LIGO Seminar

Extracting Progenitor Parameters of Rotating CCSNe via Pattern Recognition and Machine Learning

Laksh Bashin, Undergraduate, Caltech,

Core-collapse supernovae (CCSNe) are among the most energetic events in the universe, releasing up to 10^53 erg = 100 B of gravitational potential energy. Based on theoretical predictions, they are also expected to emit bursts of gravitational waves (GWs) that will be detectable by second-generation laser interferometer GW observatories such as Advanced LIGO (aLIGO), Advanced Virgo, and KAGRA.

In a novel pattern-recognition approach, we investigate the inference of progenitor parameters from numerical GW signals produced by state-of-the-art rotating core-collapse simulations. After associating physical processes with characteristic spectrogram features, we develop several machine-learning (ML) algorithms that can accurately (often within Â±20% relative error on average) and precisely determine progenitor parameters from optimally-oriented CCSN signals located 5 kpc away from Earth.

We plan to broadcast this talk using SeeVogh.

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For more information, please contact Sydney Meshkov by email at [email protected].