G. Vernardos

1.6k total citations
40 papers, 754 citations indexed

About

G. Vernardos is a scholar working on Astronomy and Astrophysics, Atomic and Molecular Physics, and Optics and Instrumentation. According to data from OpenAlex, G. Vernardos has authored 40 papers receiving a total of 754 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Astronomy and Astrophysics, 17 papers in Atomic and Molecular Physics, and Optics and 16 papers in Instrumentation. Recurrent topics in G. Vernardos's work include Galaxies: Formation, Evolution, Phenomena (31 papers), Adaptive optics and wavefront sensing (17 papers) and Astronomy and Astrophysical Research (16 papers). G. Vernardos is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (31 papers), Adaptive optics and wavefront sensing (17 papers) and Astronomy and Astrophysical Research (16 papers). G. Vernardos collaborates with scholars based in Netherlands, Switzerland and United Kingdom. G. Vernardos's co-authors include C. Tortora, L. V. E. Koopmans, Christopher J. Fluke, N. R. Napolitano, G. Covone, Saikat Chatterjee, C. E. Petrillo, Petra Schneider, N. F. Bate and Thomas E. Collett and has published in prestigious journals such as The Astrophysical Journal, Monthly Notices of the Royal Astronomical Society and The Astrophysical Journal Supplement Series.

In The Last Decade

G. Vernardos

40 papers receiving 691 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
G. Vernardos Netherlands 17 653 253 171 56 56 40 754
James Bosch United States 7 473 0.7× 186 0.7× 126 0.7× 83 1.5× 72 1.3× 10 560
R. Armstrong United States 11 655 1.0× 242 1.0× 167 1.0× 115 2.1× 107 1.9× 24 779
S. L. Bridle United Kingdom 5 682 1.0× 246 1.0× 115 0.7× 43 0.8× 145 2.6× 6 743
Mike Jarvis United States 13 667 1.0× 241 1.0× 180 1.1× 88 1.6× 125 2.2× 32 785
Y. Shu China 13 474 0.7× 233 0.9× 102 0.6× 30 0.5× 39 0.7× 43 537
Benjamin Giblin United Kingdom 15 607 0.9× 234 0.9× 64 0.4× 29 0.5× 120 2.1× 25 672
J. Meyers United States 8 467 0.7× 195 0.8× 120 0.7× 65 1.2× 166 3.0× 29 603
A. Kiessling United States 12 574 0.9× 262 1.0× 84 0.5× 37 0.7× 86 1.5× 23 616
Fumihiro Uraguchi Japan 10 475 0.7× 202 0.8× 75 0.4× 29 0.5× 49 0.9× 42 538
Chiara Spiniello Italy 19 1.4k 2.1× 729 2.9× 195 1.1× 52 0.9× 149 2.7× 52 1.5k

Countries citing papers authored by G. Vernardos

Since Specialization
Citations

This map shows the geographic impact of G. Vernardos's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by G. Vernardos with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites G. Vernardos more than expected).

Fields of papers citing papers by G. Vernardos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by G. Vernardos. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by G. Vernardos. The network helps show where G. Vernardos may publish in the future.

