Deep learning for core-collapse supernova detection

The detection of gravitational waves from core-collapse supernova (CCSN) explosions is a challenging task, yet to be achieved, in which it is key the connection between multiple messengers, including neutrinos and electromagnetic signals. In this work, we present a method for detecting these kind of signals based on machine learning techniques. We tested its robustness by injecting signals in the real noise data taken by the Advanced LIGO-Virgo network during the second observing run, O2. We trained a newly developed MiniInception Resnet neural network using time-frequency images corresponding to injections of simulated phenomenological signals, which mimic the waveforms obtained in 3D numerical simulations of CCSNe. With this algorithm we were able to identify signals from both our phenomenological template bank and from actual numerical 3D simulations of CCSNe. We computed the detection efficiency versus the source distance, obtaining that, for signal to noise ratio higher than 15, the detection efficiency is 70% at a false alarm rate lower than 5%. We notice also that, in the case of the O2 run, it would have been possible to detect signals emitted at 1 kpc of distance, while lowering down the efficiency to 60%, the event distance reaches values up to 14 kpc.

Automated summary

By using machine learning techniques, we successfully detected signals from core-collapse supernova explosions in real data, with the detection efficiency and false alarm rate calculated based on the signal-to-noise ratio. This method showed high effectiveness and sensitivity during the O2 observation period.