12 papers
Revealing massive black hole astrophysics: The potential of hierarchical inference with extreme mass-ratio inspiral observations
Shashwat Singh, Christian E. A. Chapman-Bird, Christopher P. L. Berry +1
The paper investigates how observations of extreme mass-ratio inspirals by LISA can be used with hierarchical Bayesian inference to measure massive black hole population properties…
Inference with finite time series II: the window strikes back
Colm Talbot, Sylvia Biscoveanu, Aaron Zimmerman +9
Smooth window functions are often applied to strain data when inferring the parameters describing the astrophysical sources of gravitational-wave transients. Within the LIGO-Virgo-…
GW231123: Overlapping Gravitational Wave Signals?
Qian Hu, Harsh Narola, Jef Heynen +4
The recently discovered gravitational wave event GW231123 was interpreted as the merger of two black holes with a total mass of 190-265 , making it the heaviest such merge…
Constraints on the extreme mass-ratio inspiral population from LISA data
Shashwat Singh, Christian E. A. Chapman-Bird, Christopher P L Berry +1
Gravitational waves from extreme mass-ratio inspirals (EMRIs), the inspirals of stellar-mass compact objects into massive black holes, are predicted to be observed by the Laser Int…
Neural Bayesian updates to populations with growing gravitational-wave catalogs
Noah E. Wolfe, Matthew Mould, John Veitch +1
As gravitational-wave catalogs grow, they will become increasingly computationally expensive to analyze in their entirety, especially when inferring astrophysical source population…
Costs of Bayesian Parameter Estimation in Third-Generation Gravitational Wave Detectors: an Assessment of Current Acceleration Methods
Qian Hu, John Veitch
Bayesian inference with stochastic sampling has been widely used to obtain the properties of gravitational wave (GW) sources. Although computationally intensive, its cost remains m…