Related papers: Detecting gravitational lenses using machine learn…
Locating discriminative parts plays a key role in fine-grained visual classification due to the high similarities between different objects. Recent works based on convolutional neural networks utilize the feature maps taken from the last…
Like many areas of astrophysics and cosmology, the Vera C. Rubin Observatory will be transformational for almost all the applications of strong lensing, thanks to the dramatic increase in the number of known strong lenses by two orders of…
Strong gravitational lensing provides unique opportunities to investigate the mass distribution at the cores of galaxy clusters and to study high redshift galaxies. Using $94$ strong lensing models of $74$ cluster fields from the…
Exoplanet observations are currently analysed with Bayesian retrieval techniques. Due to the computational load of the models used, a compromise is needed between model complexity and computing time. Analysis of data from future facilities,…
The best gravitational lenses for detecting distant galaxies are those with the largest mass concentrations and the most advantageous configurations of that mass along the line of sight. Our new method for finding such gravitational…
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 present a convolutional neural network to classify distinct cosmological scenarios based on the statistically similar weak-lensing maps they generate. Modified gravity (MG) models that include massive neutrinos can mimic the standard…
Galaxy clusters appear as extended sources in XMM-Newton images, but not all extended sources are clusters. So, their proper classification requires visual inspection with optical images, which is a slow process with biases that are almost…
Gravitational lensing provides a means to measure mass that does not rely on detecting and analysing light from the lens itself. Compact objects are ideal gravitational lenses, because they have relatively large masses and are dim. In this…
Like light, gravitational waves can be gravitationally lensed by massive objects along their travel path. Strong lensing produces several images from the same binary coalescence and is forecasted to have a promising rate in ground-based…
Over the past decade advancements in the understanding of several astrophysical phenomena have allowed us to infer a concordance cosmological model that successfully accounts for most of the observations of our universe. This has opened up…
Computer vision systems in real-world applications need to be robust to partial occlusion while also being explainable. In this work, we show that black-box deep convolutional neural networks (DCNNs) have only limited robustness to partial…
Deep convolutional neural networks (CNNs) have demonstrated impressive performance on visual object classification tasks. In addition, it is a useful model for predication of neuronal responses recorded in visual system. However, there is…
With today's millimeter and submillimeter instruments observers use gravitational lensing mostly as a tool to boost the sensitivity when observing distant objects. This is evident through the dominance of gravitationally lensed objects…
Convolutional Neural Networks (CNNs) have revolutionized the understanding of visual content. This is mainly due to their ability to break down an image into smaller pieces, extract multi-scale localized features and compose them to…
We present results from a systematic search for strong gravitational lenses in the GOODS ACS data. The search technique involves creating a sample of likely lensing galaxies, which we define as massive early-type galaxies in a redshift…
Convolutional neural networks (CNNs) are one of the most successful computer vision systems to solve object recognition. Furthermore, CNNs have major applications in understanding the nature of visual representations in the human brain. Yet…
Gravitational lensing has become one of the most powerful tools available for investigating the 'dark side' of the universe. Cosmological strong gravitational lensing, in particular, probes the properties of the dense cores of dark matter…
Bayesian model selection methods provide a self-consistent probabilistic framework to test the validity of competing scenarios given a set of data. We present a case study application to strong gravitational lens parametric models. Our goal…
Strong gravitational lensing provides valuable insights into the mass distribution of galaxies and the nature of dark matter. However, its modeling is computationally demanding due to the large volume of strong lensing observations. In this…