Parameter estimation on compact binary coalescences with abruptly terminating gravitational waveforms

Abstract

Gravitational-wave astronomy seeks to extract information about astrophysical systems from the gravitational-wave signals they emit. For coalescing compact-binary sources this requires accurate model templates for the inspiral and, potentially, the subsequent merger and ringdown. Models with frequency-domain waveforms that terminate abruptly in the sensitive band of the detector are often used for parameter-estimation studies. We show that the abrupt waveform termination contains significant information that affects parameter-estimation accuracy. If the sharp cutoff is not physically motivated, this extra information can lead to misleadingly good accuracy claims. We also show that using waveforms with a cutoff as templates to recover complete signals can lead to biases in parameter estimates. We evaluate when the information content in the cutoff is likely to be important in both cases. We also point out that the standard Fisher matrix formalism, frequently employed for approximately predicting parameter-estimation accuracy, cannot properly incorporate an abrupt cutoff that is present in both signals and templates; this observation explains some previously unexpected results found in the literature. These effects emphasize the importance of using complete waveforms with accurate merger and ringdown phases for parameter estimation.

Very minor changes to match published version

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Cited by in corpus (5)

• Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
• Parameter estimation for binary neutron-star coalescences with realistic noise during the Advanced LIGO era
• Accelerated gravitational-wave parameter estimation with reduced order modeling
• Distinguishing types of compact-object binaries using the gravitational-wave signatures of their mergers
• Testing general relativity with compact coalescing binaries: comparing exact and predictive methods to compute the Bayes factor