Nima Chartab

1.4k total citations
17 papers, 112 citations indexed

About

Nima Chartab is a scholar working on Astronomy and Astrophysics, Instrumentation and Ecology. According to data from OpenAlex, Nima Chartab has authored 17 papers receiving a total of 112 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Astronomy and Astrophysics, 6 papers in Instrumentation and 2 papers in Ecology. Recurrent topics in Nima Chartab's work include Galaxies: Formation, Evolution, Phenomena (15 papers), Stellar, planetary, and galactic studies (8 papers) and Astronomy and Astrophysical Research (6 papers). Nima Chartab is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (15 papers), Stellar, planetary, and galactic studies (8 papers) and Astronomy and Astrophysical Research (6 papers). Nima Chartab collaborates with scholars based in United States, Chile and Japan. Nima Chartab's co-authors include Guillermo A. Blanc, Bahram Mobasher, Andrew B. Newman, Daniel D. Kelson, Gwen C. Rudie, David Sobral, Marziye Jafariyazani, Sérgio M. Santos, Ali Ahmad Khostovan and João Calhau 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

Nima Chartab

14 papers receiving 98 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nima Chartab United States 7 105 49 16 4 3 17 112
Clara Giménez-Arteaga Denmark 5 98 0.9× 56 1.1× 6 0.4× 4 1.0× 2 0.7× 5 103
Dritan Kodra Germany 3 94 0.9× 47 1.0× 13 0.8× 4 1.0× 4 101
Zihao Li China 7 151 1.4× 50 1.0× 25 1.6× 3 0.8× 2 0.7× 13 157
Gregor Rihtaršič United States 6 97 0.9× 54 1.1× 11 0.7× 2 0.5× 7 2.3× 11 104
Maximiliano Moyano Chile 6 82 0.8× 39 0.8× 8 0.5× 3 0.8× 2 0.7× 10 87
Ivana Barišić United States 7 118 1.1× 77 1.6× 7 0.4× 3 0.8× 3 1.0× 8 119
Yongming Liang Japan 8 119 1.1× 49 1.0× 32 2.0× 5 1.3× 1 0.3× 17 137
Satoshi Kikuta Japan 5 58 0.6× 26 0.5× 15 0.9× 3 0.8× 2 0.7× 13 59
Hansung B. Gim United States 4 101 1.0× 56 1.1× 19 1.2× 2 0.5× 1 0.3× 13 108
C. Vignali Italy 4 120 1.1× 44 0.9× 19 1.2× 4 1.0× 1 0.3× 5 124

Countries citing papers authored by Nima Chartab

Since Specialization
Citations

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

Fields of papers citing papers by Nima Chartab

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nima Chartab

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

All Works

17 of 17 papers shown
1.
Newman, Andrew B., Gwen C. Rudie, Nima Chartab, et al.. (2025). LATIS: A Sample of IGM-selected Protoclusters and Protogroups at z ∼ 2.5. The Astrophysical Journal. 988(1). 47–47. 2 indexed citations
2.
Chartab, Nima, Andrew B. Newman, Gwen C. Rudie, et al.. (2025). LATIS: Galaxy–Environment Relations at Cosmic Noon and the Role of Sample Selection. The Astrophysical Journal. 994(1). 106–106.
3.
Morishita, Takahiro, Zhaoran Liu, M. Stiavelli, et al.. (2025). Accelerated Emergence of Evolved Galaxies in Early Overdensities at z ∼ 5.7. The Astrophysical Journal. 982(2). 153–153. 6 indexed citations
4.
Chartab, Nima, et al.. (2025). Machine Learning Classification of COSMOS2020 Galaxies: Quiescent versus Star-forming. The Astrophysical Journal. 993(1). 123–123.
5.
Zonoozi, Akram Hasani, et al.. (2025). Leveraging Machine Learning for Accurate and Fast Stellar Mass Estimation of Galaxies. The Astrophysical Journal. 989(1). 65–65. 3 indexed citations
6.
Newman, Andrew B., Nima Chartab, Gwen C. Rudie, et al.. (2025). LATIS: Comparing Galaxy and IGM Tomography Maps as Tracers of Large-scale Structure and Protoclusters at z ∼ 2.5. The Astrophysical Journal. 988(1). 48–48. 2 indexed citations
7.
Taamoli, Sina, Bahram Mobasher, Nima Chartab, et al.. (2024). Large-scale Structures in COSMOS2020: Evolution of Star Formation Activity in Different Environments at 0.4 < z < 4. The Astrophysical Journal. 966(1). 18–18. 10 indexed citations
8.
Newman, Andrew B., Nima Chartab, Gwen C. Rudie, et al.. (2024). LATIS: Constraints on the Galaxy–Halo Connection at z ∼ 2.5 from Galaxy–Galaxy and Galaxy–Lyα Clustering. The Astrophysical Journal. 961(1). 27–27. 5 indexed citations
9.
Taamoli, Sina, Bahram Mobasher, Nima Chartab, et al.. (2024). COSMOS2020: Disentangling the Role of Mass and Environment in Star Formation Activity of Galaxies at 0.4 < z < 4. The Astrophysical Journal. 977(2). 263–263. 1 indexed citations
10.
Hemmati, Shoubaneh, Bahram Mobasher, Gabriela Canalizo, et al.. (2024). The Application of Manifold Learning to a Selection of Different Galaxy Populations and Scaling Relation Analysis. The Astrophysical Journal. 977(2). 202–202. 1 indexed citations
11.
Brinch, Malte, T. R. Greve, D. B. Sanders, et al.. (2023). DEIMOS spectroscopy of z = 6 protocluster candidate in COSMOS – a massive protocluster embedded in a large-scale structure?. Monthly Notices of the Royal Astronomical Society. 527(3). 6591–6615. 8 indexed citations
12.
Reddy, Naveen A., Michael W. Topping, Irene Shivaei, et al.. (2023). Exploring the correlation between Hα-to-UV ratio and burstiness for typical star-forming galaxies at z ∼ 2. Monthly Notices of the Royal Astronomical Society. 526(1). 1512–1527. 6 indexed citations
13.
Weaver, John R., L. Zalesky, Vasily Kokorev, et al.. (2023). The Farmer: A Reproducible Profile-fitting Photometry Package for Deep Galaxy Surveys. The Astrophysical Journal Supplement Series. 269(1). 20–20. 11 indexed citations
14.
Chartab, Nima, Andrew B. Newman, Gwen C. Rudie, Guillermo A. Blanc, & Daniel D. Kelson. (2023). LATIS: The Stellar Mass–Metallicity Relation of Star-forming Galaxies at z ∼ 2.5. The Astrophysical Journal. 960(1). 73–73. 14 indexed citations
15.
Chartab, Nima, Hooshang Nayyeri, Asantha Cooray, et al.. (2022). Massive Molecular Gas Reservoir in a Luminous Submillimeter Galaxy during Cosmic Noon. The Astrophysical Journal. 929(1). 41–41. 3 indexed citations
16.
Chartab, Nima, Asantha Cooray, Jingzhe Ma, et al.. (2022). Low gas-phase metallicities of ultraluminous infrared galaxies are a result of dust obscuration. Nature Astronomy. 6(7). 844–849. 13 indexed citations
17.
Khostovan, Ali Ahmad, David Sobral, Bahram Mobasher, et al.. (2019). The clustering of typical Ly α emitters from z ∼ 2.5–6: host halo masses depend on Ly α and UV luminosities. Monthly Notices of the Royal Astronomical Society. 489(1). 555–573. 27 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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