  Gravitational wave astronomy has been already a well-established research
domain for many years. Moreover, after the detection by LIGO/Virgo
collaboration, in 2017, of the first gravitational wave signal emitted during
the collision of a binary neutron star system, that was accompanied by the
detection of other types of signals coming from the same event, multi-messenger
astronomy has claimed its rights more assertively. In this context, it is of
great importance in a gravitational wave experiment to have a rapid mechanism
of alerting about potential gravitational waves events other observatories
capable to detect other types of signals (e.g. in other wavelengths) that are
produce by the same event. In this paper, we present the first progress in the
development of a neural network algorithm trained to recognize and characterize
gravitational wave patterns from signal plus noise data samples. We have
implemented two versions of the algorithm, one that classifies the
gravitational wave signals into 2 classes, and another one that classifies them
into 4 classes, according to the mass ratio of the emitting source. We have
obtained promising results, with 100% training and testing accuracy for the
2-class network and approximately 95% for the 4-class network. We conclude that
the current version of the neural network algorithm demonstrates the ability of
a well-configured and calibrated Bidirectional Long-Short Term Memory software
to classify with very high accuracy and in an extremely short time
gravitational wave signals, even when they are accompanied by noise. Moreover,
the performance obtained with this algorithm qualifies it as a fast method of
data analysis and can be used as a low-latency pipeline for gravitational wave
observatories like the future LISA Mission.
