Related papers: Revealing Hidden Cosmic Flows through the Zone of …
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…
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:…
We develop a deep learning technique to infer the non-linear velocity field from the dark matter density field. The deep learning architecture we use is an "U-net" style convolutional neural network, which consists of 15 convolution layers…
A hierarchical Bayesian model is applied to the Cosmicflows-3 catalog of galaxy distances in order to derive the peculiar velocity field and distribution of matter within $z \sim 0.054$. The model assumes the $\Lambda$CDM model within the…
The uncovering of the large-scale structure that is hidden in the Zone of Avoidance is essential to our quest for the understanding of the dynamics of the nearby universe. Dedicated and sophisticated observations of the Zone of Avoidance…
Dust extinction and stellar confusion of the Milky Way hinder the detection of galaxies at low Galactic latitude, creating the so-called Zone of Avoidance (ZoA). This has hampered our understanding of the local dynamics, cosmic flow fields…
The peculiar velocities of dark matter halos are crucial to study many issues in cosmology and galaxy evolution. In this study, by using the state-of-the-art deep learning technique, a UNet-based neural network, we propose to reconstruct…
A large fraction of the extragalactic sky is obscured by foreground dust and stars along the plane of the Milky Way, leaving a major gap (~ 20%) in whole-sky maps of large-scale structures -- an incompleteness that is even more severe for…
This article publicly releases three-dimensional reconstructions of the local Universe gravitational field below z=0.8 that were computed using the CosmicFlows-4 catalog of 56,000 galaxy distances and its sub-sample of 1,008 type Ia…
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…
The Zone of Avoidance (ZOA), whose emptiness is an artifact of our Galaxy dust, has been challenging observers as well as theorists for many years. Multiple attempts have been made on the observational side to map this region in order to…
The distribution of matter that is measured through galaxy redshift and peculiar velocity surveys can be harnessed to learn about the physics of dark matter, dark energy, and the nature of gravity. To improve our understanding of the matter…
We have developed a new geometrical method for identifying and reconstructing a homogeneous and highly complete set of galaxy groups in the next generation of deep, flux-limited redshift surveys. Our method combines information from the…
The Wiener Filter (WF) technique enables the reconstruction of density and velocity fields from observed radial peculiar velocities. This paper aims at identifying the optimal design of peculiar velocity surveys within the WF framework. The…
Reconstructing the mass density, velocity, and tidal (MTV) fields of dark matter from galaxy surveys is essential for advancing our understanding of the LSS of the Universe. In this work, we present a machine learning-based framework using…
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…
Herein, we present a deep-learning technique for reconstructing the dark-matter density field from the redshift-space distribution of dark-matter halos. We built a UNet-architecture neural network and trained it using the COmoving…
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…
Line intensity mapping is emerging as a novel method that can measure the collective intensity fluctuations of atomic/molecular line emission from distant galaxies. Several observational programs with various wavelengths are ongoing and…
We propose a UNet-based deep learning model to reconstruct the real-space dark matter (DM) velocity field from the redshift-space distribution of sparse DM halos. Using various statistical measures, we show that the reconstructed velocity…