Yu Lu

3.4k total citations
42 papers, 1.6k citations indexed

About

Yu Lu is a scholar working on Astronomy and Astrophysics, Instrumentation and Nuclear and High Energy Physics. According to data from OpenAlex, Yu Lu has authored 42 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Astronomy and Astrophysics, 19 papers in Instrumentation and 7 papers in Nuclear and High Energy Physics. Recurrent topics in Yu Lu's work include Galaxies: Formation, Evolution, Phenomena (35 papers), Astronomy and Astrophysical Research (19 papers) and Stellar, planetary, and galactic studies (10 papers). Yu Lu is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (35 papers), Astronomy and Astrophysical Research (19 papers) and Stellar, planetary, and galactic studies (10 papers). Yu Lu collaborates with scholars based in United States, China and Italy. Yu Lu's co-authors include H. J. Mo, Martin D. Weinberg, Neal Katz, Risa H. Wechsler, Henry C. Ferguson, Peter Behroozi, Rachel S. Somerville, Avishai Dekel, Mark Dickinson and Yicheng Guo and has published in prestigious journals such as Nature, The Astrophysical Journal and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

Yu Lu

40 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yu Lu United States 23 1.3k 754 227 116 79 42 1.6k
Daizhong Liu Germany 23 1.4k 1.0× 574 0.8× 233 1.0× 235 2.0× 115 1.5× 103 1.7k
E. Pellegrini Germany 23 1.1k 0.8× 134 0.2× 190 0.8× 37 0.3× 139 1.8× 56 1.5k
J. M. Kovac United States 12 941 0.7× 25 0.0× 559 2.5× 39 0.3× 178 2.3× 35 1.3k
E. Molinari Italy 11 308 0.2× 105 0.1× 76 0.3× 250 2.2× 306 3.9× 89 658
Salvatore Esposito Italy 16 495 0.4× 5 0.0× 600 2.6× 30 0.3× 260 3.3× 79 1.0k
A. Nomerotski United States 19 42 0.0× 176 0.2× 253 1.1× 227 2.0× 533 6.7× 90 1.2k
D. A. Diver United Kingdom 15 351 0.3× 10 0.0× 84 0.4× 123 1.1× 121 1.5× 63 639
Jeremy Sage United States 22 178 0.1× 80 0.1× 40 0.2× 384 3.3× 1.7k 21.6× 47 2.1k
Emma E. Wollman United States 18 253 0.2× 236 0.3× 33 0.1× 707 6.1× 954 12.1× 43 1.5k
Karel Van Acoleyen Belgium 21 334 0.3× 84 0.1× 585 2.6× 700 6.0× 1.1k 13.8× 47 2.0k

Countries citing papers authored by Yu Lu

Since Specialization
Citations

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

Fields of papers citing papers by Yu Lu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yu Lu

