  Gravitational Waves (GWs) from coalescing binaries carry crucial information
about their component sources, like mass, spin and tidal effects. This implies
that the analysis of GW signals from binary neutron star mergers can offer
unique opportunities to extract information about the tidal properties of NSs,
thereby adding constraints to the NS equation of state. In this work, we use
Deep Learning (DL) techniques to overcome the computational challenges
confronted in conventional methods of matched-filtering and Bayesian analyses
for signal-detection and parameter-estimation. We devise a DL approach to
classify GW signals from binary black hole and binary neutron star mergers. We
further employ DL to analyze simulated GWs from binary neutron star merger
events for parameter estimation, in particular, the regression of mass and
tidal deformability of the component objects. The results presented in this
work demonstrate the promising potential of DL techniques in GW analysis,
paving the way for further advancement in this rapidly evolving field. The
proposed approach is an efficient alternative to explore the wealth of
information contained within GW signals of binary neutron star mergers, which
can further help constrain the NS EoS.
