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Current searches for gravitational waves (GWs) from black hole binaries usingthe LIGO and Virgo observatories are limited to analytical models for systemswith black hole spins aligned (or anti-aligned) with the orbital angularmomentum of the binary. Detecting black hole binaries with precessing spinsiscrucial for gaining unique astrophysical insights into the formation of thesesources. Therefore, it is essential to develop a search strategy capable ofidentifying compact binaries with precessing spins. Aligned-spin waveformmodels are inadequate for detecting compact binaries with high precessingspins. While several efforts have been made to construct template banks fordetecting precessing binaries using matched filtering, this approach requiresmany templates to cover the entire search parameter space, significantlyincreasing the computational cost. This work explores the detection of GWsignals from binary black holes(BBH) with both aligned and precessing spinsusing a convolutional neural network (CNN). We frame the detection of GWsignals from aligned or precessing BBH systems as a hierarchical binaryclassification problem. The first CNN model classifies strain data as eitherpure noise or noisy signals (GWs from BBH). A second CNN model then classifiesthe detected noisy signal data as originating from either precessing ornon-precessing (aligned/anti-aligned) systems. Using simulated data, thetrained classifier distinguishes between noise and noisy GW signals with morethan 99% accuracy. The second classifier further differentiates between alignedand highly precessing signals with around 95% accuracy. We extended ouranalysis to a multi-detector framework by performing a coincident test.Additionally, we tested the performance of our trained architecture on datafrom the first three observation runs of LIGO to identify detected BBH eventsas either aligned or precessing.

Detection of Gravitational Wave Signals from Precessing Binary Black Hole Systems using Convolutional Neural Network