V. de Lapparent

2.8k total citations · 1 hit paper
34 papers, 1.0k citations indexed

About

V. de Lapparent is a scholar working on Astronomy and Astrophysics, Instrumentation and Computer Vision and Pattern Recognition. According to data from OpenAlex, V. de Lapparent has authored 34 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Astronomy and Astrophysics, 25 papers in Instrumentation and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in V. de Lapparent's work include Astronomy and Astrophysical Research (25 papers), Galaxies: Formation, Evolution, Phenomena (21 papers) and Stellar, planetary, and galactic studies (10 papers). V. de Lapparent is often cited by papers focused on Astronomy and Astrophysical Research (25 papers), Galaxies: Formation, Evolution, Phenomena (21 papers) and Stellar, planetary, and galactic studies (10 papers). V. de Lapparent collaborates with scholars based in France, United States and Italy. V. de Lapparent's co-authors include Margaret J. Geller, J. P. Huchra, E. Bertin, S. Arnouts, Jr. Corwin Harold G., E. Slezak, A. Bijaoui, Gaspar Galaz, Paul Hickson and Y. Mellier 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

V. de Lapparent

32 papers receiving 988 citations

Hit Papers

A slice of the universe 1986 2026 1999 2012 1986 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
V. de Lapparent France 17 885 355 210 138 79 34 1.0k
J. Huchra United States 15 1.3k 1.4× 538 1.5× 263 1.3× 150 1.1× 50 0.6× 31 1.4k
Dmitry Pogosyan Canada 10 1.2k 1.3× 405 1.1× 317 1.5× 146 1.1× 42 0.5× 13 1.2k
R. Scaramella Italy 17 908 1.0× 356 1.0× 179 0.9× 73 0.5× 59 0.7× 56 996
M. Ramella Italy 22 1.4k 1.6× 744 2.1× 242 1.2× 142 1.0× 54 0.7× 51 1.4k
Sandrine Codis France 20 1.4k 1.5× 537 1.5× 337 1.6× 160 1.2× 34 0.4× 49 1.4k
Chiaki Hikage Japan 20 1.1k 1.3× 298 0.8× 360 1.7× 113 0.8× 37 0.5× 41 1.2k
J. Hartlap Germany 15 1.3k 1.5× 462 1.3× 261 1.2× 89 0.6× 49 0.6× 17 1.4k
Hume A. Feldman United States 22 1.6k 1.8× 335 0.9× 531 2.5× 182 1.3× 26 0.3× 51 1.7k
Karl B. Fisher United States 19 1.6k 1.8× 596 1.7× 405 1.9× 180 1.3× 36 0.5× 24 1.7k
E. Krause United States 19 1.2k 1.4× 466 1.3× 278 1.3× 68 0.5× 29 0.4× 52 1.3k

Countries citing papers authored by V. de Lapparent

Since Specialization
Citations

This map shows the geographic impact of V. de Lapparent'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 V. de Lapparent with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites V. de Lapparent more than expected).

Fields of papers citing papers by V. de Lapparent

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by V. de Lapparent. 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 V. de Lapparent. The network helps show where V. de Lapparent may publish in the future.

