  A major boost in the understanding of the universe was given by the
revelation of the first coalescence event of two neutron stars (GW170817) and
the observation of the same event across the entire electromagnetic spectrum.
With 3rd Generation gravitational wave detectors and the new astronomical
facilities, we expect many multi messenger events of the same type. We
anticipate the need to analyse the data provided to us by such events, to
fulfill the requirements of real-time analysis, but also in order to decipher
the event in its entirety through the information emitted in the different
messengers using Machine Learning. We propose a change in the paradigm in the
way we will do multi-messenger astronomy, using simultaneously the complete
information generated by violent phenomena in the Universe. What we propose is
the application of a multimodal machine learning approach to characterize these
events.
