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Current searches for gravitational waves from black hole binaries with LIGOand Virgo observatories are limited to using the analytical models computed forthe systems with spins of the black holes aligned with the orbital angularmomentum of the binary. The observation of black hole binaries with precessingspins (spins misaligned with the orbital angular momentum) could provide uniqueastrophysical insights into the formation of these sources; thus, it is crucialto design a search scheme to detect compact binaries with precession spins. Ithas been shown that the detection of compact binaries carrying precessing spinsis not achievable with aligned-spins template waveforms. Efforts have been madeto construct the template banks to detect the precessing binaries using thematched filtering based detection pipelines. However, in the absence of robustmethods and huge computational requirements, the precessing searches, more orless, still remain a challenge. This work introduces a detection scheme for thebinary black holes to classify them into aligned and precessing systems using aconvolution neural network (\CNN). We treat the detection of the $\GW$ signalsfrom $\BBH$ systems as a classification problem. Our architecture firstclassifies data as $\GW$ signals originated from binary black holes ($\BBH$) ornoise and then it further classifies the detected $\BBH$ systems as precessingor non-precessing (aligned/anti-aligned) systems. Our classifier with anaccuracy of $\approx$ 99\% classifies between noise and the signal and with anaccuracy of $\approx$ 91\% it classifies the detected signals into aligned andprecessing signals. We have also extended our analysis for a multi-detectorframework and tested the performance of our designed architecture on $\O1$,$\O2$ and $\O3$ data to identify the detected $\BBH$ events as aligned andprecessing events.

Can Convolution Neural Networks Be Used for Detection of Gravitational Waves from Precessing Black Hole Systems?