Co-authorship network of co-authors of V. de Lapparent

This figure shows the co-authorship network connecting the top 25 collaborators of V. de Lapparent. A scholar is included among the top collaborators of V. de Lapparent 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 V. de Lapparent. V. de Lapparent 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.
Lapparent, V. de, et al.. (2023). Tailoring galaxies: Size–luminosity–surface brightness relations of bulges and disks along the morphological sequence. Astronomy and Astrophysics. 680. A49–A49. 2 indexed citations
2.
Lapparent, V. de, et al.. (2022). Aging of galaxies along the morphological sequence, marked by bulge growth and disk quenching. Astronomy and Astrophysics. 666. A170–A170. 19 indexed citations
3.
Livet, F., Tom Charnock, D. Le Borgne, & V. de Lapparent. (2021). Catalog-free modeling of galaxy types in deep images. Springer Link (Chiba Institute of Technology). 1 indexed citations
4.
Lapparent, V. de, et al.. (2017). Inferring the photometric and size evolution of galaxies from image simulations. Springer Link (Chiba Institute of Technology). 12 indexed citations
5.
Lapparent, V. de, et al.. (2017). Inferring the size and photometric evolution of galaxies from image simulations. arXiv (Cornell University). 1 indexed citations
6.
Bertin, E., V. de Lapparent, P. Fouqué, et al.. (2011). The EFIGI catalogue of 4458 nearby galaxies with detailed morphology. Springer Link (Chiba Institute of Technology). 71 indexed citations
7.
Lapparent, V. de, et al.. (2011). The EFIGI catalogue of 4458 nearby galaxies with morphology. Astronomy and Astrophysics. 532. A75–A75. 20 indexed citations
8.
Seymour, N., B. Rocca‐Volmerange, & V. de Lapparent. (2007). A 12 $\mathsf{\mu}$m ISOCAM survey of the ESO-Sculptor field. Astronomy and Astrophysics. 475(3). 791–799. 1 indexed citations
9.
Rocca‐Volmerange, B., V. de Lapparent, N. Seymour, & Michel Fioc. (2007). The 12 $\mathsf{\mu}$mISO-ESO-Sculptor and 24 $\mathsf{\mu}$mSpitzerfaint counts reveal a population of ULIRGs as dusty massive ellipticals. Astronomy and Astrophysics. 475(3). 801–812. 7 indexed citations
10.
Lapparent, V. de & E. Slezak. (2007). Spatial clustering in the ESO-Sculptor survey: two-pointcorrelation functions by galaxy type at redshifts 0.1–0.5. Astronomy and Astrophysics. 472(1). 29–49. 2 indexed citations
11.
Lapparent, V. de, S. Arnouts, Gaspar Galaz, & S. Bardelli. (2004). The ESO-Sculptor Survey: Evolution of late-type galaxies \nat redshifts 0.1–0.5. Springer Link (Chiba Institute of Technology). 5 indexed citations
12.
Lapparent, V. de, Gaspar Galaz, S. Bardelli, & S. Arnouts. (2003). The ESO-Sculptor Survey: Luminosity functions of galaxies per spectral type at redshifts $0.1{-}0.5$. Astronomy and Astrophysics. 404(3). 831–860. 28 indexed citations
13.
Lapparent, V. de. (2003). Critical analysis of the luminosity functions per galaxy type measured from redshift surveys. Astronomy and Astrophysics. 408(3). 845–872. 22 indexed citations
14.
Hickson, Paul, et al.. (2002). The Large Zenith Telescope Survey: A Deep Survey Using a 6-m Liquid Mirror Telescope. CERN Bulletin. 283. 129. 2 indexed citations
15.
Cabanac, R., V. de Lapparent, & Paul Hickson. (2002). Classification and redshift estimation by principal component analysis. Astronomy and Astrophysics. 389(3). 1090–1116. 19 indexed citations
16.
Arnouts, S., V. de Lapparent, G. Mathez, et al.. (1997). The ESO-Sculptor faint galaxy redshift survey: The photometric sample. Springer Link (Chiba Institute of Technology). 22 indexed citations
17.
Galaz, Gaspar & V. de Lapparent. (1997). THE ESO-SCULPTOR SURVEY : SPECTRAL CLASSIFICATION OF GALAXIES WITH Z 0.5. arXiv (Cornell University). 332(2). 459–478. 6 indexed citations
18.
Lapparent, V. de, Cindy Bellanger, S. Arnouts, et al.. (1993). Mapping the large-scale structure with the ESO multi-slit spectrographs.. Msngr. 72. 34–38.
19.
Huchra, J. P., Margaret J. Geller, V. de Lapparent, & R. Burg. (1988). The CFA Redshift Survey. 130. 105. 3 indexed citations
20.
Geller, Margaret J., Michael J. Kurtz, & V. de Lapparent. (1984). The Shane-Wirtanen counts. The Astrophysical Journal. 287. L55–L55. 7 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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