Classifying the Equation of State from Rotating Core Collapse Gravitational Waves with Deep Learning

Abstract

In this paper, we seek to answer the question "given a rotating core collapse gravitational wave signal, can we determine its nuclear equation of state?". To answer this question, we employ deep convolutional neural networks to learn visual and temporal patterns embedded within rotating core collapse gravitational wave (GW) signals in order to predict the nuclear equation of state (EOS). Using the 1824 rotating core collapse GW simulations by Richers et al. (2017), which has 18 different nuclear EOS, we consider this to be a classic multi-class image classification and sequence classification problem. We attain up to 72\% correct classifications in the test set, and if we consider the "top 5" most probable labels, this increases to up to 97\%, demonstrating that there is a moderate and measurable dependence of the rotating core collapse GW signal on the nuclear EOS.

10 pages, 5 figures

References in corpus (9)

• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Advanced LIGO
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• The gravitational wave burst signal from core collapse of rotating stars
• Correlated Signatures of Gravitational-Wave and Neutrino Emission in Three-Dimensional General-Relativistic Core-Collapse Supernova Simulations
• Inferring the core-collapse supernova explosion mechanism with gravitational waves
• A New Method to Observe Gravitational Waves emitted by Core Collapse Supernovae
• Inferring the core-collapse supernova explosion mechanism with three-dimensional gravitational-wave simulations
• Bayesian parameter estimation of core collapse supernovae using gravitational wave simulations

Cited by in corpus (17)

• The Science of the Einstein Telescope
• Parameter estimation with gravitational waves
• Deep learning for multimessenger core-collapse supernova detection
• Three dimensional magnetorotational core-collapse supernova explosions of a 39 solar mass progenitor star
• Extraction of Binary Black Hole Gravitational Wave Signals from Detector Data Using Deep Learning
• Inferring Astrophysical Parameters of Core-Collapse Supernovae from their Gravitational-Wave Emission
• 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
• A novel stacked hybrid autoencoder for imputing LISA data gaps
• Deep-Learning Classification and Parameter Inference of Rotational Core-Collapse Supernovae
• Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification
• Parameter estimation of protoneutron stars from gravitational wave signals using the Hilbert-Huang transform
• Generative adversarial network for stellar core-collapse gravitational waves
• Gravitational Lensing of Core Collapse Supernova Gravitational Wave Signals
• Assessing the Distance for Probing the Nuclear Equation of State with Supernova Gravitational Waves