REVIEW 2 cited by
Detection is truncation: studying source populations with truncated marginal neural ratio estimation
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
Signed reviews
read the original abstract
Statistical inference of population parameters of astrophysical sources is challenging. It requires accounting for selection effects, which stem from the artificial separation between bright detected and dim undetected sources that is introduced by the analysis pipeline itself. We show that these effects can be modeled self-consistently in the context of sequential simulation-based inference. Our approach couples source detection and catalog-based inference in a principled framework that derives from the truncated marginal neural ratio estimation (TMNRE) algorithm. It relies on the realization that detection can be interpreted as prior truncation. We outline the algorithm, and show first promising results.
Forward citations
Cited by 2 Pith papers
-
Sequential simulation-based inference for extreme mass ratio inspirals
Sequential simulation-based inference with truncated marginal neural ratio estimation shrinks the 11-parameter search volume for simulated non-spinning extreme-mass-ratio inspirals by factors of 1e6 to 1e7 and recover...
-
Learning Optimal and Interpretable Summary Statistics of Galaxy Catalogs with SBI
Jointly training a graph neural network with a normalizing flow yields low-dimensional summary statistics from simulated galaxy catalogs that support likelihood-free inference of Omega_m, and can be interpreted via co...
Discussion (0). Continue with ORCID to comment.