  The detection of the binary neutron star (BNS) merger, GW170817, was the
first success story of multi-messenger observations of compact binary mergers.
The inferred merger rate along with the increased sensitivity of the
ground-based gravitational-wave (GW) network in the present LIGO/Virgo, and
future LIGO/Virgo/KAGRA observing runs, strongly hints at detection of binaries
which could potentially have an electromagnetic (EM) counterpart. A rapid
assessment of properties that could lead to a counterpart is essential to aid
time-sensitive follow-up operations, especially robotic telescopes. At minimum,
the possibility of counterparts require a neutron star (NS). Also, the tidal
disruption physics is important to determine the remnant matter post merger,
the dynamics of which could result in the counterparts. The main challenge,
however, is that the binary system parameters such as masses and spins
estimated from the real time, GW template-based searches are often dominated by
statistical and systematic errors. Here, we present an approach that uses
supervised machine-learning to mitigate such selection effects to report
possibility of counterparts based on presence of a NS component, and presence
of remnant matter post merger in real time.
