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 of a few Hz, thus enabling the detection of gravitational wave signals of very long duration. The run time of standard parameter estimation techniques with these long waveforms can be months or even years, making it impractical with existing Bayesian inference pipelines. Reduced order modeling and the reduced order quadrature integration rule have recently been exploited as promising techniques that can greatly reduce parameter estimation computational costs. We describe a Python-based reduced order quadrature building code, pyroq, which builds the reduced order quadrature data needed to accelerate parameter estimation of gravitational waves. We present the first bases for the imrphenomxphm waveform model of binary-black-hole coalescences, including subdominant harmonic modes and precessing spins effects.