  Excess transient noise events, or glitches, impact the data quality of
ground-based ravitational-wave (GW) detectors and impair the detection of
signals produced by astrophysical sources. Identification of the causes of
these glitches is a crucial starting point for the improvement of GW signal
detectability. However, glitches are the product of linear and non-linear
couplings among the interrelated detector-control systems that include
mitigation of ground motion and regulation of optic motion, which generally
makes it difficult to find their origin. We present a new software called
PyChChoo which uses time series recorded in the instrumental control systems
and environmental sensors around times when glitches are present in the
detector's output to reveal essential clues about their origin. Applying
PyChChoo on the most adversely affecting glitches on background triggers
generated by one of unmodeled GW detection pipelines called coherent WaveBurst
(cWB) operated in the data from the LIGO detectors between January 1st, 2020
and February 3rd, 2020, we find that 80% of triggers are marked as either being
vetoed or unvetoed in common between our analysis and the current LIGO
infrastructure.
