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most citedCosmology and the Bispectrum

270 citations · 4.8k across the 154 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

astro-ph.CO2021★ 39 cited

The Covariance of Squeezed Bispectrum Configurations

Matteo Biagetti, Lina Castiblanco, Jorge Noreña +1

We measure the halo bispectrum covariance in a large set of N-body simulations and compare it with theoretical expectations. We find a large correlation among (even mildly) squeeze…

astro-ph.GA2021★ 80 cited

Euclid preparation: XVIII. Cosmic Dawn Survey. Spitzer observations of the Euclid deep fields and calibration fields

Andrea Moneti, H. J. McCracken, M. Shuntov +194

We present a new infrared survey covering the three Euclid deep fields and four other Euclid calibration fields using Spitzer's Infrared Array Camera (IRAC). We have combined these…

astro-ph.CO2021★ 56 cited

Euclid preparation: XIX. Impact of magnification on photometric galaxy clustering

F. Lepori, I. Tutusaus, C. Viglione +183

We investigate the importance of lensing magnification for estimates of galaxy clustering and its cross-correlation with shear for the photometric sample of Euclid. Using updated s…

astro-ph.CO2021★ 59 cited

Cosmological parameters from the likelihood analysis of the galaxy power spectrum and bispectrum in real space

Andrea Oddo, Federico Rizzo, Emiliano Sefusatti +2

We present a joint likelihood analysis of the halo power spectrum and bispectrum in real space. We take advantage of a large set of numerical simulations and of an even larger set…

astro-ph.CO2021★ 2 cited

The reach of next-to-leading-order perturbation theory for the matter bispectrum

Davit Alkhanishvili, Cristiano Porciani, Emiliano Sefusatti +4

We provide a comparison between the matter bispectrum derived with different flavours of perturbation theory at next-to-leading order and measurements from an unprecedentedly large…

astro-ph.GA2021★ 32 cited

Euclid preparation: XIII. Forecasts for galaxy morphology with the Euclid Survey using Deep Generative Models

Euclid Collaboration, H. Bretonnière, M. Huertas-Company +193

We present a machine learning framework to simulate realistic galaxies for the Euclid Survey. The proposed method combines a control on galaxy shape parameters offered by analytic…