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.

arXiv comment13 pages, 7 figures

References in corpus (48)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Auto-Encoding Variational Bayes
• The NumPy array: a structure for efficient numerical computation
• Generative Adversarial Networks
• Advanced Virgo: a 2nd generation interferometric gravitational wave detector
• 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
• GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
• Evolution of binary stars and the effect of tides on binary populations
• API design for machine learning software: experiences from the scikit-learn project
• Science Case for the Einstein Telescope
• Bilby: A user-friendly Bayesian inference library for gravitational-wave astronomy
• Robust parameter estimation for compact binaries with ground-based gravitational-wave observations using the LALInference software library
• Compact Object Modeling with the StarTrack Population Synthesis Code
• KAGRA: 2.5 Generation Interferometric Gravitational Wave Detector
• GW150914: First results from the search for binary black hole coalescence with Advanced LIGO
• The Sensitivity of the Advanced LIGO Detectors at the Beginning of Gravitational Wave Astronomy
• An introduction to Bayesian inference in gravitational-wave astronomy: parameter estimation, model selection, and hierarchical models
• Double Compact Objects III: Gravitational Wave Detection Rates
• Extracting distribution parameters from multiple uncertain observations with selection biases
• The progenitors of compact-object binaries: impact of metallicity, common envelope and natal kicks
• The effect of metallicity on the detection prospects for gravitational waves
• Merging black hole binaries: the effects of progenitor's metallicity, mass-loss rate and Eddington factor
• COSMIC Variance in Binary Population Synthesis
• Measuring the binary black hole mass spectrum with an astrophysically motivated parameterization
• Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
• Inferring the eccentricity distribution
• Going the Distance: Mapping Host Galaxies of LIGO and Virgo Sources in Three Dimensions Using Local Cosmography and Targeted Follow-up
• Reconstructing phenomenological distributions of compact binaries via gravitational wave observations
• A Survey of Constrained Gaussian Process Regression: Approaches and Implementation Challenges
• MOCCA Code for Star Cluster Simulations - II. Comparison with N-body Simulations
• Distance measures in gravitational-wave astrophysics and cosmology
• Probing Fundamental Physics with Gravitational Waves: The Next Generation
• Accuracy Requirements for Empirically-Measured Selection Functions
• Explaining LIGO's observations via isolated binary evolution with natal kicks
• Mining Gravitational-wave Catalogs To Understand Binary Stellar Evolution: A New Hierarchical Bayesian Framework
• Impact of Bayesian prior on the characterization of binary black hole coalescences
• Populations of stellar mass Black holes from binary systems
• The power of Bayesian evidence in astronomy
• Gravitational wave population inference with deep flow-based generative network
• Impacts of overlapping gravitational-wave signals on the parameter estimation: Toward the search for cosmological backgrounds
• 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
• Exploring the Parameter Space of Compact Binary Population Synthesis
• Estimates of the coverage of parameter space by Latin Hypercube and Orthogonal sampling: connections between Populations of Models and Experimental Designs
• Jacobian Determinant of Normalizing Flows

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