Investigating the efficiency of the SPIIR pipeline in O3 offline Gravitational wave search

Gravitational waves are 'ripples' in space-time caused by some of the most violent and energetic processes in the Universe. Detecting and analyzing the information carried by gravitational waves is allowing us to observe the Universe in a way never before possible, providing astronomers and other scientists with their first glimpses of literally unseeable wonders. SPIIR is one of the fastest of the five online pipelines.The SPIIR pipeline is distinguished from other CBC search pipelines in several aspects as it adopts the summed parallel infinite impulse response (SPIIR) method for matched filtering. This method is expected to be more efficient computationally than the traditional Fourier method when a filtering delay of less than 10s is intended. The other main difference is that the SPIIR pipeline selects candidates based on the maximum network likelihood ratio (MNLR) principle which is referred to as the coherent method. I use SPIIR and investigate its efficiency during the third LIGO-Virgo observing run (O3). I do this via investigating the distribution of False alarm rates of Gravitational waves via machine learning algorithm. I try to classify false alarms from the simulation of data  from GW detectors. My work will support SPIIR group's efforts to produce an 'offline' catalog of SPIIR GW.