Simulatenous Inference of Astrophysical and Noise Populations in Gravitational Wave Detectors

The global network of interferometric gravitational wave (GW) observatories (LIGO, Virgo, KAGRA) has detected and characterized nearly 100 high significance mergers of binary compact objects. However, many more real GWs are likely lurking subthreshold, which need to be sifted from terrestrial-origin noise triggers (known as glitches). Because glitches are not due to astrophysical phenomena, inference under the assumption of an astrophysical source (e.g. binary black hole coalescence) results in source parameters (masses, spins of the black holes) which are inconsistent with the known astrophysical population. In this work, we show how one can extract unbiased population constraints from a catalog of both real GW events and glitch contaminants by doing bayesian inference on their source populations simultaneously. For a proof of principle, we assume glitches come from a specific class with a well-characterized effective population (blip glitches). We also calculate posteriors on the probability of each event in the catalog belonging to the astrophysical or glitch class, and obtain posteriors on the number of astrophysical events in the catalog, finding it to be consistent with the actual number of events included.