  Current searches for gravitational waves (GWs) from black hole binaries using
the LIGO and Virgo observatories are limited to analytical models for systems
with black hole spins aligned (or anti-aligned) with the orbital angular
momentum of the binary. Detecting black hole binaries with precessing spinsis
crucial for gaining unique astrophysical insights into the formation of these
sources. Therefore, it is essential to develop a search strategy capable of
identifying compact binaries with precessing spins. Aligned-spin waveform
models are inadequate for detecting compact binaries with high precessing
spins. While several efforts have been made to construct template banks for
detecting precessing binaries using matched filtering, this approach requires
many templates to cover the entire search parameter space, significantly
increasing the computational cost. This work explores the detection of GW
signals from binary black holes(BBH) with both aligned and precessing spins
using a convolutional neural network (CNN). We frame the detection of GW
signals from aligned or precessing BBH systems as a hierarchical binary
classification problem. The first CNN model classifies strain data as either
pure noise or noisy signals (GWs from BBH). A second CNN model then classifies
the detected noisy signal data as originating from either precessing or
non-precessing (aligned/anti-aligned) systems. Using simulated data, the
trained classifier distinguishes between noise and noisy GW signals with more
than 99% accuracy. The second classifier further differentiates between aligned
and highly precessing signals with around 95% accuracy. We extended our
analysis to a multi-detector framework by performing a coincident test.
Additionally, we tested the performance of our trained architecture on data
from the first three observation runs of LIGO to identify detected BBH events
as either aligned or precessing.
