Hierarchical Bayesian inference on GWTC-5.0 constrains the memory enhancement factor to 0.26 with large uncertainties consistent with the GR value of 1 and forecasts that 2000 detections are needed for a 1σ constraint away from zero.
Cauchy-characteristic matching
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
This paper gives a detailed pedagogic presentation of the central concepts underlying a new algorithm for the numerical solution of Einstein's equations for gravitation. This approach incorporates the best features of the two leading approaches to computational gravitation, carving up spacetime via Cauchy hypersurfaces within a central worldtube, and using characteristic hypersurfaces in its exterior to connect this region with null infinity and study gravitational radiation. It has worked well in simplified test problems, and is currently being used to build computer codes to simulate black hole collisions in 3-D.
fields
gr-qc 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Constraining Gravitational Wave Memory with Hierarchical Inference
Hierarchical Bayesian inference on GWTC-5.0 constrains the memory enhancement factor to 0.26 with large uncertainties consistent with the GR value of 1 and forecasts that 2000 detections are needed for a 1σ constraint away from zero.