Datensatz

Zusammenfassung

The Parameter Estimation (PE) for Gravitational Waves (GW) merger events
relies on a waveform model calibrated using numerical simulations. Within the
Bayesian framework, this waveform model represents the GW signal produced
during the merger and is crucial for estimating the likelihood function.
However, these waveform models may possess systematic errors that can differ
across the parameter space. Addressing these errors in the current data
analysis pipeline is an active area of research. This work presents a framework
for accounting for uncertainties in waveform modeling. We introduce two
parametrizations, relative and absolute errors in the phase of the waveform, to
modify the base waveform model, which can account for uncertainties. When the
waveform errors are known, those error budgets can be used as a prior
distribution in the Bayesian framework. We also show that conservative priors
can be used to quantify uncertainties in waveform modeling without any
knowledge of waveform error budgets. By conducting zero-noise injections and
recoveries, we demonstrate through PE results that even 1-2% of errors in
relative phase to the actual waveform model can introduce biases in the
recovered parameters. These biases can be corrected when we account for
waveform uncertainties within the PE framework. By injecting a series of
precessing waveform models and using the nonspinning model for recovery, we
show that our method can account for the missing physics by making the
posterior samples broad enough to account for bias. We also present a Python
package that is easily integrated with the publicly available GW analysis tool
PyCBC and can be used to do PE with the parametrization presented in this
paper.