Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification

Abstract

We investigate the potential of using gravitational wave (GW) signals from rotating core-collapse supernovae to probe the equation of state (EOS) of nuclear matter. By generating GW signals from simulations with various EOSs, we train machine learning models to classify them and evaluate their performance. Our study builds on previous work by examining how different machine learning models, parameters, and data preprocessing techniques impact classification accuracy. We test convolutional and recurrent neural networks, as well as six classical algorithms: random forest, support vector machines, naïve Bayes, logistic regression, $k$-nearest neighbors, and eXtreme gradient boosting. All models, except naïve Bayes, achieve over 90 per cent accuracy on our dataset. Additionally, we assess the impact of approximating the GW signal using the general relativistic effective potential (GREP) on EOS classification. We find that models trained on GREP data exhibit low classification accuracy. However, normalizing time by the peak signal frequency, which partially compensates for the absence of the time dilation effect in GREP, leads to a notable improvement in accuracy. Despite this, the accuracy does not exceed 70 per cent, suggesting that GREP lacks the precision necessary for EOS classification. Finally, our study has several limitations, including the omission of detector noise and the focus on a single progenitor mass model, which will be addressed in future works.

Accepted for publication in Machine Learning: Science and Technology

References in corpus (74)

• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• The Supernova -- Gamma-Ray Burst Connection
• The Progenitor Stars of Gamma-Ray Bursts
• Observation of gravitational waves from two neutron star-black hole coalescences
• GW150914: The Advanced LIGO Detectors in the Era of First Discoveries
• Black Hole Formation in Failing Core-Collapse Supernovae
• Core-collapse supernova equations of state based on neutron star observations
• Nucleosynthesis and Remnants in Massive Stars of Solar Metallicity
• Stability of Standing Accretion Shocks, With an Eye Toward Core Collapse Supernovae
• Statistical Model for a Complete Supernova Equation of State
• The Proto-Magnetar Model for Gamma-Ray Bursts
• Magnetorotationally driven Supernovae as the origin of early galaxy $r$-process elements?
• The Sensitivity of the Advanced LIGO Detectors at the Beginning of Gravitational Wave Astronomy
• Physics of Core-Collapse Supernovae in Three Dimensions: a Sneak Preview
• Simulations of Magnetically-Driven Supernova and Hypernova Explosions in the Context of Rapid Rotation
• Perspectives on Core-Collapse Supernova Theory
• Radiation hydrodynamics with neutrinos: Variable Eddington factor method for core-collapse supernova simulations
• Supernova Simulations with Boltzmann Neutrino Transport: A Comparison of Methods
• A New Multi-Dimensional General Relativistic Neutrino Hydrodynamics Code of Core-Collapse Supernovae III. Gravitational Wave Signals from Supernova Explosion Models
• New Equations of State in Simulations of Core-Collapse Supernovae
• Magnetorotational Core-Collapse Supernovae in Three Dimensions
• Exploring the relativistic regime with Newtonian hydrodynamics: An improved effective gravitational potential for supernova simulations
• A New Multi-Dimensional General Relativistic Neutrino Hydrodynamics Code for Core-Collapse Supernovae II. Relativistic Explosion Models of Core-Collapse Supernovae
• A Model for Gravitational Wave Emission from Neutrino-Driven Core-Collapse Supernovae
• Observing the Next Galactic Supernova
• An Open-Source Neutrino Radiation Hydrodynamics Code for Core-Collapse Supernovae
• Neutrino-driven convection versus advection in core collapse supernovae
• Characterizing the Gravitational Wave Signal from Core-Collapse Supernovae
• Hydrodynamics of core-collapse supernovae and their progenitors
• Multiple physical elements to determine the gravitational-wave signatures of core-collapse supernovae
• Relativistic simulations of rotational core collapse. I. Methods, initial models, and code tests
• The gravitational wave signal from core-collapse supernovae
• Gravitational Wave Signals from 3D Neutrino Hydrodynamics Simulations of Core-Collapse Supernovae
• The Progenitor Dependence of the Preexplosion Neutrino Emission in Core-Collapse Supernovae
• Observing Gravitational Waves from Core-Collapse Supernovae in the Advanced Detector Era
• Improved constrained scheme for the Einstein equations: An approach to the uniqueness issue
• Equation of State Effects on Gravitational Waves from Rotating Core Collapse
• Magnetorotational core collapse of possible GRB progenitors. I. Explosion mechanisms
• 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
• "Mariage des Maillages": A new numerical approach for 3D relativistic core collapse simulations
• Waveless Approximation Theories of Gravity
• Interplay of Neutrino Opacities in Core-collapse Supernova Simulations
• Two Dimensional Core-Collapse Supernova Explosions Aided by General Relativity with Multidimensional Neutrino Transport
• Magnetorotational Explosion of A Massive Star Supported by Neutrino Heating in General Relativistic Three Dimensional Simulations
• A Second Relativistic Mean Field and Virial Equation of State for Astrophysical Simulations
• Gravitational waves from axisymmetric rotating stellar core collapse to a neutron star in full general relativity
• Gravitational wave signatures in black-hole-forming core collapse
• Correlated Gravitational Wave and Neutrino Signals from General-Relativistic Rapidly Rotating Iron Core Collapse
• Measuring the Angular Momentum Distribution in Core-Collapse Supernova Progenitors with Gravitational Waves
• Equation of state effects in the core collapse of a $20$-$M_{\odot }$ star
• A New Multi-Dimensional General Relativistic Neutrino Hydrodynamics Code for Core-Collapse Supernovae IV. The Neutrino Signal
• Rotating Collapse of Stellar Iron Cores in General Relativity
• Detection and Classification of Supernova Gravitational Waves Signals: A Deep Learning Approach
• A New Method to Observe Gravitational Waves emitted by Core Collapse Supernovae
• Core Collapse Supernova Gravitational Wave Emission for Progenitors of 9.6, 15, and 25 Solar Masses
• Deep learning for multimessenger core-collapse supernova detection
• Chimera: A massively parallel code for core-collapse supernova simulation
• A 3D Simulation of a Neutrino-Driven Supernova Explosion Aided By Convection and Magnetic Fields
• Three-dimensional simulation of a core-collapse supernova for a binary star progenitor of SN 1987A
• Three-dimensional core-collapse supernovae with complex magnetic structures: I. Explosion dynamics
• CFC+: Improved dynamics and gravitational waveforms from relativistic core collapse simulations
• Detection Prospects of Core-Collapse Supernovae with Supernova-Optimized Third-Generation Gravitational-wave Detectors
• 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
• Determining the Structure of Rotating Massive Stellar Cores with Gravitational Waves
• 3D Simulations of Strongly Magnetised Non-Rotating Supernovae: Explosion Dynamics and Remnant Properties
• Circular polarization of gravitational waves from non-rotating supernova cores: a new probe into the pre-explosion hydrodynamics
• Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning
• LSTM and CNN application for core-collapse supernova search in gravitational wave real data
• Classification of the core-collapse supernova explosion mechanism with learned dictionaries
• Exploring Supernova Gravitational Waves with Machine Learning
• Probing nuclear physics with supernova gravitational waves and machine learning
• Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae
• Generative adversarial network for stellar core-collapse gravitational waves

Cited by in corpus (1)

• Assessing the Distance for Probing the Nuclear Equation of State with Supernova Gravitational Waves