O. Lahav

75.9k total citations · 1 hit paper
137 papers, 4.2k citations indexed

About

O. Lahav is a scholar working on Astronomy and Astrophysics, Instrumentation and Nuclear and High Energy Physics. According to data from OpenAlex, O. Lahav has authored 137 papers receiving a total of 4.2k indexed citations (citations by other indexed papers that have themselves been cited), including 119 papers in Astronomy and Astrophysics, 42 papers in Instrumentation and 31 papers in Nuclear and High Energy Physics. Recurrent topics in O. Lahav's work include Galaxies: Formation, Evolution, Phenomena (95 papers), Cosmology and Gravitation Theories (52 papers) and Astronomy and Astrophysical Research (42 papers). O. Lahav is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (95 papers), Cosmology and Gravitation Theories (52 papers) and Astronomy and Astrophysical Research (42 papers). O. Lahav collaborates with scholars based in United Kingdom, United States and Russia. O. Lahav's co-authors include F. B. Abdalla, P. B. Lilje, Joel R. Primack, M. J. Rees, L. Sodré, Michael C. Storrie‐Lombardi, Shaun A. Thomas, Sarah Bridle, J. P. Huchra and I. Sadeh and has published in prestigious journals such as Nature, Science and Physical Review Letters.

In The Last Decade

O. Lahav

134 papers receiving 4.1k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

THE 2MASS REDSHIFT SURVEY—DESCRIPTION AND DATA RELEASE

2012 450 citations J. P. Huchra, Lucas M. Macri et al. The Astrophysical Journal Supplement Series profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
O. Lahav United Kingdom	35	3.6k	1.3k	1.1k	283	244	137	4.2k
Tamás Budavári United States	23	3.5k 1.0×	816 0.6×	1.3k 1.3×	313 1.1×	136 0.6×	92	3.9k
E. Gaztañaga Spain	36	4.9k 1.4×	1.6k 1.2×	1.2k 1.2×	247 0.9×	508 2.1×	143	5.1k
Raúl E. Angulo Spain	36	4.6k 1.3×	1.4k 1.0×	2.0k 1.8×	242 0.9×	378 1.5×	124	4.9k
Robert J. Thacker Canada	18	3.6k 1.0×	629 0.5×	1.7k 1.6×	250 0.9×	284 1.2×	32	3.9k
Shirley Ho United States	32	3.2k 0.9×	1.4k 1.0×	795 0.7×	105 0.4×	290 1.2×	106	3.8k
Will J. Percival United Kingdom	37	6.3k 1.7×	2.4k 1.8×	1.7k 1.6×	261 0.9×	463 1.9×	139	6.6k
L. Moscardini Italy	39	5.2k 1.4×	1.6k 1.2×	1.9k 1.8×	202 0.7×	358 1.5×	182	5.4k
H. Hildebrandt Germany	39	4.0k 1.1×	908 0.7×	1.7k 1.6×	246 0.9×	165 0.7×	135	4.3k
Alan Heavens United Kingdom	46	5.9k 1.6×	1.7k 1.2×	1.9k 1.8×	179 0.6×	436 1.8×	166	6.2k
Gustavo Yepes Spain	43	6.2k 1.7×	1.5k 1.1×	2.8k 2.7×	290 1.0×	432 1.8×	215	6.4k

Countries citing papers authored by O. Lahav

Since Specialization

Citations

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

Fields of papers citing papers by O. Lahav

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by O. Lahav. 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 O. Lahav. The network helps show where O. Lahav may publish in the future.

Co-authorship network of co-authors of O. Lahav

This figure shows the co-authorship network connecting the top 25 collaborators of O. Lahav. A scholar is included among the top collaborators of O. Lahav 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 O. Lahav. O. Lahav is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Ferreras, Ignacio, Marina Trevisan, O. Lahav, R. R. de Carvalho, & Joseph Silk. (2025). Is velocity dispersion the major driver of stellar population properties over sub-galaxy scales? An SDSS MaNGA IFU study. Monthly Notices of the Royal Astronomical Society. 540(1). 1069–1083.

