collaborators

8 papers

gr-qc2026

GW230814: investigation of a loud gravitational-wave signal observed with a single detector

The LIGO Scientific Collaboration, The Virgo Collaboration, The Kagra Collaboration +1783

GW230814, detected by the LIGO Livingston observatory with a signal-to-noise ratio of 42.4, represents the loudest gravitational-wave signal in the GWTC-4.0 catalog. Its source is…

gr-qc2026

The Sequential Monte Carlo goes NUTS: Boosting Gravitational-Wave Inference

Gabriele Demasi, Giulia Capurri, Massimo Lenti +9

Sequential Monte Carlo (SMC) methods have recently been applied to gravitational-wave inference as a powerful alternative to standard sampling techniques, such as Nested Sampling.…

stat.CO2025

samsara: A Continuous-Time Markov Chain Monte Carlo Sampler for Trans-Dimensional Bayesian Analysis

Gabriele Astorino, Lorenzo Valbusa Dall'Armi, Riccardo Buscicchio +3

Bayesian inference requires determining the posterior distribution, a task that becomes particularly challenging when the dimension of the parameter space is large and unknown. Thi…

astro-ph.HE2025

GW241011 and GW241110: Exploring Binary Formation and Fundamental Physics with Asymmetric, High-Spin Black Hole Coalescence

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration +1783

We report the observation of gravitational waves from two binary black hole coalescences during the fourth observing run of the LIGO--Virgo--KAGRA detector network, GW241011 and GW…

gr-qc2025

Black Hole Spectroscopy and Tests of General Relativity with GW250114

The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration

The binary black hole signal GW250114, the loudest gravitational wave detected to date, offers a unique opportunity to test Einstein's general relativity (GR) in the high-velocity,…

gr-qc2025

Likelihood for a Network of Gravitational-Wave Detectors with Correlated Noise

Francesco Cireddu, Milan Wils, Isaac C. F. Wong +3

The Einstein Telescope faces a critical data analysis challenge with correlated noise, often overlooked in current parameter estimation analyses. We address this issue by presentin…