Genetic-algorithm-optimized neural networks for gravitational wave classification

Abstract

Gravitational-wave detection strategies are based on a signal analysis technique known as matched filtering. Despite the success of matched filtering, due to its computational cost, there has been recent interest in developing deep convolutional neural networks (CNNs) for signal detection. Designing these networks remains a challenge as most procedures adopt a trial and error strategy to set the hyperparameter values. We propose a new method for hyperparameter optimization based on genetic algorithms (GAs). We compare six different GA variants and explore different choices for the GA-optimized fitness score. We show that the GA can discover high-quality architectures when the initial hyperparameter seed values are far from a good solution as well as refining already good networks. For example, when starting from the architecture proposed by George and Huerta, the network optimized over the 20-dimensional hyperparameter space has 78% fewer trainable parameters while obtaining an 11% increase in accuracy for our test problem. Using genetic algorithm optimization to refine an existing network should be especially useful if the problem context (e.g. statistical properties of the noise, signal model, etc) changes and one needs to rebuild a network. In all of our experiments, we find the GA discovers significantly less complicated networks as compared to the seed network, suggesting it can be used to prune wasteful network structures. While we have restricted our attention to CNN classifiers, our GA hyperparameter optimization strategy can be applied within other machine learning settings.

arXiv comment25 pages, 8 figures, and 2 tables; Version 2 includes an expanded discussion of our hyperparameter optimization model

References in corpus (51)

• Adam: A Method for Stochastic Optimization
• Observation of Gravitational Waves from a Binary Black Hole Merger
• GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
• GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs
• Advanced LIGO
• GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence
• Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour
• GW170104: Observation of a 50-Solar-Mass Binary Black Hole Coalescence at Redshift 0.2
• GW170814: A Three-Detector Observation of Gravitational Waves from a Binary Black Hole Coalescence
• Gravitational Waves from Mergin Compact Binaries: How Accurately Can One Extract the Binary's Parameters from the Inspiral Waveform?
• GW170608: Observation of a 19-solar-mass Binary Black Hole Coalescence
• Binary Black Hole Population Properties Inferred from the First and Second Observing Runs of Advanced LIGO and Advanced Virgo
• Astrophysical Implications of the Binary Black-Hole Merger GW150914
• Detection, Measurement and Gravitational Radiation
• FINDCHIRP: an algorithm for detection of gravitational waves from inspiraling compact binaries
• Don't Decay the Learning Rate, Increase the Batch Size
• Search templates for gravitational waves from inspiraling binaries: Choice of template spacing
• Detection methods for stochastic gravitational-wave backgrounds: a unified treatment
• Method for detection and reconstruction of gravitational wave transients with networks of advanced detectors
• Analysis Framework for the Prompt Discovery of Compact Binary Mergers in Gravitational-wave Data
• Train longer, generalize better: closing the generalization gap in large batch training of neural networks
• A chi-squared time-frequency discriminator for gravitational wave detection
• Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
• Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• Matching matched filtering with deep networks in gravitational-wave astronomy
• Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
• Fast and accurate prediction of numerical relativity waveforms from binary black hole coalescences using surrogate models
• Low-latency analysis pipeline for compact binary coalescences in the advanced gravitational wave detector era
• Deep Transfer Learning: A new deep learning glitch classification method for advanced LIGO
• Glitch Classification and Clustering for LIGO with Deep Transfer Learning
• Gravitational-wave parameter estimation with autoregressive neural network flows
• PyCBC Live: Rapid Detection of Gravitational Waves from Compact Binary Mergers
• Searching for Gravitational Waves from Compact Binaries with Precessing Spins
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• Learning Bayesian posteriors with neural networks for gravitational-wave inference
• Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing
• Rademacher Complexity for Adversarially Robust Generalization
• Real-Time Detection of Gravitational Waves from Binary Neutron Stars using Artificial Neural Networks
• Gravitational Wave Denoising of Binary Black Hole Mergers with Deep Learning
• Gravitational-Wave Data Analysis. Formalism and Sample Applications: The Gaussian Case
• Detection of gravitational-wave signals from binary neutron star mergers using machine learning
• Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors
• Detection of Gravitational Waves Using Bayesian Neural Networks
• Binary Neutron Stars Gravitational Wave Detection Based on Wavelet Packet Analysis And Convolutional Neural Networks
• Physics-inspired deep learning to characterize the signal manifold of quasi-circular, spinning, non-precessing binary black hole mergers
• A Convolutional Neural Network For Cosmic String Detection in CMB Temperature Maps
• Particle Swarm Optimization based search for gravitational waves from compact binary coalescences: performance improvements
• Searching for Gravitational Radiation from Binary Black Hole MACHOs in the Galactic Halo
• Detection of gravitational waves using topological data analysis and convolutional neural network: An improved approach
• Towards understanding feedback from supermassive black holes using convolutional neural networks

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• Interpretable AI forecasting for numerical relativity waveforms of quasi-circular, spinning, non-precessing binary black hole mergers