  The global network of interferometric gravitational wave (GW) observatories
(LIGO, Virgo, KAGRA) has detected and characterized nearly 100 mergers of
binary compact objects. However, many more real GWs are lurking sub-threshold,
which need to be sifted from terrestrial-origin noise triggers (known as
glitches). Because glitches are not due to astrophysical phenomena, inference
on the glitch under the assumption it has an astrophysical source (e.g. binary
black hole coalescence) results in source parameters that are inconsistent with
what is known about the 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 performing Bayesian inference on their source
populations simultaneously. In this paper, 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.
