Deep learning for core-collapse supernova detection

Abstract

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 Mini-Inception 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.

Inferring Astrophysical Parameters of Core-Collapse Supernovae from their Gravitational-Wave Emission

“…Astone et al [30] produce a phenomenological model to describe the high-frequency CCSN signal mode, which they use to train a machine learning algorithm for the detection of CCSN gravitational-wave signals, however they do not perform waveform reconstruction or parameter estimation with their model. In a follow up study, Lopez et al [31] show that machine learning algorithms trained with this phenomenological model can detect CCSN waveforms from hydrodynamical simulations. Bizouard et al [32], use a set of 1D CCSN simulations between 11 M and 40 M to infer the PNS properties of a source in current or next generation gravitational-wave detectors.…”

Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks

“…This would be especially beneficial for searches that require low latency, such as the early warning of binary neutron star mergers (Baltus et al, 2021 ; Yu et al, 2021 ). Other successful usage of ML techniques in GW astronomy include the identification of various GW events (Bayley et al, 2020 ; Chan et al, 2020 ; Dreissigacker and Prix, 2020 ; Huerta et al, 2020 ; Krastev, 2020 ; Schäfer et al, 2020 ; Wong et al, 2020 ; Beheshtipour and Papa, 2021 ; Chang et al, 2021 ; Chatterjee et al, 2021 ; López et al, 2021 ; Marianer et al, 2021 ; Mishra et al, 2021 ; Saiz-Pérez et al, 2021 ; Wei and Huerta, 2021 ; Yan et al, 2021 ), source parameter estimations (Gabbard et al, 2019 ; Chatterjee et al, 2020 ; Chua and Vallisneri, 2020 ; Green et al, 2020 ; Talbot and Thrane, 2020 ; Álvares et al, 2021 ; D'Emilio et al, 2021 ; Krastev et al, 2021 ; Williams et al, 2021 ; Xia et al, 2021 ), and detector characterization (Biswas et al, 2020 ; Colgan et al, 2020 ; Cuoco et al, 2020 ; Essick et al, 2020 ; Torres-Forné et al, 2020 ; Mogushi, 2021 ; Sankarapandian and Kulis, 2021 ; Soni et al, 2021 ; Zhan et al, 2021 ). Besides GW astronomy, the usage of CNNs has led to breakthroughs in a variety of topics related to time-series forecasting and classification (e.g., Refs.…”

Localization of gravitational waves using machine learning

“…It is generally difficult to create an appropriate model wave-form for GWs from core-collapse supernovae, rendering matched filtering unsuitable. In view of this, machine learning has been introduced as an alternative [13][14][15][16]. Research is also underway as a method to analyze continuous waves [17][18][19][20][21].…”