Deep learning for multimessenger 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 observation 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 O2 run, it would have been possible to detect signals emitted at 1 kpc of distance, whilst lowering down the efficiency to 60%, the event distance reaches values up to 14 kpc.

References in corpus (37)

• Adam: A Method for Stochastic Optimization
• GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs
• Nearby Supernova Rates from the Lick Observatory Supernova Search. II. The Observed Luminosity Functions and Fractions of Supernovae in a Complete Sample
• Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo
• Method for detection and reconstruction of gravitational wave transients with networks of advanced detectors
• BayesWave: Bayesian Inference for Gravitational Wave Bursts and Instrument Glitches
• A New Multi-Dimensional General Relativistic Neutrino Hydrodynamics Code of Core-Collapse Supernovae III. Gravitational Wave Signals from Supernova Explosion Models
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• A Model for Gravitational Wave Emission from Neutrino-Driven Core-Collapse Supernovae
• SNEWS: The SuperNova Early Warning System
• Exploring Fundamentally Three-dimensional Phenomena in High-fidelity Simulations of Core-collapse Supernovae
• Characterizing the Gravitational Wave Signal from Core-Collapse Supernovae
• Equation-of-State Dependent Features in Shock-Oscillation Modulated Neutrino and Gravitational-Wave Signals from Supernovae
• Gravitational Wave Signals from 3D Neutrino Hydrodynamics Simulations of Core-Collapse Supernovae
• Gravitational Waves from Core Collapse Supernovae
• An information-theoretic approach to the gravitational-wave burst detection problem
• A New Gravitational-Wave Signature from Standing Accretion Shock Instabilities in Supernovae
• Parametrized 3D models of neutrino-driven supernova explosions: Neutrino emission asymmetries and gravitational-wave signals
• Gravitational Wave Emission from 3D Explosion Models of Core-Collapse Supernovae with Low and Normal Explosion Energies
• The influence of model parameters on the prediction of gravitational wave signals from stellar core collapse
• An Optically Targeted Search for Gravitational Waves emitted by Core-Collapse Supernovae during the First and Second Observing Runs of Advanced LIGO and Advanced Virgo
• Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks
• Low-Latency Gravitational Wave Alerts for Multi-Messenger Astronomy During the Second Advanced LIGO and Virgo Observing Run
• Correlated Signatures of Gravitational-Wave and Neutrino Emission in Three-Dimensional General-Relativistic Core-Collapse Supernova Simulations
• All-sky search for short gravitational-wave bursts in the first Advanced LIGO run
• All-sky search for short gravitational-wave bursts in the second Advanced LIGO and Advanced Virgo run
• A First Targeted Search for Gravitational-Wave Bursts from Core-Collapse Supernovae in Data of First-Generation Laser Interferometer Detectors
• Universal relations for gravitational-wave asteroseismology of proto-neutron stars
• Neutrino Emission from Supernovae
• Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach
• A New Method to Observe Gravitational Waves emitted by Core Collapse Supernovae
• How common are long Gamma-Ray Bursts in the Local Universe?
• Effects of Degradations on Deep Neural Network Architectures
• Dimension dependence of numerical simulations on gravitational waves from protoneutron stars
• Improving the background of gravitational-wave searches for core collapse supernovae: A machine learning approach
• Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning
• Parameter estimates in binary black hole collisions using neural networks

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• Inferring Astrophysical Parameters of Core-Collapse Supernovae from their Gravitational-Wave Emission
• Space-based gravitational wave signal detection and extraction with deep neural network
• LSTM and CNN application for core-collapse supernova search in gravitational wave real data
• Simulating Transient Noise Bursts in LIGO with Generative Adversarial Networks
• Three approaches for the classification of protoneutron star oscillation modes
• Applications of machine learning in gravitational wave research with current interferometric detectors
• Generalised gravitational burst generation with Generative Adversarial Networks
• Applications of Machine Learning to Detecting Fast Neutrino Flavor Instabilities in Core-Collapse Supernova and Neutron Star Merger Models
• Real-Time Detection of Unmodelled Gravitational-Wave Transients Using Convolutional Neural Networks
• Convolutional neural network for gravitational-wave early alert: Going down in frequency
• Application of the Hilbert-Huang transform for analyzing standing-accretion-shock-instability induced gravitational waves in a core-collapse supernova
• Using supervised learning algorithms as a follow-up method in the search of gravitational waves from core-collapse supernovae
• Dilated convolutional neural network for detecting extreme-mass-ratio inspirals
• Convolutional neural network search for long-duration transient gravitational waves from glitching pulsars
• Exploring Supernova Gravitational Waves with Machine Learning
• Multi-messenger observations of core-collapse supernovae: Exploiting the standing accretion shock instability
• Visualizing convolutional neural network for classifying gravitational waves from core-collapse supernovae
• Probing nuclear physics with supernova gravitational waves and machine learning
• Matched filtering for gravitational wave detection without template bank driven by deep learning template prediction model bank
• Localization of gravitational waves using machine learning
• A convolutional neural network to distinguish glitches from minute-long gravitational wave transients
• Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae
• Detecting and reconstructing gravitational waves from the next Galactic core-collapse supernova in the Advanced Detector Era
• Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification
• Parameter estimation of protoneutron stars from gravitational wave signals using the Hilbert-Huang transform
• Null Stream Based Third-generation-ready Glitch Mitigation for Gravitational Wave Measurements
• Nonlinear noise regression in gravitational-wave detectors with convolutional neural networks
• Generative adversarial network for stellar core-collapse gravitational waves
• Unveiling gravitational waves from core-collapse supernovae with MUSE
• A machine learning algorithm for minute-long Burst searches