Related papers: Enhancing Gravitational Lens Study with Deep Learn…
Gravitationally lensed supernovae (SNe) are extremely rare and fade quickly; as a result, they are challenging to detect. To identify lensed SNe in large imaging datasets, current surveys primarily rely on the {\it magnification} effect of…
In this paper, we propose a new method to use the strong lensing data sets to constrain a cosmological model. By taking the ratio…
Current ground-based cosmological surveys, such as the Dark Energy Survey (DES), are predicted to discover thousands of galaxy-scale strong lenses, while future surveys, such as the Vera Rubin Observatory Legacy Survey of Space and Time…
Strong gravitational lensing along with the distance sum rule method can constrain both cosmological parameters as well as density profiles of galaxies without assuming any fiducial cosmological model. To constrain galaxy parameters and…
Applying the distance sum rule in strong gravitational lensing (SGL) and type Ia supernova (SN Ia) observations, one can provide an interesting cosmological model-independent method to determine the cosmic curvature parameter $\Omega_k$. In…
The unprecedented number of gravitational lenses expected from new-generation facilities such as the ESA Euclid telescope and the Vera Rubin Observatory makes it crucial to rethink our classical approach to lens-modelling. In this paper, we…
Forthcoming large imaging surveys such as Euclid and the Vera Rubin Observatory Legacy Survey of Space and Time are expected to find more than $10^5$ strong gravitational lens systems, including many rare and exotic populations such as…
Real astrophysical lenses typically lack axisymmetry, necessitating the study of gravitational-wave (GW) lensing by elliptical mass distributions to accurately assess detectability and waveform interpretation. We investigate strong lensing…
Galaxy-scale strong lenses in galaxy clusters provide a unique tool to investigate their inner mass distribution and the sub-halo density profiles in the low-mass regime, which can be compared with the predictions from cosmological…
The large number of strong lenses discoverable in future astronomical surveys will likely enhance the value of strong gravitational lensing as a cosmic probe of dark energy and dark matter. However, leveraging the increased statistical…
In a strong gravitational lensing system, the distorted light from a source is analysed to infer the properties of the lens. However, light emitted by the lens itself can contaminate the image of the source, introducing systematic errors in…
In these lectures I give an overview of gravitational lensing, concentrating on theoretical aspects, including derivations of some of the important results. Topics covered include the determination of surface mass densities of intervening…
We show how the combination of observations related to strong gravitational lensing and stellar dynamics in ellipticals offers a new way to measure the cosmological matter and dark-energy density parameters. A gravitational lensing estimate…
Recently convolutional neural networks (CNNs) achieve great accuracy in visual recognition tasks. DenseNet becomes one of the most popular CNN models due to its effectiveness in feature-reuse. However, like other CNN models, DenseNets also…
Galaxy clusters are the most massive gravitationally bound structures in the Universe and key probes of cosmic evolution. The large data volume expected from upcoming surveys requires efficient automated analysis methods for tens of…
Strong gravitational lensing of distant supernovae (SNe), particularly Type Ia's, has some exploitable properties not available when other sorts of cosmologically distant sources are lensed. One such property is that the ``standard candle''…
Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR) performances in recent years. %Despite the substantial advancement% While most existing work assumes a simple and fixed degradation model (e.g., bicubic…
Studies of strong gravitational lensing in current and upcoming wide and deep photometric surveys, and of stellar kinematics from (integral-field) spectroscopy at increasing redshifts, promise to provide valuable constraints on galaxy…
Dropout Regularization, serving to reduce variance, is nearly ubiquitous in Deep Learning models. We explore the relationship between the dropout rate and model complexity by training 2,000 neural networks configured with random…
Searches and analyses of strong gravitational lenses are challenging due to the rarity and image complexity of these astronomical objects. Next-generation surveys (both ground- and space-based) will provide more opportunities to derive…