  A yet undetected class of GW signals is represented by the close encounters
between compact objects in highly-eccentric e~1 orbits, that can occur in
binary systems formed in dense environments such as globular clusters. The
expected gravitational signals are short-duration pulses that would repeat over
a much longer time scale in case of multiple passages at periastron. These
sources represent a unique opportunity of exploring astrophysical formation
channels as well as a different way of testing GR. In the case of binary
systems containing neutron stars, the observation of these sources could help
to constrain the EOS, thanks to the signature left in the GW signal by the
f-modes excitation that can occur during the encounter. The detection and PE of
these signals is challenging given the short duration of expected signals and
the sensitivities of current ground-based GW interferometers. We present a
novel approach that exploits Probabilistic ML. We have used Conditional
Normalizing Flows to model complex probability distributions and therefore
infer posterior distributions for the source parameters. Fast detection and PE
is very important as it could trigger electromagnetic follow-up campaigns and
offer the possibility to study these events in a multimessenger context. To
develop and test the algorithm, we have focused on the simulations of single
bursts emission obtained using the Effective Fly-by formalism and embedded in
the noise of aLIGO and Virgo during O3. Our proposed model outperforms standard
Bayesian methods in accuracy and is 5 orders of magnitude faster, being able to
produce 5x10^4 posterior samples in just 0.5s. The results are extremely
promising and constitute the first successful attempt for a fast and complete
parameter estimation of binary CEs using deep learning, offering a new approach
to study the evolution of orbital parameters of compact binary systems.
