Debiasing Cosmic Gravitational Wave Sirens
  Accurate estimation of the Hubble constant, and other cosmological
parameters, from distances measured by cosmic gravitational wave sirens
requires sufficient allowance for the dark energy evolution. We demonstrate how
model independent statistical methods, specifically Gaussian process
regression, can remove bias in the reconstruction of $H(z)$, and can be
combined model independently with supernova distances. This allows stringent
tests of both $H_0$ and $\Lambda$CDM, and can detect unrecognized systematics.
We also quantify the redshift systematic control necessary for the use of dark
sirens, showing that it must approach spectroscopic precision to avoid
significant bias.
