Python-based reduced order quadrature building code for fast gravitational wave inference

The next generation of gravitational-wave observatories will reach low frequency limits on the orders a few Hz, thus enabling detection gravitational wave signals very long duration. run time standard parameter estimation techniques with these waveforms can be months or even years, making it impractical existing Bayesian inference pipelines. Reduced order modeling and reduced quadrature integration rule have recently been exploited as promising that greatly reduce computational costs. We describe Python-based building code, PyROQ, which builds data needed to accelerate waves. present first bases for IMRPhenomXPHM waveform model binary-black-hole coalescences, including subdominant harmonic modes precessing spins effects. Furthermore, code infrastructure makes directly applicable space-borne detectors such Laser Interferometer Space Antenna (LISA).