Characterizing the gravitational wave temporal evolution of the gmode fundamental resonant frequency for a core collapse supernova: A neural network approach

Understand

We present a methodology based on the implementation of a fully connected neural network to estimate the gravitational wave (GW) temporal evolution of the gmode fundamental resonant frequency for a Core Collapse Supernova (CCSN).

• To perform the estimation, we construct a training data set, using synthetic waveforms, that serves to train the ML algorithm, and then use several CCSN waveforms to test the model.
• According to the results obtained from the implementation of our model, we provide numerical evidence to support the classification of progenitors according to their degree of rotation.
• The relative error associated with the estimate of the slope of the resonant frequency versus time for the GW from CCSN signals is within $13\%$ for the tested candidates included in this study.