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Bayesian parameter estimation of gravitational waves from compact binarycoalescence (CBC) typically requires more than millions of evaluations ofcomputationally expensive template waveforms. We propose a technique to reducethe cost of waveform generation by exploiting the chirping behavior of CBCsignal. Our technique does not require waveforms at all frequencies in thefrequency range used in the analysis, and does not suffer from the fixed costdue to the upsampling of waveforms. Our technique speeds up the parameterestimation 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 offlinepreparations or accurate estimates of source parameters provided by detectionpipelines.

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