REVIEW 6 cited by
Growing Pains: Understanding the Impact of Likelihood Uncertainty on Hierarchical Bayesian Inference for Gravitational-Wave Astronomy
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Observations of gravitational waves emitted by merging compact binaries have provided tantalising hints about stellar astrophysics, cosmology, and fundamental physics. However, the physical parameters describing the systems, (mass, spin, distance) used to extract these inferences about the Universe are subject to large uncertainties. The most widely-used method of performing these analyses requires performing many Monte Carlo integrals to marginalise over the uncertainty in the properties of the individual binaries and the survey selection bias. These Monte Carlo integrals are subject to fundamental statistical uncertainties. Previous treatments of this statistical uncertainty has focused on ensuring the precision of the inferred inference is unaffected, however, these works have neglected the question of whether sufficient accuracy can also be achieved. In this work, we provide a practical exploration of the impact of uncertainty in our analyses and provide a suggested framework for verifying that astrophysical inferences made with the gravitational-wave transient catalogue are accurate. Applying our framework to models used by the LIGO-Virgo-KAGRA collaboration and in the wider literature, we find that Monte Carlo uncertainty in estimating the survey selection bias is the limiting factor in our ability to probe narrow population models and this will rapidly grow more problematic as the size of the observed population increases.
Forward citations
Cited by 6 Pith papers
-
When (not) to trust Monte Carlo approximations for hierarchical Bayesian inference
A unified error statistic E-hat measures information lost to Monte Carlo noise in hierarchical Bayesian inference, with a recommended cutoff of 0.2 bits.
-
Fortifying gravitational-wave population inference with normalizing flows
Representing each gravitational-wave event's posterior with a normalizing flow lets analysts generate enough cheap posterior samples to keep the Monte-Carlo variance of population inference below threshold for catalog...
-
Constraints on primordial black holes from the first part of LIGO-Virgo-KAGRA fourth observing run
O4a gravitational-wave data give 95% CL upper limits f_PBH ~ 1e-2 to 1e-4 for monochromatic primordial black holes with mean masses 0.6-100 M_sun, with no evidence of a PBH merger component.
-
Gravitational-wave astronomy requires population-informed parameter estimation
Single-event GW parameter estimates under reference priors are population-biased; hierarchical, population-informed re-analysis is needed and changes the identification of the most extreme black holes in the catalog.
-
The Long Road to Alignment: Measuring Black Hole Spin Orientation with Expanding Gravitational-Wave Datasets
Simulated gravitational-wave catalogs show spin-tilt peaks at alignment are hard to confirm even with 1500 events, while integrated tilt fractions are robust.
-
The first decade of gravitational-wave measurements of black hole spins
A review summarizing formation-channel predictions, waveform effects, and population-level constraints on stellar-mass black hole spins from the first decade of gravitational-wave observations.
Discussion (0). Continue with ORCID to comment.