Related papers: Revealing Hidden Cosmic Flows through the Zone of …
We present a new reconstruction of the mass density and the peculiar velocity fields in the nearby universe using recent measurements of Tully-Fisher distances for a sample of late spirals. We find significant differences between our…
Galaxy peculiar velocities provide an integral source of cosmological information that can be harnessed to measure the growth rate of large scale structure and constrain possible extensions to General Relativity. In this work, we present a…
Weak lensing provides a direct way of mapping the density distribution in the universe. To reconstruct the density field from the shear catalog, an important step is to build the shear field from the shear catalog, which can be quite…
We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen…
Redshift space distortions (RSD) in the void-galaxy correlation $\xi^s$ provide information on the linear growth rate of structure in low density environments. Accurate modelling of these RSD effects can also allow the use of voids in…
We present an Effective Field Theory based reconstruction of quintessence models of dark energy directly from cosmological data. We show that current cosmological data possess enough constraining power to test several quintessence model…
The void galaxy cross correlation function is a powerful tool to extract cosmological information. Through the void galaxy cross correlation function, cosmic voids, the underdense regions in the galaxy distribution, are used for refined…
We propose a novel method to reconstruct high-resolution three-dimensional mass maps using data from photometric weak-lensing surveys. We apply an adaptive LASSO algorithm to perform a sparsity-based reconstruction on the assumption that…
We investigate the prospect of reconstructing the ''cosmic distance ladder'' of the Universe using a novel deep learning framework called LADDER - Learning Algorithm for Deep Distance Estimation and Reconstruction. LADDER is trained on the…
The distribution of galaxies in the Zone of Avoidance (ZoA) is incomplete due to the presence of our own Galaxy. Our research focused on the identification and characterisation of galaxies in the ZoA, using the new near-infrared data from…
Context: Several observations of the local Universe (LU) point towards the existence of very prominent structures. The presence of massive galaxy clusters and local super clusters on the one hand, but also large local voids and…
Galaxy surveys are crucial for studying large-scale structure (LSS) and cosmology, yet they face limitations--imaging surveys provide extensive sky coverage but suffer from photo-$z$ uncertainties, while spectroscopic surveys yield precise…
The inverse scattering problem is of critical importance in a number of fields, including medical imaging, sonar, sensing, non-destructive evaluation, and several others. The problem of interest can vary from detecting the shape to the…
Sparse arrays enable resolving more direction of arrivals (DoAs) than antenna elements using non-uniform arrays. This is typically achieved by reconstructing the covariance of a virtual large uniform linear array (ULA), which is then…
Cosmological surveys aim at answering fundamental questions about our Universe, including the nature of dark matter or the reason of unexpected accelerated expansion of the Universe. In order to answer these questions, two important…
We introduce a novel method for reconstructing the projected matter distributions of galaxy clusters with weak-lensing (WL) data based on convolutional neural network (CNN). Training datasets are generated with ray-tracing through…
One of the hallmarks of active galactic nuclei are that they are highly variable with time. In watching the spectra vary it has been observed that the emission-lines often appear to "reverberate" -- that is they vary in response to…
We demonstrate the capabilities of probabilistic diffusion models to reduce dramatically the computational cost of expensive hydrodynamical simulations to study the relationship between observable baryonic cosmological probes and dark…
In many applications, flow measurements are usually sparse and possibly noisy. The reconstruction of a high-resolution flow field from limited and imperfect flow information is significant yet challenging. In this work, we propose an…
We present a new method for reconstructing the cosmological density, peculiar velocity and peculiar gravitational potential on large scales from redshift data. We remove the distorting effects of line-of-sight peculiar motions by using the…