4 papers
labrador: A domain-optimized machine-learning tool for gravitational wave inference
Javier Roulet, Marco Crisostomi, Lucy M. Thomas +1
Fast and reliable inference of gravitational-wave source parameters is crucial for analyzing large catalogs that are reaching the size of hundreds of detections, and for identifyin…
Impact of sky localization uncertainty on ringdown inference
Kallol Dey, Enrico Barausse, Marco Crisostomi +1
As gravitational-wave ringdown signals grow louder, quasinormal-mode inference depends increasingly on the treatment of extrinsic parameters. Standard analyses fix sky localization…
Beyond diagonal approximations: improved covariance modeling for pulsar timing array data analysis
Marco Crisostomi, Rutger van Haasteren, Patrick M. Meyers +1
Pulsar Timing Array (PTA) searches for nHz gravitational-wave backgrounds (GWBs) typically model time-correlated noise by assuming a diagonal covariance in Fourier space, neglectin…
Bayesian evidence estimation from posterior samples with normalizing flows
Rahul Srinivasan, Marco Crisostomi, Roberto Trotta +2
We propose a novel method (), based on normalizing flows, to estimate the Bayesian evidence (and its numerical uncertainty) from a pre-existing set of samples drawn from the…