Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae

We test Deep-Learning (DL) techniques for the analysis of rotational core-collapse supernovae (CCSN) gravitational-wave (GW) signals by performing classification and parameter inference of the GW strain amplitude (D ·Δ h) and the maximum (peak) frequency (f_peak), attained at core bounce. Our datasets are built from a catalog of numerically generated CCSN waveforms assembled by Richers 2017. Those waveforms are injected into noise from the Advanced LIGO and Advanced Virgo detectors corresponding to the O2 and O3a observing runs. For a signal-to-noise ratio (SNR) above 5, our classification network using time series detects Galactic CCSN GW signals buried in detector noise with a false positive rate (FPR) of 0.10 accuracy, being able to detect all signals with SNR>10. The inference of f_peak is more accurate than for D ·Δ h, particularly for our datasets with the shortest time window (0.25 s) and for a minimum SNR=15. From the calibration plots of predicted versus true values of the two parameters, the standard deviation (σ) and the slope deviation with respect to the ideal value are computed. We find σ_D ·Δ h = 52.6cm and σ_f_peak = 18.3Hz, with respective slope deviations of 11.6 recent CCSN catalog built by Mitra 2023, different from the one used for the training. For these new waveforms the true values of the two parameters are mostly within the 1σ band around the network's predicted values. Our results show that DL techniques hold promise to infer physical parameters of Galactic rotational CCSN even in the presence of real (non-Gaussian) noise conditions from current GW detectors.