Exploring Supernova Gravitational Waves with Machine Learning

Abstract

Core-collapse supernovae (CCSNe) emit powerful gravitational waves (GWs). Since GWs emitted by a source contain information about the source, observing GWs from CCSNe may allow us to learn more about CCSNs. We study if it is possible to infer the iron core mass from the bounce and early ring-down GW signal. We generate GW signals for a range of stellar models using numerical simulations and apply machine learning to train and classify the signals. We consider an idealized favourable scenario. First, we use rapidly rotating models, which produce stronger GWs than slowly rotating models. Second, we limit ourselves to models with four different masses, which simplifies the selection process. We show that the classification accuracy does not exceed ~70%, signifying that even in this optimistic scenario, the information contained in the bounce and early ring-down GW signal is not sufficient to precisely probe the iron core mass. This suggests that it may be necessary to incorporate additional information such as the GWs from later post-bounce evolution and neutrino observations to accurately measure the iron core mass.

Accepted MNRAS. 12 pages, 13 figures

References in corpus (48)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• Advanced LIGO
• Nucleosynthesis and Remnants in Massive Stars of Solar Metallicity
• Core-Collapse Supernova Explosion Theory
• The gravitational wave burst signal from core collapse of rotating stars
• Hydrodynamics of core-collapse supernovae and their progenitors
• Features of the Acoustic Mechanism of Core-Collapse Supernova Explosions
• The Progenitor Dependence of the Preexplosion Neutrino Emission in Core-Collapse Supernovae
• Three-Dimensional Supernova Explosion Simulations of 9-, 10-, 11-, 12-, and 13-M${}_{\odot }$ Stars
• Three-dimensional core-collapse supernova simulations of massive and rotating progenitors
• Magnetorotational Explosion of A Massive Star Supported by Neutrino Heating in General Relativistic Three Dimensional Simulations
• Magnetar formation through a convective dynamo in protoneutron stars
• Correlated Signatures of Gravitational-Wave and Neutrino Emission in Three-Dimensional General-Relativistic Core-Collapse Supernova Simulations
• Gravitational-wave signal of a core-collapse supernova explosion of a 15 Solar mass star
• Parameter estimation with gravitational waves
• Anisotropic emission of neutrino and gravitational-wave signals from rapidly rotating core-collapse supernovae
• A systematic study of proto-neutron star convection in three-dimensional core-collapse supernova simulations
• Gravitational Wave Extraction in Simulations of Rotating Stellar Core Collapse
• Noise Reduction in Gravitational-wave Data via Deep Learning
• Inferring the core-collapse supernova explosion mechanism with gravitational waves
• Rotating Collapse of Stellar Iron Cores in General Relativity
• A New Method to Observe Gravitational Waves emitted by Core Collapse Supernovae
• Gravitational Waves from Neutrino Asymmetries in Core-Collapse Supernovae
• Supernova Seismology: Gravitational Wave Signatures of Rapidly Rotating Core Collapse
• Deep learning for multimessenger core-collapse supernova detection
• Stellar Mass Black Hole Formation and Multi-messenger Signals from Three Dimensional Rotating Core-Collapse Supernova Simulations
• Inference of proto-neutron star properties from gravitational-wave data in core-collapse supernovae
• Star formation rates and stellar masses from machine learning
• Universal relation for supernova gravitational waves
• A scalable random forest regressor for combining neutron-star equation of state measurements: A case study with GW170817 and GW190425
• Features of Accretion-phase Gravitational-wave Emission from Two-dimensional Rotating Core-collapse Supernovae
• Inferring Astrophysical Parameters of Core-Collapse Supernovae from their Gravitational-Wave Emission
• Insights into non-axisymmetric instabilities in three-dimensional rotating supernova models with neutrino and gravitational-wave signatures
• Astrophysics with core-collapse supernova gravitational wave signals in the next generation of gravitational wave detectors
• Determining the Structure of Rotating Massive Stellar Cores with Gravitational Waves
• Gravitational-wave Signals From Three-dimensional Supernova Simulations With Different Neutrino-Transport Methods
• Gravitational wave signature of proto-neutron star convection: I. MHD numerical simulations
• Gravitational Waves from Core-Collapse Supernovae
• Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning
• Applications of Machine Learning to Predicting Core-collapse Supernova Explosion Outcomes
• Gravitational Wave Signals from Two-Dimensional Core-Collapse Supernova Models with Rotation and Magnetic Fields
• Bayesian parameter estimation of core collapse supernovae using gravitational wave simulations
• Multivariate Regression Analysis of Gravitational Waves from Rotating Core Collapse
• Efficient estimation method for time evolution of proto-neutron star mass and radius from supernova neutrino signal
• Classification of the core-collapse supernova explosion mechanism with learned dictionaries
• Reconstructing Gravitational Wave Core-Collapse Supernova Signals with Dynamic Time Warping
• Using supervised learning algorithms as a follow-up method in the search of gravitational waves from core-collapse supernovae
• Inferring physical properties of stellar collapse by third-generation gravitational-wave detectors

Cited by in corpus (7)

• The Science of the Einstein Telescope
• Applications of Machine Learning to Detecting Fast Neutrino Flavor Instabilities in Core-Collapse Supernova and Neutron Star Merger Models
• Probing nuclear physics with supernova gravitational waves and machine learning
• Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification
• Enhancing Cosmological Model Selection with Interpretable Machine Learning
• Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae
• Assessing the Distance for Probing the Nuclear Equation of State with Supernova Gravitational Waves