Speaker
Description
Among astrophysical gravitational waves sources yet undetected, of great interest are the binary close encounters involving black holes and/or neutron stars. These systems are characterized by high orbital eccentricities and form via dynamical interactions in dense stellar environments, like globular clusters or Active Galactic Nuclei disks. Their detection could shed light on the different formation channels and could allow tests of General Relativity in the strong field regime as well as multimessenger observations.
The expected gravitational wave emission from these events differs from standard coalescences, being instead characterized by repeated short duration bursts emitted at each periastron passage of the two objects. The burst nature of the signal paired with the expected low signal-to-noise ratio makes them a challenging source either for detection or parameter estimation with traditional data analysis approaches. We present HYPERION (“HYP-er fast close EncounteR Inference from Observation with Normalizing-flows”): a novel data analysis pipeline based on probabilistic machine learning and normalizing flows to infer Bayesian posterior. We show that our method is very promising, since it can make detection and inference several orders of magnitude faster than traditional techniques while maintaining high accuracy on the parameters, thus showing how machine learning could help in studying these sources.
