Testing the robustness of simulation-based gravitational-wave population inference

Abstract

Gravitational-wave population studies have become more important in gravitational-wave astronomy because of the rapid growth of the observed catalog. In recent studies, emulators based on different machine learning techniques are used to emulate the outcomes of the population synthesis simulation with fast speed. In this study, we benchmark the performance of two emulators that learn the truncated power-law phenomenological model by using Gaussian process regression and normalizing flows techniques to see which one is a more capable likelihood emulator in the population inference. We benchmark the characteristic of the emulators by comparing their performance in the population inference to the phenomenological model using mock and real observation data. Our results suggest that the normalizing flows emulator can recover the posterior distribution by using the phenomenological model in the population inference with up to 300 mock injections. The normalizing flows emulator also underestimates the uncertainty for some posterior distributions in the population inference on real observation data. On the other hand, the Gaussian process regression emulator has poor performance on the same task and can only be used effectively in low-dimension cases.

13 pages, 7 figures

References in corpus (14)

• The NumPy array: a structure for efficient numerical computation
• Generative Adversarial Networks
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• Advanced LIGO
• Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
• Double Compact Objects III: Gravitational Wave Detection Rates
• Merging black hole binaries: the effects of progenitor's metallicity, mass-loss rate and Eddington factor
• Inferring the eccentricity distribution
• Accuracy Requirements for Empirically-Measured Selection Functions
• A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful
• Machine-learning interpolation of population-synthesis simulations to interpret gravitational-wave observations: a case study
• Observational Selection Effects with Ground-based Gravitational Wave Detectors
• Astrophysical Prior Information and Gravitational-wave Parameter Estimation
• Jacobian Determinant of Normalizing Flows

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• Isolated pulsar population synthesis with simulation-based inference
• Surrogate forward models for population inference on compact binary mergers
• Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Tests
• Quantifying Weighted Morphological Content of Large-Scale Structures via Simulation-Based Inference