This figure shows the co-authorship network connecting the top 25 collaborators of Yu Lu. A scholar is included among the top collaborators of Yu Lu 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 Yu Lu. Yu Lu 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.
Kanarik, Keren J., Wojciech T. Osowiecki, Yu Lu, et al.. (2023). Human–machine collaboration for improving semiconductor process development. Nature. 616(7958). 707–711. 67 indexed citations
2.
Karagodin, Ilya, et al.. (2020). Identifying Phenogroups in patients with subclinical diastolic dysfunction using unsupervised statistical learning. BMC Cardiovascular Disorders. 20(1). 367–367. 15 indexed citations
3.
Benson, Andrew, Christoph Behrens, & Yu Lu. (2020). A random-walk model for dark matter halo spins. Monthly Notices of the Royal Astronomical Society. 496(3). 3371–3380. 9 indexed citations
4.
Blanc, Guillermo A., Yu Lu, Andrew Benson, Antonios Katsianis, & Marcelo Barraza-Alfaro. (2019). A Characteristic Mass Scale in the Mass–Metallicity Relation of Galaxies. The Astrophysical Journal. 877(1). 6–6. 31 indexed citations
5.
Lu, Yu, Andrew Benson, Andrew Wetzel, et al.. (2017). The Importance of Preventive Feedback: Inference from Observations of the Stellar Masses and Metallicities of Milky Way Dwarf Galaxies. The Astrophysical Journal. 846(1). 66–66. 23 indexed citations
6.
Zhou, Hongyan, et al.. (2017). VizieR Online Data Catalog: Narrow line Seyfert 1 galaxies from SDSS-DR3 (Zhou+, 2006). 1 indexed citations
7.
Nierenberg, Anna, Tommaso Treu, N. Menci, et al.. (2016). The missing satellite problem in 3D. Monthly Notices of the Royal Astronomical Society. 462(4). 4473–4481. 20 indexed citations
8.
Behroozi, Peter, Guangtun Zhu, Henry C. Ferguson, et al.. (2015). Using galaxy pairs to probe star formation during major halo mergers. Monthly Notices of the Royal Astronomical Society. 450(2). 1546–1564. 22 indexed citations
9.
Salmon, Brett, Casey Papovich, Steven L. Finkelstein, et al.. (2015). THE RELATION BETWEEN STAR FORMATION RATE AND STELLAR MASS FOR GALAXIES AT 3.5 ⩽z⩽ 6.5 IN CANDELS. The Astrophysical Journal. 799(2). 183–183. 159 indexed citations
10.
Salmon, Brett, Casey Papovich, Steven L. Finkelstein, et al.. (2014). The Star-Formation Rate and Stellar Mass Relation of Galaxies at 3.5 $\le z\le$ 6.5 in CANDELS. arXiv (Cornell University).
11.
Sommariva, V., A. Fontana, A. Lamastra, et al.. (2014). A mass threshold in the number density of passive galaxies atz~ 2. Astronomy and Astrophysics. 571. A99–A99. 2 indexed citations
12.
Lu, Yu, et al.. (2014). Estimating stellar atmospheric parameters based on Lasso features. Research in Astronomy and Astrophysics. 14(4). 423–432. 4 indexed citations
13.
Behroozi, Peter, Risa H. Wechsler, Yu Lu, et al.. (2014). MERGERS AND MASS ACCRETION FOR INFALLING HALOS BOTH END WELL OUTSIDE CLUSTER VIRIAL RADII. The Astrophysical Journal. 787(2). 156–156. 87 indexed citations
14.
Cassata, P., Mauro Giavalisco, Christina C. Williams, et al.. (2013). CONSTRAINING THE ASSEMBLY OF NORMAL AND COMPACT PASSIVELY EVOLVING GALAXIES FROM REDSHIFTz= 3 TO THE PRESENT WITH CANDELS. The Astrophysical Journal. 775(2). 106–106. 64 indexed citations
15.
Nierenberg, Anna, Tommaso Treu, N. Menci, Yu Lu, & Wenting Wang. (2013). THE COSMIC EVOLUTION OF FAINT SATELLITE GALAXIES AS A TEST OF GALAXY FORMATION AND THE NATURE OF DARK MATTER. The Astrophysical Journal. 772(2). 146–146. 29 indexed citations
16.
Alberts, Stacey, G. W. Wilson, Yu Lu, et al.. (2013). Submm/mm galaxy counterpart identification using a characteristic density distribution. Monthly Notices of the Royal Astronomical Society. 431(1). 194–209. 5 indexed citations
17.
Lu, Yu, Dušan Kereš, Neal Katz, et al.. (2011). On the algorithms of radiative cooling in semi-analytic models. Monthly Notices of the Royal Astronomical Society. no–no. 24 indexed citations
18.
Lu, Yu & H. J. Mo. (2007). The accretion and cooling of pre-heated gas in dark matter haloes. Monthly Notices of the Royal Astronomical Society. 377(2). 617–629. 17 indexed citations
19.
Choi, Jun H., Yu Lu, H. J. Mo, & Martin D. Weinberg. (2006). Dark matter halo response to the disc growth. Monthly Notices of the Royal Astronomical Society. 372(4). 1869–1874. 10 indexed citations
20.
Lu, Yu, et al.. (2001). Black hole mass and velocity dispersion of narrow line region in active galactic nuclei and narrow line Seyfert 1 galaxies. Springer Link (Chiba Institute of Technology). 75 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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