From the 1 of 117 linked papers with an AI index.
8 citations · 21 across the 57 of their papers we have counts for
62 papers · 1 filter
Cross-Correlating the Universe: The Gravitational Wave Background and Large-Scale Structure
Federico Semenzato, J. Andrew Casey-Clyde, Chiara M. F. Mingarelli +4
The paper proposes using cross‑correlations between gravitational‑wave background anisotropy maps and galaxy surveys to detect the imprint of unresolved supermassive black‑hole bin…
Exploring Gravitational Wave Signatures Due to Primordial Non-gaussianity and Large Scale Structure Using SKAO
H. V. Ragavendra, Giulia Capurri, Alina Mierna +8
This chapter explores theoretical and observational strategies to use the stochastic gravitational-wave background detectable by Square Kilometre Array Observatory (SKAO) as a prob…
Euclid preparation. First investigation of the impact of cross-contamination on spectroscopic redshift measurements with pixel-level simulations
Euclid Collaboration, F. Passalacqua, S. Anselmi +299
We present a study on simulated data focused on understanding the performance of the spectroscopic redshift measurements with the Near-Infrared Spectrometer and Photometer (NISP) i…
Euclid. Populating a dark universe with galaxies using SciPIC
Euclid Collaboration, E. J. Gonzalez, J. Carretero +305
High-fidelity galaxy mocks are crucial for validating analysis pipelines and for cosmological inference. In this context, the Science Pipeline at PIC (SciPIC) is a pipeline specifi…
Euclid preparation. CIV. Impact of galaxy intrinsic alignment modelling choices on Euclid 3x2pt cosmology
Euclid Collaboration, D. Navarro-Gironés, I. Tutusaus +274
The Euclid galaxy survey will provide unprecedented constraints on cosmology, but achieving unbiased results will require an optimal characterisation and mitigation of systematic e…
Euclid preparation. CII. Non-Gaussianity of 2-pt statistics likelihood: Parameter inference with a non-Gaussian likelihood in Fourier and configuration space
Euclid Collaboration, S. Gouyou Beauchamps, J. Bel +277
In this work we account for this skewness in parameter inference by modelling the likelihood through an Edgeworth expansion which involves the complete skewness tensor, composed of…