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From the 1 of 10 linked papers with an AI index.

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10 papers

gr-qc2026

Advanced Virgo during the LIGO-Virgo-KAGRA fourth observing run

Virgo Collaboration, F Acernese, A Agapito +546

The paper reports on Advanced Virgo's participation in the fourth observing run (O4) of the global gravitational‑wave detector network, describing the commissioning of its new sign…

gr-qc2026

A story about a tipsy kangaroo: Reversible jump MCMC for model selection in the analysis of gravitational-wave signals from the coalescence of compact objects

Anna Puecher, Tim Dietrich, Hauke Koehn +4

Bayesian inference is commonly employed in the analysis of gravitational-wave signals not only to estimate the source parameters, but also for model selection. The latter provides…

gr-qc2026

Impact of the Einstein Telescope's duty cycle on the estimation of binary black holes parameters

Luca Negri, Thomas C. K. Ng, Thibeau Wouters +13

The geometry of the Einstein Telescope, the proposed next-generation European gravitational-wave observatory, is yet to be finalized. Two competing designs are under consideration:…

astro-ph.IM2026

Advanced Virgo Plus for O5 -- Design Report Overview

F. Acernese, A. Agapito, D. Agarwal +578

This document presents an overview of the design, implementation, and expected performance of the Advanced Virgo Plus (AdV+) upgrades in view of the O5 observing run. Following the…

gr-qc2025

Overlapping signals in next-generation gravitational wave observatories: A recipe for selecting the best parameter estimation technique

Tomasz Baka, Harsh Narola, Justin Janquart +3

Third-generation gravitational wave detectors such as Einstein Telescope and Cosmic Explorer will have significantly better sensitivities than current detectors, as well as a wider…

astro-ph.HE2025

Neural likelihood estimators for flexible gravitational wave data analysis

Luca Negri, Anuradha Samajdar

In this paper, we develop a Neural Likelihood Estimator and apply it to analyse real gravitational-wave (GW) data for the first time. We assess the usability of neural likelihood f…