  Excess noise from scattered light poses a persistent challenge in the
analysis of data from gravitational wave detectors such as LIGO. We integrate a
physically motivated model for the behavior of these "glitches" into a standard
Bayesian analysis pipeline used in gravitational wave science. This allows for
the inference of the free parameters in this model, and subtraction of these
models to produce glitch-free versions of the data. We show that this inference
is an effective discriminator of the presence of the features of these
glitches, even when those features may not be discernible in standard
visualizations of the data.
