  Core-collapse supernovae are sources of powerful gravitational waves (GWs).
We assess the possibility of extracting information about the equation of state
(EOS) of high density matter from the GW signal. We use the bounce and early
post-bounce signals of rapidly rotating supernovae. A large set of GW signals
is generated using general relativistic hydrodynamics simulations for various
EOS models. The uncertainty in the electron capture rate is parametrized by
generating signals for six different models. To classify EOSs based on the GW
data, we train a convolutional neural network (CNN) model. Even with the
uncertainty in the electron capture rates, we find that the CNN models can
classify the EOSs with an average accuracy of about 87 percent for a set of
four distinct EOS models.