Co-authorship network of co-authors of G. Vernardos

This figure shows the co-authorship network connecting the top 25 collaborators of G. Vernardos. A scholar is included among the top collaborators of G. Vernardos based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with G. Vernardos. G. Vernardos is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Anguita, T., et al.. (2025). Predicting High-magnification Events in Microlensed Quasars in the Era of LSST Using Recurrent Neural Networks. The Astrophysical Journal. 981(1). 61–61. 1 indexed citations
2.
Vernardos, G., et al.. (2024). Resolving the vicinity of supermassive black holes with gravitational microlensing. Monthly Notices of the Royal Astronomical Society. 531(1). 1095–1112. 5 indexed citations
3.
Vernardos, G., et al.. (2024). Measuring the substructure mass power spectrum of 23 SLACS strong galaxy–galaxy lenses with convolutional neural networks. Monthly Notices of the Royal Astronomical Society. 532(2). 2248–2269. 2 indexed citations
4.
Vernardos, G., Christopher J. Fluke, N. F. Bate, Darren Croton, & D. Vohl. (2024). GERLUMPH DATA RELEASE 2:2.5 BILLION SIMULATED MICROLENSING LIGHT CURVES. Figshare. 5 indexed citations
5.
Shajib, Anowar J., G. Vernardos, V. Motta, et al.. (2024). Strong Lensing by Galaxies. Space Science Reviews. 220(8). 87–87. 11 indexed citations
6.
Suyu, S. H., A. Goobar, Thomas E. Collett, Anupreeta More, & G. Vernardos. (2024). Strong Gravitational Lensing and Microlensing of Supernovae. Space Science Reviews. 220(1). 13–13. 23 indexed citations
7.
Galan, A., et al.. (2024). Exploiting the diversity of modeling methods to probe systematic biases in strong lensing analyses. Astronomy and Astrophysics. 692. A87–A87. 5 indexed citations
8.
Biggio, Luca, Han Wang, A. Galan, et al.. (2023). Accelerating galaxy dynamical modeling using a neural network for joint lensing and kinematic analyses. Astronomy and Astrophysics. 679. A59–A59. 3 indexed citations
9.
Galan, A., et al.. (2023). COOLEST: COde-independent Organized LEnsSTandard. The Journal of Open Source Software. 8(88). 5567–5567. 3 indexed citations
10.
Vernardos, G., et al.. (2023). Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses. Astronomy and Astrophysics. 675. A125–A125. 5 indexed citations
11.
Rojas, K., B. Clément, F. Courbin, et al.. (2022). Strong lensing in UNIONS: Toward a pipeline from discovery to modeling. Astronomy and Astrophysics. 666. A1–A1. 19 indexed citations
12.
Rojas, K., B. Clément, F. Courbin, et al.. (2022). Search of strong lens systems in the Dark Energy Survey using convolutional neural networks. Astronomy and Astrophysics. 668. A73–A73. 34 indexed citations
13.
Li, Rui, N. R. Napolitano, Chiara Spiniello, et al.. (2021). High-quality Strong Lens Candidates in the Final Kilo-Degree Survey Footprint. The Astrophysical Journal. 923(1). 16–16. 38 indexed citations
14.
Chan, J. H. H., Cameron Lemon, F. Courbin, et al.. (2021). Discovery of strongly lensed quasars in the Ultraviolet Near Infrared Optical Northern Survey (UNIONS). Astronomy and Astrophysics. 659. A140–A140. 10 indexed citations
15.
Vernardos, G., Dominique Sluse, Martin Millon, et al.. (2021). Constraining quasar structure using high-frequency microlensing variations and continuum reverberation. arXiv (Cornell University). 14 indexed citations
16.
Sluse, Dominique, et al.. (2021). TDCOSMO. Astronomy and Astrophysics. 659. A127–A127. 29 indexed citations
17.
Koopmans, L. V. E., C. Tortora, Matthieu Schaller, et al.. (2021). SEAGLE – III: Towards resolving the mismatch in the dark-matter fraction in early-type galaxies between simulations and observations. Monthly Notices of the Royal Astronomical Society. 509(1). 1245–1251. 7 indexed citations
18.
Li, Rui, N. R. Napolitano, C. Tortora, et al.. (2020). New High-quality Strong Lens Candidates with Deep Learning in the Kilo-Degree Survey. The Astrophysical Journal. 899(1). 30–30. 50 indexed citations
19.
Spiniello, Chiara, Adriano Agnello, N. R. Napolitano, et al.. (2018). KiDS-SQuaD: The KiDS Strongly lensed Quasar Detection project. Monthly Notices of the Royal Astronomical Society. 480(1). 1163–1173. 31 indexed citations
20.
Thompson, A., G. Vernardos, Christopher J. Fluke, & Benjamin R. Barsdell. (2014). GPU-D: Generating cosmological microlensing magnification maps. ascl. 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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