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A weakly-parametric approach to stochastic background inference in LISA

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arxiv 2311.12111 v1 pith:JVT3JL5S submitted 2023-11-20 astro-ph.CO astro-ph.IMgr-qc

Federico Pozzoli , Riccardo Buscicchio , Christopher J. Moore , Francesco Haardt , Alberto Sesana This is my paper
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keywords astrophysicalsgwbsstochasticapproachinferencelevellisanoise
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Detecting stochastic gravitational wave backgrounds (SGWBs) with The Laser Interferometer Space Antenna (LISA) is among the mission science objectives. Disentangling SGWBs of astrophysical and cosmological origin is a challenging task, further complicated by the noise level uncertainties. In this study, we introduce a Bayesian methodology to infer upon SGWBs, taking inspiration from Gaussian stochastic processes. We investigate the suitability of the approach for signal of unknown spectral shape. We do by discretely exploring the model hyperparameters, a first step towards a more efficient transdimensional exploration. We apply the proposed method to a representative astrophysical scenario: the inference on the astrophysical foreground of Extreme Mass Ratio Inspirals, recently estimated in~\cite{Pozzoli2023}. We find the algorithm to be capable of recovering the injected signal even with large priors, while simultaneously providing estimate of the noise level.

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Cited by 5 Pith papers

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  5. Science of the LISA mission: A Summary for the European Strategy for Particle Physics

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