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Bayesian inference of gravitational wave signals is subject to systematic error due to modeling uncertainty in waveform signal models coined approximants. A growing collection of approximants are available which use different approaches and make different assumptions to ease the process of model development. We provide a method to marginalize over the uncertainty in a set of waveform approximants by constructing a mixture-model multiwaveform likelihood. This method fits into existing workflows by determining the mixture parameters from the per-waveform evidence, enabling the production of marginalized combined sample sets from independent runs. [ABSTRACT FROM AUTHOR]

In order to separate astrophysical gravitational-wave signals from instrumental noise, which often contains transient non-Gaussian artifacts, astronomers have traditionally relied on bootstrap methods such as time slides. Bootstrap methods sample with replacement, comparing single-observatory data to construct a background distribution, which is used to assign a false-alarm probability to candidate signals. While bootstrap methods have played an important role establishing the first gravitational-wave detections, there are limitations. First, as the number of detections increases, it makes increasingly less sense to treat single-observatory data as bootstrap-estimated noise, when we know that the data are filled with astrophysical signals, some resolved, some unresolved. Second, it has been known for a decade that background estimation from time slides eventually breaks down due to saturation effects, yielding incorrect estimates of significance. Third, the false alarm probability cannot be used to weight candidate significance, for example when performing population inference on a set of candidates. Given recent debate about marginally resolved gravitational-wave detection claims, the question of significance has practical consequences. We propose a Bayesian framework for calculating the odds that a signal is of astrophysical origin versus instrumental noise without bootstrap noise estimation. We show how the astrophysical odds can safely accommodate glitches. We argue that it is statistically optimal. We demonstrate the method with simulated noise and provide examples to build intuition about this new approach to significance. [ABSTRACT FROM AUTHOR]

We infer the collapse times of long-lived neutron stars into black holes using the x-ray afterglows of 18 short gamma-ray bursts. We then apply hierarchical inference to infer properties of the neutron star equation of state and dominant spin-down mechanism. We measure the maximum non-rotating neutron star mass MTOV=2.31-0.21+0.36M⊙ and constrain the fraction of remnants spinning down predominantly through gravitational-wave emission to η=0.69-0.39+0.21 with 68% uncertainties. In principle, this method can determine the difference between hadronic and quark equation of states. In practice, however, the data is not yet informative with indications that these neutron stars do not have hadronic equation of states at the 1σ level. These inferences all depend on the underlying progenitor mass distribution for short gamma-ray bursts produced by binary neutron star mergers. The recently announced gravitational-wave detection of GW190425 suggests this underlying distribution is different from the locally measured population of double neutron stars. We show that MTOV and η constraints depend on the fraction of binary mergers that form through a distribution consistent with the locally measured population and a distribution that can explain GW190425. The more binaries that form from the latter distribution, the larger MTOV needs to be to satisfy the x-ray observations. Our measurements above are marginalized over this unknown fraction. If instead, we assume GW190425 is not a binary neutron star merger, i.e., the underlying mass distribution of double neutron stars is the same as observed locally, we measure MTOV=2.26-0.17+0.31M⊙. [ABSTRACT FROM AUTHOR]