Georgios Vernardos

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Time-delay estimation in unresolved lensed quasars

Georgios Vernardos

Time-delay cosmography can be used to infer the Hubble parameter H-0 by measuring the relative time delays between multiple images of gravitationally lensed quasars. A few of such systems have already been used to measure H-0: Their time delays were determ ...

OXFORD UNIV PRESS2022

The very knotty lenser: Exploring the role of regularization in source and potential reconstructions using Gaussian process regression

Georgios Vernardos

Reconstructing lens potentials and lensed sources can easily become an underconstrained problem, even when the degrees of freedom are low, due to degeneracies, particularly when potential perturbations superimposed on a smooth lens are included. Regulariza ...

OXFORD UNIV PRESS2022

SEAGLE III: Towards resolving the mismatch in the dark-matter fraction in early-type galaxies between simulations and observations

Georgios Vernardos, Matthieu Schaller

The central dark-matter fraction of galaxies is sensitive to feedback processes during galaxy formation. Strong gravitational lensing has been effective in the precise measurement of the dark-matter fraction inside massive early-type galaxies. Here, we com ...

OXFORD UNIV PRESS2022

Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses

Frédéric Courbin, Aymeric Alexandre Galan, Georgios Vernardos, Austin Chandler Peel

Modeling the mass distribution of galaxy-scale strong gravitational lenses is a task of increasing difficulty. The high-resolution and depth of imaging data now available render simple analytical forms ineffective at capturing lens structures spanning a la ...

EDP SCIENCES S A2022

Search of strong lens systems in the Dark Energy Survey using convolutional neural networks

Frédéric Courbin, Benjamin Yvan Alexandre Clement, Hung-Hsu Chan, Aymeric Alexandre Galan, Karina Alexandra Rojas Olate, Cameron Alexander Campbell Lemon, Georgios Vernardos, Elodie Marie Charlène Savary

We present our search for strong lens, galaxy-scale systems in the first data release of the Dark Energy Survey (DES), based on a color-selected parent sample of 18 745 029 luminous red galaxies (LRGs). We used a convolutional neural network (CNN) to grade ...

EDP SCIENCES S A2022

TDCOSMO VII. Boxyness/discyness in lensing galaxies: Detectability and impact on H-0

Aymeric Alexandre Galan, Georgios Vernardos

In the context of gravitational lensing, the density profile of lensing galaxies is often considered to be perfectly elliptical. Potential angular structures are generally ignored, except to explain flux ratios of point-like sources (i.e. flux ratio anomal ...

EDP SCIENCES S A2022

Strong lensing in UNIONS: Toward a pipeline from discovery to modeling

Frédéric Courbin, Benjamin Yvan Alexandre Clement, Hung-Hsu Chan, Karina Alexandra Rojas Olate, Cameron Alexander Campbell Lemon, Georgios Vernardos, Elodie Marie Charlène Savary

We present a search for galaxy-scale strong gravitational lenses in the initial 2500 square degrees of the Canada-France Imaging Survey (CFIS). We designed a convolutional neural network (CNN) committee that we applied to a selection of 2 344 002 r-band im ...

EDP SCIENCES S A2022

High-quality Strong Lens Candidates in the Final Kilo-Degree Survey Footprint

Georgios Vernardos

We present 97 new high-quality strong lensing candidates found in the final similar to 350 deg(2) that complete the full similar to 1350 deg(2) area of the Kilo-Degree Survey (KiDS). Together with our previous findings, the final list of high-quality candi ...

IOP Publishing Ltd2021

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The very knotty lenser: Exploring the role of regularization in source and potential reconstructions using Gaussian process regression

Georgios Vernardos

OXFORD UNIV PRESS2022

SEAGLE III: Towards resolving the mismatch in the dark-matter fraction in early-type galaxies between simulations and observations

Georgios Vernardos, Matthieu Schaller

OXFORD UNIV PRESS2022

Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses

Frédéric Courbin, Aymeric Alexandre Galan, Georgios Vernardos, Austin Chandler Peel

EDP SCIENCES S A2022