW with arbitrary eccentricity. The MatBYIB employs the Analytical Kludge (AK) waveform as a computationally efficient signal generator and computes parameter uncertainties via the Fisher Information Matrix (FIM) and the Markov Chain Monte Carlo (MCMC). For Bayesian inference, we implement the Metropolis-Hastings (M-H) algorithm to derive posterior distributions. To guarantee convergence, the Gelman-Rubin convergence criterion (the Potential Scale Reduction Factor R) is used to determine sampling adequacy, with MatBYIB dynamically increasing the sample size until R < 1.05 for all parameters. Our results demonstrate strong agreement between FIM- based predictions and full MCMC sampling. This program is user-friendly and allows for estimation of gravitational wave parameters with arbitrary eccentricity on standard personal computers. Code availability:The implementation is open-source at https://github.com/GenliangLi/MatBYIB.
