Related papers: New high-quality strong lens candidates with deep …
Strong gravitational lensing is a promising way of uncovering the nature of dark matter, by finding perturbations to images that cannot be well accounted for by modeling the lens galaxy without additional structure, be it subhalos (smaller…
We have carried out HST snapshot observations at 1.1 $\mu$m of 281 candidate strongly lensed galaxies identified in the wide-area extragalactic surveys conducted with the Herschel space observatory. Our candidates comprise systems with flux…
The identification of strong gravitational lenses in large surveys has historically been a rather time consuming exercise. Early data from the Herschel Astrophysical Terahertz Large Area Survey (Herschel-ATLAS) demonstrate that lenses can…
We present the results of a new search for galaxy-scale strong lensing systems in CFHTLS Wide. Our lens-finding technique involves a preselection of potential lens galaxies, applying simple cuts in size and magnitude. We then perform a…
Cosmological models of hierarchical structure formation predict the existence of a widespread population of dual accreting supermassive black holes (SMBHs) on kpc-scale separations, corresponding to projected distances < 0".8 at redshifts…
Strong lensing in galaxy clusters probes properties of dense cores of dark matter halos in mass, studies the distant universe at flux levels and spatial resolutions otherwise unavailable, and constrains cosmological models independently.…
Strong lensing of background galaxies provides important information about the matter distribution around lens galaxies. Traditional modelling of such strong lenses is both time and resource intensive. Fast and automated analysis methods…
Strong gravitationally lensed quasars provide powerful means to study galaxy evolution and cosmology. We use Chitah to hunt for new lens systems in the Hyper Suprime$-$Cam Subaru Strategic Program (HSC SSP) S16A. We present 46 lens…
The Planck sub-mm surveys detected the brightest strongly gravitationally lensed dusty galaxies in the sky. The combination of their extreme gravitational flux boosting and image stretching offers the unique possibility of measuring in…
Strong gravitational lenses allow us to peer into the farthest reaches of space by bending the light from a background object around a massive object in the foreground. Unfortunately, these lenses are extremely rare, and manually finding…
In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep…
Context. We have developed deep learning (DL) and AI-based tools to search extant narrow-band wide-field H$\alpha$ surveys of the Galactic Plane for elusive planetary nebulae (PNe) which are hidden in dense star fields towards the Galactic…
We present a comprehensive study of the effectiveness of Convolution Neural Networks (CNNs) to detect long duration transient gravitational-wave signals lasting $O(hours-days)$ from isolated neutron stars. We determine that CNNs are robust…
This paper proposes a deep leaning method to address the challenging facial attractiveness prediction problem. The method constructs a convolutional neural network of facial beauty prediction using a new deep cascaded fine-turning scheme…
We present the results of a CCD imaging survey for gravitational lensing in a sample of 38 X-ray-selected clusters of galaxies. Our sample consists of the most X-ray luminous (Lx>= 2x10^{44} erg s^{-1}) clusters selected from the Einstein…
We report on a novel search for radio gravitational lenses. Using the Very Large Array, we imaged ten candidates with both dual redshifts in Sloan Digital Sky Survey spectra and 1.4 GHz radio flux >2 mJy in the FIRST survey. The VLA maps…
We introduce a new light-field dataset of materials, and take advantage of the recent success of deep learning to perform material recognition on the 4D light-field. Our dataset contains 12 material categories, each with 100 images taken…
Convolutional Neural Networks (CNN) have become state-of-the-art in the field of image classification. However, not everything is understood about their inner representations. This paper tackles the interpretability and explainability of…
In the last two years, convolutional neural networks (CNNs) have achieved an impressive suite of results on standard recognition datasets and tasks. CNN-based features seem poised to quickly replace engineered representations, such as SIFT…
Modelling the mass distributions of strong gravitational lenses is often necessary to use them as astrophysical and cosmological probes. With the high number of lens systems ($>10^5$) expected from upcoming surveys, it is timely to explore…