2.

Joachimi, Benjamin, E. Charles, Patricia Larsen, et al.. (2024). Impact of survey spatial variability on galaxy redshift distributions and the cosmological 3 × 2-point statistics for the Rubin Legacy Survey of Space and Time (LSST). Monthly Notices of the Royal Astronomical Society. 535(4). 2970–2997. 1 indexed citations

3.

Ferreras, Ignacio, et al.. (2023). What drives the variance of galaxy spectra?. Monthly Notices of the Royal Astronomical Society. 526(1). 585–599. 2 indexed citations

4.

Hassanshahi, M. H., et al.. (2023). A quantum-enhanced support vector machine for galaxy classification. 2(1). 752–759. 3 indexed citations

5.

Thiyagalingam, Jeyan, et al.. (2022). Deep learning methods for obtaining photometric redshift estimations from images. Monthly Notices of the Royal Astronomical Society. 512(2). 1696–1709. 27 indexed citations

6.

Thiyagalingam, Jeyan, et al.. (2021). Benchmarking and scalability of machine-learning methods for photometric redshift estimation. Monthly Notices of the Royal Astronomical Society. 505(4). 4847–4856. 18 indexed citations

7.

Sadeh, I., F. B. Abdalla, & O. Lahav. (2019). ANNz2: Estimating photometric redshift and probability density functions using machine learning methods. ascl. 1 indexed citations

8.

Lahav, O.. (2018). A tribute to Donald Lynden-Bell. Observatory. 138. 261–263. 1 indexed citations

9.

Lochner, Michelle, et al.. (2016). PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING. The Astrophysical Journal Supplement Series. 225(2). 31–31. 99 indexed citations

10.

Agarwal, Shankar, F. B. Abdalla, Hume A. Feldman, O. Lahav, & Shaun A. Thomas. (2012). PkANN - I. Non-linear matter power spectrum interpolation through artificial neural networks. Monthly Notices of the Royal Astronomical Society. 424(2). 1409–1418. 40 indexed citations

11.

Lahav, O. & Adrian Collister. (2012). ANNz: Artificial Neural Networks for estimating photometric redshifts. ascl. 2 indexed citations

12.

Hobson, M. P., M. P. Hobson, John Skilling, et al.. (2009). Bayesian Methods in Cosmology. Cambridge University Press eBooks. 85 indexed citations

13.

Huchra, J. P., O. Lahav, Matthew Colless, et al.. (2008). The Dipole Anisotropy of the 2 Micron All-Sky Redshift Survey. 56 indexed citations

14.

Elgarøy, Ø., et al.. (2008). Neutrino mass, dark energy, and the linear growth factor. Physical review. D. Particles, fields, gravitation, and cosmology. 77(6). 32 indexed citations

15.

Lahav, O. & Ø. Elgarøy. (2004). Weighing neutrinos with large-scale structure. 2 indexed citations

16.

Lahav, O. & Yasushi Suto. (2004). Measuring our Universe from Galaxy Redshift Surveys. SHILAP Revista de lepidopterología. 7(1). 8–8. 21 indexed citations

17.

Elgarøy, Ø. & O. Lahav. (2003). The role of priors in deriving upper limits on neutrino masses from the 2dFGRS and WMAP. arXiv (Cornell University). 4 indexed citations

18.

Lahav, O., et al.. (1997). Wiener reconstruction of the IRAS 1.2-Jy galaxy redshift survey: cosmographical implications. Monthly Notices of the Royal Astronomical Society. 287(2). 425–444. 26 indexed citations

19.

Jahoda, K., R. F. Mushotzky, E. A. Boldt, & O. Lahav. (1991). Cross-correlation of the X-ray background with nearby galaxies. UCL Discovery (University College London). 23. 932. 4 indexed citations

20.

Lahav, O., D. Lynden–Bell, & S. F. Gull. (1988). From 2-D to 3-D by Maximum Entropy Method. Symposium - International Astronomical Union. 130. 559–559. 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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