Generative adversarial network for stellar core-collapse gravitational waves

We present a rapid stellar core-collapse waveform emulator built using a deep convolutional generative adversarial network (DCGAN). The DCGAN was trained on the Richers et al. [Phys. Rev. D 95 , 063019 (2017)] waveform catalog to learn the structure of rotating stellar core-collapse gravitational-wave signals and generate realistic waveforms. We show that the DCGAN learns the distribution of the training data reasonably well, and that the waveform emulator produces signals that appear to have the key features of core-collapse, bounce, early postbounce, and ringdown oscillations of the early protoneutron star. The pretrained DCGAN can therefore be used as a phenomenological model for rotating stellar core-collapse gravitational-waves.