Recalibrating Gravitational Wave Phenomenological Waveform Model

TL;DR

This paper introduces a gradient-based joint optimization method for recalibrating the IMRPhenomD gravitational waveform model, significantly reducing mismatch with numerical relativity data and revealing parameter space dependencies.

Contribution

It presents the first joint optimization of all waveform calibration coefficients using automatic differentiation, improving model accuracy and understanding parameter correlations.

Findings

Median mismatch reduced by 50% after recalibration.

Optimization effectiveness varies with source spins.

Joint calibration captures correlations between waveform parts.

Abstract

We investigate the possibility of improving the accuracy of the phenomenological waveform model, IMRPhenomD, by jointly optimizing all the calibration coefficients at once, given a set of numerical relativity (NR) waveforms. When IMRPhenomD was first calibrated to NR waveforms, different parts (i.e., the inspiral, merger, and ringdown) of the waveform were calibrated separately. Using ripple, a library of waveform models compatible with automatic differentiation, we can, for the first time, perform gradient-based optimization on all the waveform coefficients at the same time. This joint optimization process allows us to capture previously ignored correlations between separate parts of the waveform. We found that after recalibration, the median mismatch between the model and NR waveforms decreases by 50%. We further explore how different regions of the source parameter space respond to the optimization procedure. We find that the degree of improvement correlates with the spins of the source. This work shows a promising avenue to help understand and treat systematic error in waveform models.