Gravitational wave population inference with deep flow-based generative network

Abstract

We combine hierarchical Bayesian modeling with a flow-based deep generative network, in order to demonstrate that one can efficiently constraint numerical gravitational wave (GW) population models at a previously intractable complexity. Existing techniques for comparing data to simulation,such as discrete model selection and Gaussian process regression, can only be applied efficiently to moderate-dimension data. This limits the number of observable (e.g. chirp mass, spins.) and hyper-parameters (e.g. common envelope efficiency) one can use in a population inference. In this study, we train a network to emulate a phenomenological model with 6 observables and 4 hyper-parameters, use it to infer the properties of a simulated catalogue and compare the results to the phenomenological model. We find that a 10-layer network can emulate the phenomenological model accurately and efficiently. Our machine enables simulation-based GW population inferences to take on data at a new complexity level.

11 pages, 3 figures

References in corpus (10)

• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
• Use and Abuse of the Fisher Information Matrix in the Assessment of Gravitational-Wave Parameter-Estimation Prospects
• Double Compact Objects III: Gravitational Wave Detection Rates
• Merging black hole binaries: the effects of progenitor's metallicity, mass-loss rate and Eddington factor
• Distinguishing Spin-Aligned and Isotropic Black Hole Populations With Gravitational Waves
• Inferring the eccentricity distribution
• Where are LIGO's Big Black Holes?
• Machine-learning interpolation of population-synthesis simulations to interpret gravitational-wave observations: a case study
• Astrophysical Prior Information and Gravitational-wave Parameter Estimation

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• Gravitational-wave surrogate models powered by artificial neural networks: The ANN-Sur for waveform generation
• Deep learning and Bayesian inference of gravitational-wave populations: Hierarchical black-hole mergers
• Accelerated Bayesian SED Modeling using Amortized Neural Posterior Estimation
• Dynamical mass inference of galaxy clusters with neural flows
• $\mathrm{S}\mathrm{IM}\mathrm{BIG}$: Mock Challenge for a Forward Modeling Approach to Galaxy Clustering
• Gravitational wave populations and cosmology with neural posterior estimation
• Distinguishing double neutron star from neutron star-black hole binary populations with gravitational wave observations
• Applications of machine learning in gravitational wave research with current interferometric detectors
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• PopSED: Population-Level Inference for Galaxy Properties from Broadband Photometry with Neural Density Estimation
• The effect of spin mismodeling on gravitational-wave measurements of the binary neutron star mass distribution
• Flow-Based Likelihoods for Non-Gaussian Inference
• Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data
• Testing the robustness of simulation-based gravitational-wave population inference
• Unifying supervised learning and VAEs -- coverage, systematics and goodness-of-fit in normalizing-flow based neural network models for astro-particle reconstructions
• Single-event likelihood of star cluster properties with LIGO-Virgo-Kagra binary black hole observations
• Which black hole formed first? Mass-ratio reversal in massive binary stars from gravitational-wave data