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相关论文: Toward an Optimal Sampling of Peculiar Velocity Su…

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We present an alternative, Bayesian method for large-scale reconstruction from observed peculiar velocity data. The method stresses a rigorous treatment of the random errors and it allows extrapolation into poorly sampled regions in real…

天体物理学 · 物理学 2009-10-31 Saleem Zaroubi , Yehuda Hoffman , Avishai Dekel

Reconstructing the large scale density and velocity fields from surveys of galaxy distances, is a major challenge for cosmography. The data is very noisy and sparse. Estimated distances, and thereby peculiar velocities, are strongly…

宇宙学与河外天体物理 · 物理学 2022-12-21 Aurélien Valade , Noam I Libeskind , Yehuda Hoffman , Simon Pfeifer

This paper presents an analysis of the local peculiar velocity field based on the Wiener Filter reconstruction method. We used our currently available catalog of distance measurements containing 1,797 galaxies within 3000 km/s:…

宇宙学与河外天体物理 · 物理学 2015-05-30 Helene M. Courtois , Yehuda Hoffman , R. Brent Tully , Stefan Gottlober

Galaxy distances and derived radial peculiar velocity catalogs constitute valuable datasets to study the dynamics of the Local Universe. However, such catalogs suffer from biases whose effects increase with the distance. Malmquist biases…

宇宙学与河外天体物理 · 物理学 2015-05-20 Jenny G. Sorce

We present a new method for recovering the cosmological density, velocity, and potential fields from all-sky redshift catalogues. The method is based on an expansion of the fields in orthogonal radial (Bessel) and angular (spherical…

天体物理学 · 物理学 2015-06-24 Karl Fisher , Ofer Lahav , Yehuda Hoffman , Donald Lynden-Bell , Saleem Zaroubi

The peculiar velocity field of the local Universe provides direct insights into its matter distribution and the underlying theory of gravity, and is essential in cosmological analyses for modelling deviations from the Hubble flow. Numerous…

The formalism of Wiener filtering is developed here for the purpose of reconstructing the large scale structure of the universe from noisy, sparse and incomplete data. The method is based on a linear minimum variance solution, given data…

天体物理学 · 物理学 2009-10-22 S. Zaroubi , Y. Hoffman , K. B. Fisher , O. Lahav

Galaxy peculiar velocity data provide important dynamical clues to the structures obscured by the Zone of Avoidance (hereafter, ZOA) with resolution >~ 500km/s. This indirect probe complements the very challenging approach of directly…

天体物理学 · 物理学 2007-05-23 Saleem Zaroubi

We reconstruct the underlying density field of the 2 degree Field Galaxy Redshift Survey (2dFGRS) for the redshift range 0.035<z<0.200 using the Wiener Filtering method. The Wiener Filter suppresses shot noise and accounts for selection and…

High quality reconstructions of the three dimensional velocity and density fields of the local Universe are essential to study the local Large Scale Structure. In this paper, the Wiener Filter reconstruction technique is applied to galaxy…

宇宙学与河外天体物理 · 物理学 2017-06-21 Jenny G. Sorce , Elmo Tempel

Scatter in distance indicators introduces two conceptually distinct systematic biases when reconstructing peculiar velocity fields from redshifts and distances. The first is distance Malmquist bias (dMB) that affects individual distance…

宇宙学与河外天体物理 · 物理学 2025-12-04 Adi Nusser

A statistical method for reconstructing large scale structure behind the Zone of Avoidance is presented. It also corrects for shot-noise and for redshift distortion in galaxy surveys. The galaxy distribution is expanded in an orthogonal set…

天体物理学 · 物理学 2007-05-23 Ofer Lahav

We assess a neural network (NN) method for reconstructing 3D cosmological density and velocity fields (target) from discrete and incomplete galaxy distributions (input). We employ second-order Lagrangian Perturbation Theory to generate a…

宇宙学与河外天体物理 · 物理学 2023-06-02 Punyakoti Ganeshaiah Veena , Robert Lilow , Adi Nusser

We reconstruct the 3D matter density and peculiar velocity fields in the local Universe up to a distance of 200$\,h^{-1}\,$Mpc from the Two-Micron All-Sky Redshift Survey (2MRS), using a neural network (NN). We employed an NN with a U-net…

宇宙学与河外天体物理 · 物理学 2024-10-01 Robert Lilow , Punyakoti Ganeshaiah Veena , Adi Nusser

The formalism of Wiener filtering is applied to reconstruct, in terms of spherical harmonics, the projected $4\pi$ galaxy distribution of a mock IRAS 1.2 Jy catalog with a `Zone of Avoidance' $|b| = 15^\circ$. The Singular Value…

天体物理学 · 物理学 2007-05-23 Saleem Zaroubi

The analysis of whole-sky galaxy surveys commonly suffers from the problems of shot-noise and incomplete sky coverage (e.g. at the Zone of Avoidance). The orthogonal set of spherical harmonics is utilized here to expand the observed galaxy…

天体物理学 · 物理学 2009-10-22 O. Lahav , K. B. Fisher , Y. Hoffman , C. A. Scharf , S. Zaroubi

We present a maximum probability approach to reconstructing spatial maps of the peculiar velocity field at redshifts $z\sim0.1$, where the velocities have been measured from distance indicators (DI) such as $D_n-\sigma$ relations or…

宇宙学与河外天体物理 · 物理学 2014-11-18 Russell Johnston , David Bacon , Luis F. A. Teodoro , Robert C. Nichol , Michael S. Warren , Catherine Cress

We present a new method for constructing three-dimensional mass maps from gravitational lensing shear data. We solve the lensing inversion problem using truncation of singular values (within the context of generalized least squares…

宇宙学与河外天体物理 · 物理学 2015-05-19 Jake VanderPlas , Andrew Connolly , Bhuvnesh Jain , Mike Jarvis

Using higher-order statistics to capture cosmological information from weak lensing surveys often requires a transformation of observed shear to a measurement of the convergence signal. This inverse problem is complicated by noise and…

宇宙学与河外天体物理 · 物理学 2024-06-25 Nisha Grewal , Joe Zuntz , Tilman Tröster

The large scale structure of the universe is a complex web of clusters, filaments, and voids. Its properties are informed by galaxy redshift surveys and measurements of peculiar velocities. Wiener Filter reconstructions recover…

宇宙学与河外天体物理 · 物理学 2015-06-16 Helene M. Courtois , Daniel Pomarede , R. Brent Tully , Yehuda Hoffman , Denis Courtois
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