Accelerating parameter estimation for parameterized tests of general relativity with gravitational-wave observations

Abstract

Tests of general relativity (GR) with gravitational waves (GWs) introduce additional deviation parameters in the waveform model. The enlarged parameter space makes inference computationally costly, which has so far limited systematic, large-scale studies that are essential to quantify parameter degeneracies, check the effect of waveform systematics, and assess robustness across non-stationary and non-Gaussian noise effects. The need is even sharper for next-generation observatories where signals are longer, signal-to-noise ratios (SNRs) are higher, and likelihood evaluations increase substantially. We address this by applying relative binning to the TIGER framework for parameterized tests of GR. Relative binning replaces dense frequency waveform evaluations with evaluations on adaptively chosen frequency bins, reducing the cost per likelihood call while preserving posterior accuracy. Using simulated binary black hole signals, we demonstrate unbiased recovery for GR-consistent cases and targeted non-GR deviations, and we map how bin resolution controls accuracy, with finer binning primarily required for the $-1$ post-Newtonian term. A high-SNR simulated signal at next-generation sensitivity further shows accurate recovery with tight posteriors. Applied to GW150914 and GW250114, both single and multi-parameter TIGER analyses finish within a day, yielding bounds consistent with GR at 90\% credibility and in agreement with previous results. Across analyses, the method reduces wall time by factors of $\mathcal{O}(10)$ to $\mathcal{O}(100)$, depending on frequency range and binning scheme, without degrading parameter estimation accuracy.