LitePIG: A Lite Parameter Inference system for the Gravitational wave in the millihertz band

TL;DR

LitePIG is a Python-based parameter inference system for millihertz gravitational waves, incorporating higher-order modes to significantly improve distance estimation and test general relativity with high precision.

Contribution

The paper introduces LitePIG, a novel, efficient parameter inference system that includes higher-order harmonic modes for gravitational wave analysis in the millihertz band.

Findings

Including higher-order modes improves luminosity distance estimation by a factor of 50.

The system can constrain higher harmonic amplitudes and deviations from GR with high precision.

The method enhances the robustness of testing general relativity using gravitational wave data.

Abstract

We present a python based parameter inference system for the gravitational wave (GW) measured in the millihertz band. This system includes the following features: the GW waveform originated from the massive black hole binaries (MBHB), the stationary instrumental gaussian noise, the higher-order harmonic modes, the full response function from the time delay interferometry (TDI) and the gaussian likelihood function with the dynamic nested parameter sampler. In particular, we highlight the role of higher-order modes. By including these modes, the luminosity distance estimation precision can be improved roughly by a factor of 50, compared with the case with only the leading order ($\ell=2,|m|=2$) mode. This is due to the response function of different harmonic modes on the inclination angle are different. Hence, it can help to break the distance-inclination degeneracy. Furthermore, we show the robustness of testing general relativity (GR) by using the higher-order harmonics. Our results show that the GW from MBHB can simultaneously constrain four of the higher harmonic amplitudes (deviation from GR) with a precision of $c_{21}=0.54^{+0.61}_{-0.82}$, $c_{32}=-0.65^{+0.22}_{-0.08}$, $c_{33}=0.56^{+0.60}_{-0.76}$ and $c_{44}=1.57^{+2.34}_{-1.90}$, respectively.