10–14 Nov 2025
Europe/Prague timezone

Combining machine learning with recurrence analysis for resonance detection

13 Nov 2025, 14:00
20m

Speaker

Ondřej Zelenka (Astronomical Institute of the Czech Academy of Sciences)

Description

The width of a resonance in a nearly integrable system can tell us how a perturbation parameter is driving the system away from integrability. Although the tool that we are presenting here is quite generic, and can be used in a variety of systems, our particular interest lies in binary compact object systems known as extreme mass ratio inspirals (EMRIs), when a lighter compact object, like a black hole or a neutron star, inspirals into a supermassive black hole due to gravitational radiation reaction. During this inspiral, the lighter object crosses resonances, which are still not very well modeled. Finding resonances in EMRI models in EMRI models is critical to incorporate them in waveform models. To tackle this issue in our study, we show first that recurrence quantifiers of orbits carry imprints of resonant behavior. As a next step, we apply a long short-term memory machine learning architecture to automate the resonance detection procedure. Our analysis is developed on a simple standard map and gradually we extend it to more complicated systems, until finally we employ it in a generic deformed Kerr spacetime known in the literature as the Johannsen-Psaltis spacetime.

Primary author

Ondřej Zelenka (Astronomical Institute of the Czech Academy of Sciences)

Co-authors

Georgios Loukes Gerakopoulos (Astronomical Institute, Czech Academy of Sciences) Ondřej Kopáček (Technical University of Liberec)

Presentation materials

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