  We present the results on the search for the coalescence of compact binary
mergers using convolutional neural networks and the LIGO/Virgo data for the O3
observation period. Two-dimensional images in time and frequency are used as
input. The analysis is performed in three separate mass regions covering the
range for the masses in the binary system from 0.2 to 100 solar masses,
excluding very asymmetric mass configurations. We explore neural networks
trained with input information from pairs of interferometers or all three
interferometers together, concluding that the use of the maximum information
available leads to an improved performance. A scan over the O3 data set, using
the convolutional neural networks, is performed with different fake rate
thresholds for claiming detection of at most one event per year or at most one
event per week. The latter would correspond to a loose online selection still
leading to affordable fake alarm rates. The efficiency of the neutral networks
to detect the O3 catalog events is discussed. In the case of a fake rate
threshold of at most one event per week, the scan leads to the detection of
about 50% of the O3 catalog events. Once the search is limited to the catalog
events within the mass range used for neural networks training, the detection
efficiency increases up to 70%. A further improvement in the search efficiency,
using the same kind of algorithms, will require the implementation of new
criteria for the suppression of detector glitches.
