Accelerating parameter estimation of gravitational waves from compact binary coalescence using adaptive frequency resolutions

TL;DR

This paper introduces an adaptive frequency resolution method that significantly accelerates Bayesian parameter estimation for gravitational waves from compact binary coalescence, reducing computational costs without prior source parameter estimates.

Contribution

The authors propose a novel adaptive frequency resolution technique that speeds up waveform generation in gravitational wave parameter estimation, avoiding fixed upsampling costs and offline preparations.

Findings

Speeds up parameter estimation by a factor of ~10 for 20 Hz cutoff.

Achieves a speed-up of ~100 for 5 Hz cutoff.

Does not require offline precomputations or initial source parameter estimates.

Abstract

Bayesian parameter estimation of gravitational waves from compact binary coalescence (CBC) typically requires more than millions of evaluations of computationally expensive template waveforms. We propose a technique to reduce the cost of waveform generation by exploiting the chirping behavior of CBC signal. Our technique does not require waveforms at all frequencies in the frequency range used in the analysis, and does not suffer from the fixed cost due to the upsampling of waveforms. Our technique speeds up the parameter estimation of typical binary neutron star signal by a factor of $\mathcal{O}(10)$ for the low-frequency cutoff of $20\,\mathrm{Hz}$, and $\mathcal{O}(10^2)$ for $5\,\mathrm{Hz}$. It does not require any offline preparations or accurate estimates of source parameters provided by detection pipelines.