From the 1 of 104 linked papers with an AI index.
2 citations · 10 across the 37 of their papers we have counts for
52 papers · 1 filter
Cosmological constraining power of the redshifts, heights, and angular clustering of weak gravitational lensing peaks
Jeger C. Broxterman, Matthieu Schaller, Ian G. McCarthy +8
Weak gravitational lensing (WL) peaks probe non-Gaussian information of the large-scale distribution of matter that is not captured by two-point lensing statistics. We study the co…
On combining estimated and analytic covariance matrices
Alan Heavens, Lorne Whiteway, Elena Sellentin
The paper derives an accurate multivariate Student‑t approximation for likelihoods that combine covariance matrices estimated from simulations with additional Gaussian error contri…
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…