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We use a suite of high-resolution $N$-body simulations and state-of-the-art perturbation theory to improve the code halofit, which predicts the nonlinear matter power spectrum. We restrict attention to parameters in the vicinity of the…

宇宙学与河外天体物理 · 物理学 2019-04-09 Robert E. Smith , Raul E. Angulo

Weak lensing mass-mapping is a useful tool to access the full distribution of dark matter on the sky, but because of intrinsic galaxy ellipticies and finite fields/missing data, the recovery of dark matter maps constitutes a challenging…

宇宙学与河外天体物理 · 物理学 2023-04-05 Benjamin Remy , Francois Lanusse , Niall Jeffrey , Jia Liu , Jean-Luc Starck , Ken Osato , Tim Schrabback

We investigate 3D density and weak lensing profiles of dark matter haloes predicted by a cosmology-rescaling algorithm for $N$-body simulations. We extend the rescaling method of Angulo & White (2010) and Angulo & Hilbert (2015) to improve…

宇宙学与河外天体物理 · 物理学 2018-07-04 Malin Renneby , Stefan Hilbert , Raúl E. Angulo

We investigate cosmological parameter inference from realistic simulated weak lensing image data using only galaxy position angles, as opposed to full-ellipticity information. We demonstrate that input shear fields can be accurately…

宇宙学与河外天体物理 · 物理学 2019-10-30 Lee Whittaker

In this paper we present results from the weak lensing shape measurement GRavitational lEnsing Accuracy Testing 2010 (GREAT10) Galaxy Challenge. This marks an order of magnitude step change in the level of scrutiny employed in weak lensing…

In the era of precision cosmology, establishing the correct magnitude of statistical errors in cosmological parameters is of crucial importance. However, widely used approximations in galaxy surveys analyses can lead to parameter…

宇宙学与河外天体物理 · 物理学 2020-10-07 Nicola Bellomo , José Luis Bernal , Giulio Scelfo , Alvise Raccanelli , Licia Verde

Rapid progress in cosmological Large Scale Structure (LSS) surveys motivates precise theoretical predictions. The Effective Field Theory of Large-Scale Structure (EFTofLSS) is routinely applied to data, and requires fast computation of its…

宇宙学与河外天体物理 · 物理学 2026-02-26 Charalampos Anastasiou , Andrea Favorito , Matthew Lewandowski , Leonardo Senatore , Henry Zheng

We explore the sensitivity of limb darkening coefficients computed from stellar atmosphere models to different least-squares fitting methods. We demonstrate that conventional methods are strongly biased to fitting the stellar limb. Our…

天体物理学 · 物理学 2008-11-26 David Heyrovsky

We study the projected clustering of photometric luminous red galaxies from the DESI Legacy Survey, combining their angular power spectrum, bispectrum, and cross-correlation with maps of the CMB lensing convergence from the Planck…

宇宙学与河外天体物理 · 物理学 2025-10-21 Francesco Verdiani , Lea Harscouet , Matteo Zennaro , David Alonso , Boryana Hadzhiyska

Accurate cosmological simulations that include the effect of non-linear matter clustering as well as of massive neutrinos are essential for measuring the neutrino mass scale from upcoming galaxy surveys. Typically, Newtonian simulations are…

宇宙学与河外天体物理 · 物理学 2020-09-24 Christian Partmann , Christian Fidler , Cornelius Rampf , Oliver Hahn

The current generation of large galaxy surveys will test the cosmological model by combining multiple types of observational probes. Realising the statistical promise of these new datasets requires rigorous attention to all aspects of…

We present a general framework for obtaining robust bounds on the nature of dark matter using cosmological $N$-body simulations and Lyman-alpha forest data. We construct an emulator of hydrodynamical simulations, which is a flexible,…

宇宙学与河外天体物理 · 物理学 2021-02-23 Keir K. Rogers , Hiranya V. Peiris

We present a new efficient method to compute the angular power spectra of large-scale structure observables that circumvents the numerical integration over Bessel functions, expanding on a recently proposed algorithm based on FFTlog. This…

宇宙学与河外天体物理 · 物理学 2018-10-31 Nils Schöneberg , Marko Simonović , Julien Lesgourgues , Matias Zaldarriaga

We derive and implement a full Bayesian large scale structure inference method aiming at precision recovery of the cosmological power spectrum from galaxy redshift surveys. Our approach improves over previous Bayesian methods by performing…

宇宙学与河外天体物理 · 物理学 2015-06-16 Jens Jasche , Benjamin D. Wandelt

We present a novel framework for jointly modelling the weak lensing source galaxy redshift distribution and the intrinsic alignment of galaxies via a shared luminosity function (LF). Considering this framework within the context of a Rubin…

宇宙学与河外天体物理 · 物理学 2025-02-13 Nikolina Šarčević , C. Danielle Leonard , Markus M. Rau , LSST Dark Energy Science Collaboration

Conventional algorithms for galaxy power spectrum estimation measure the true spectrum convolved with a survey window function, which, for parameter inference, must be compared with a similarly convolved theory model. In this work, we…

宇宙学与河外天体物理 · 物理学 2021-05-12 Oliver H. E. Philcox

The last few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical analysis of galaxy clustering data beyond the linear regime…

We present a novel method for reconstructing weak lensing mass or convergence maps as a probe to study non-Gaussianities in the cosmic density field. While previous surveys have relied on a flat-sky approximation, the forthcoming stage IV…

宇宙学与河外天体物理 · 物理学 2023-02-03 Vanshika Kansal

Context. Strong lensing mass measurements require the knowledge of the redshift of both the lens and the source galaxy. Traditionally, spectroscopic redshifts are used for this purpose. Upcoming surveys, however, will lead to the discovery…

星系天体物理 · 物理学 2022-04-15 Alessandro Sonnenfeld

A central challenge in precision cosmology with galaxy surveys is to extract non-Gaussian information from large-scale structure while controlling systematic uncertainties such as tracer bias. Conventional clustering statistics, such as the…

宇宙学与河外天体物理 · 物理学 2026-05-27 Zhujun Jiang , Xu Xiao , Fenfen Yin , Xiao-Dong Li , Le Zhang