English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Detecting stochastic gravitational wave backgrounds (SGWBs) with The LaserInterferometer Space Antenna (LISA) is among the mission science objectives.Disentangling SGWBs of astrophysical and cosmological origin is a challengingtask, further complicated by the noise level uncertainties. In this study, weintroduce a Bayesian methodology to infer upon SGWBs, taking inspiration fromGaussian stochastic processes. We investigate the suitability of the approachfor signal of unknown spectral shape. We do by discretely exploring the modelhyperparameters, a first step towards a more efficient transdimensionalexploration. We apply the proposed method to a representative astrophysicalscenario: the inference on the astrophysical foreground of Extreme Mass RatioInspirals, recently estimated in~\cite{Pozzoli2023}. We find the algorithm tobe capable of recovering the injected signal even with large priors, whilesimultaneously providing estimate of the noise level.

A weakly-parametric approach to stochastic background inference in LISA