  The gravitational waves emitted during the coalescence of binary black holes
are an excellent probe to test the behaviour of strong gravity. In this paper,
we propose a new test called the `merger-ringdown consistency test` that
focuses on probing the imprints of the dynamics in strong-gravity around the
black-holes during the plunge-merger and ringdown phase. Furthermore, we
present a scheme that allows us to efficiently combine information across
multiple ringdown observations to perform a statistical null test of GR using
the detected BH population. We present a proof-of-concept study for this test
using simulated binary black hole ringdowns embedded in the next-generation
ground-based detector noise. We demonstrate the feasibility of our test using a
deep learning framework, setting a precedence for performing precision tests of
gravity with neural networks.
