Title: Deep learning to detect gravitational waves from binary close encounters: Fast parameter estimation using normalizing flows

Abstract: 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.