Related papers: Enhancing Gravitational Lens Study with Deep Learn…
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…
In this study, we explore gravitational lensing using more realistic dark matter halo models, moving beyond the limitations of spherical-collapse approximations. Through analytical calculations employing various mass functions, we address…
Gravitational lensing is the relativistic effect generated by massive bodies, which bend the space-time surrounding them. It is a deeply investigated topic in astrophysics and allows validating theoretical relativistic results and studying…
Deep neural networks have become the default choice for many of the machine learning tasks such as classification and regression. Dropout, a method commonly used to improve the convergence of deep neural networks, generates an ensemble of…
Traditional estimators of the galaxy power spectrum and bispectrum are sensitive to the survey geometry. They yield spectra that differ from the true underlying signal since they are convolved with the window function of the survey. For the…
Self-supervised learning has been known for learning good representations from data without the need for annotated labels. We explore the simple siamese (SimSiam) architecture for representation learning on strong gravitational lens images.…
The Early Release Observations (ERO) from Euclid have detected several new galaxy-galaxy strong gravitational lenses, with the all-sky survey expected to find 170,000 new systems, greatly enhancing studies of dark matter, dark energy, and…
This paper exploits the gravitational magnification of SNe Ia to measure properties of dark matter haloes. The magnification of individual SNe Ia can be computed using observed properties of foreground galaxies and dark matter halo models.…
Deep learning is a powerful analysis technique that has recently been proposed as a method to constrain cosmological parameters from weak lensing mass maps. Due to its ability to learn relevant features from the data, it is able to extract…
Robust measurements of cosmological parameters from galaxy surveys rely on our understanding of systematic effects that impact the observed galaxy density field. In this paper we present, validate, and implement the idea of adopting the…
Weak Lensing (WL) surveys are reaching unprecedented depths, enabling the investigation of very small angular scales. At these scales, nonlinear gravitational effects lead to higher-order correlations making the matter distribution highly…
The distribution of dark and luminous matter can be mapped around galaxies that gravitationally lens background objects into arcs or Einstein rings. New surveys will soon observe hundreds of thousands of galaxy lenses, and current,…
Deep convolutional neural networks (DCNNs) have become the most common solution for automatic image annotation due to their non-parametric nature, good performance, and their accessibility through libraries such as TensorFlow. Among other…
We have conducted a search for new strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 8. We use deep residual neural networks, building on previous work presented in Huang…
Establishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge. Because…
Ongoing searches for supernovae (SNe) at cosmological distances have recently started to provide large numbers of events with measured redshifts and apparent brightnesses. Compared to quasars or galaxies, Type Ia SNe represent a population…
Strong gravitational lensing is a powerful probe for studying the fundamental properties of dark matter on sub-galactic scales. Detailed analyses of galaxy-scale lenses have revealed localized gravitational perturbations beyond the smooth…
We present a sample of 16 likely strong gravitational lenses identified in the VST Optical Imaging of the CDFS and ES1 fields (VOICE survey) using Convolutional Neural Networks (CNNs). We train two different CNNs on composite images…
Gravitational lensing shear has the potential to be the most powerful tool for constraining the nature of dark energy. However, accurate measurement of galaxy shear is crucial and has been shown to be non-trivial by the Shear TEsting…
Significant adjustments to the values of the cosmological parameters estimated from high-redshift Type Ia Supernov\ae data are reported, almost an order of magnitude greater than previously found. They arise from the effects of weak…