Improved post-Newtonian waveform model for inspiralling precessing-eccentric compact binaries

TL;DR

This paper introduces pyEFPE, a fast and stable frequency-domain post-Newtonian waveform model for inspiralling precessing-eccentric compact binaries, improving parameter estimation in gravitational wave astronomy.

Contribution

The paper presents a new waveform model with analytical Fourier mode amplitudes, enhanced stability, and faster computation, advancing the modeling of complex GW signals.

Findings

pyEFPE achieves up to 15x speedup in waveform computation.

pyEFPE shows good agreement with existing models in various limits.

Successfully recovers parameters from simulated GW signals.

Abstract

The measurement of spin-precession and orbital eccentricity in gravitational-wave (GW) signals is a key priority in GW astronomy, as these effects not only provide insights into the astrophysical formation and evolution of compact binaries but also, if neglected, could introduce significant biases in parameter estimation, searches, and tests of General Relativity. Despite the growing potential of upcoming LIGO-Virgo-KAGRA observing runs and future detectors to measure eccentric-precessing signals, accurately and efficiently modeling them remains a challenge. In this work, we present pyEFPE, a frequency-domain post-Newtonian (PN) waveform model for the inspiral of precessing-eccentric compact binaries. pyEFPE improves upon previous models by introducing analytical expressions for the Fourier mode amplitudes, enhancing the numerical stability of the multiple scale analysis framework, and adding recently derived PN corrections, critical to accurately describe signals in GW detectors. Additionally, we simplify the numerical implementation and introduce a scheme to interpolate the amplitudes, achieving a speedup of up to ~O(15) in the waveform computations, making the model practical for data analysis applications. We thoroughly validate pyEFPE by comparing it to other waveform models in the quasi-circular and eccentric-spin-aligned limits, finding good agreement. Additionally, we demonstrate pyEFPE's capability to analyze simulated GW events, accurately recovering the parameters of signals described by both pyEFPE and IMRPhenomXP. While pyEFPE still lacks important physical effects, such as higher-order PN corrections, higher-order modes, mode asymmetries, tidal interactions or the merger-ringdown phase, it represents a significant step towards more complete waveform models, offering a flexible and efficient framework that can be extended in future work to incorporate these effects.