  Excess transient noise artifacts, or glitches impact the data quality of
ground-based gravitational-wave (GW) detectors and impair the detection of
signals produced by astrophysical sources. Mitigation of glitches is crucial
for improving GW signal detectability. However, glitches are the product of
short-lived linear and non-linear couplings among the interrelated
detector-control systems that include optic alignment systems and mitigation of
seismic disturbances, generally making it difficult to model noise couplings.
Hence, typically time periods containing glitches are vetoed to mitigate the
effect of glitches in GW searches at the cost of reduction of the analyzable
data. To increase the available data period and improve the detectability for
both model and unmodeled GW signals, we present a new machine learning based
method which uses on-site sensors/system-controls monitoring the instrumental
and environmental conditions to model noise couplings and subtract glitches in
GW detector data. We find that our method reduces 20-70% of the excess power in
the data due to glitches. By injecting software simulated signals into the data
and recovering them with one of the unmodeled GW detection pipelines, we
address the robustness of the glitch-reduction technique to efficiently remove
glitches with no unintended effects on GW signals.
