We describe the search pipeline, called PyCBC , used to identify gravitational wave candidates in Advanced LIGO's O1 search. We give an overall summary of the pipeline including a general layout and the methods for calculating the signal-to-noise ratio and the chi-squared statistic. We then give detailed explanations of removing noise transient before processing, called gating ; the way to create a bank of template waveforms and the process of matched filtering these waveforms against the data; the chi-squared statistic's role in eliminating noise transients; the latest coincidence testing method, which requires candidates to be both nearly simultaneous and have the exact same parameters; and the method of translating a candidate's significance into a probability of being a true signal. We compare this pipeline to the one used in Initial LIGO's fifth and sixth science runs and report a 40% increase in sensitive volume with no significant change to computational cost.
