James D. Neill

11.4k total citations
67 papers, 2.1k citations indexed

About

James D. Neill is a scholar working on Astronomy and Astrophysics, Instrumentation and Nuclear and High Energy Physics. According to data from OpenAlex, James D. Neill has authored 67 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 59 papers in Astronomy and Astrophysics, 20 papers in Instrumentation and 11 papers in Nuclear and High Energy Physics. Recurrent topics in James D. Neill's work include Galaxies: Formation, Evolution, Phenomena (33 papers), Gamma-ray bursts and supernovae (26 papers) and Astrophysical Phenomena and Observations (24 papers). James D. Neill is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (33 papers), Gamma-ray bursts and supernovae (26 papers) and Astrophysical Phenomena and Observations (24 papers). James D. Neill collaborates with scholars based in United States, France and United Kingdom. James D. Neill's co-authors include Robin Ciardullo, George H. Jacoby, Holland Ford, R. Michael Rich, Michael M. Shara, Mark Seibert, Patrick Morrissey, D. Christopher Martin, S. R. Kulkarni and T. H. Jarrett and has published in prestigious journals such as Nature, The Astrophysical Journal and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

James D. Neill

63 papers receiving 2.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
James D. Neill United States 25 2.0k 526 345 74 42 67 2.1k
K. A. Misselt United States 29 2.7k 1.3× 689 1.3× 286 0.8× 48 0.6× 42 1.0× 76 2.7k
А. В. Моисеев Russia 20 1.6k 0.8× 456 0.9× 202 0.6× 67 0.9× 19 0.5× 162 1.7k
P. Berlind United States 17 1.7k 0.8× 497 0.9× 261 0.8× 49 0.7× 19 0.5× 32 1.7k
A. Y. Kniazev Russia 27 2.5k 1.2× 1.0k 1.9× 211 0.6× 96 1.3× 40 1.0× 184 2.5k
Gaspar Galaz Chile 21 1.2k 0.6× 324 0.6× 346 1.0× 71 1.0× 23 0.5× 58 1.2k
Ian B. Thompson United States 21 1.9k 1.0× 684 1.3× 236 0.7× 79 1.1× 31 0.7× 48 2.0k
M. Calkins United States 15 1.5k 0.8× 448 0.9× 316 0.9× 44 0.6× 19 0.5× 42 1.6k
Steven Janowiecki United States 18 2.4k 1.2× 798 1.5× 515 1.5× 65 0.9× 52 1.2× 41 2.5k
Hideaki Maehara Japan 2 1.9k 0.9× 637 1.2× 215 0.6× 93 1.3× 16 0.4× 7 1.9k
Thomas Robitaille United States 32 3.6k 1.8× 479 0.9× 279 0.8× 46 0.6× 25 0.6× 72 3.7k

Countries citing papers authored by James D. Neill

Since Specialization
Citations

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

Fields of papers citing papers by James D. Neill

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by James D. Neill. 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 James D. Neill. The network helps show where James D. Neill may publish in the future.

Co-authorship network of co-authors of James D. Neill

This figure shows the co-authorship network connecting the top 25 collaborators of James D. Neill. A scholar is included among the top collaborators of James D. Neill 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 James D. Neill. James D. Neill 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.
Sharma, Y., A. Mahabal, J. Sollerman, et al.. (2025). CCSNscore: A Multi-input Deep Learning Tool for Classification of Core-collapse Supernovae Using SED-machine Spectra. Publications of the Astronomical Society of the Pacific. 137(3). 34507–34507. 2 indexed citations
2.
Kim, Young-Lo, I. Hook, L. Galbany, et al.. (2024). How Accurate are Transient Spectral Classification Tools?— A Study Using 4646 SEDMachine Spectra. Publications of the Astronomical Society of the Pacific. 136(11). 114501–114501. 1 indexed citations
3.
Cunningham, Tim, Ilaria Caiazzo, Jim Fuller, et al.. (2024). Expansion Properties of the Young Supernova Type Iax Remnant Pa 30 Revealed. The Astrophysical Journal Letters. 975(1). L7–L7. 2 indexed citations
4.
Daddi, E., R. Michael Rich, Francesco Valentino, et al.. (2022). Evidence for Cold-stream to Hot-accretion Transition as Traced by Lyα Emission from Groups and Clusters at 2 < z < 3.3. The Astrophysical Journal Letters. 926(2). L21–L21. 27 indexed citations
5.
Kim, Young-Lo, M. Rigault, James D. Neill, et al.. (2022). New Modules for the SEDMachine to Remove Contaminations from Cosmic Rays and Non-target Light: byecr and contsep. Publications of the Astronomical Society of the Pacific. 134(1032). 24505–24505. 11 indexed citations
6.
Kalita, Boris S., E. Daddi, Francesco Valentino, et al.. (2021). An Ancient Massive Quiescent Galaxy Found in a Gas-rich z ∼ 3 Group. The Astrophysical Journal Letters. 917(2). L17–L17. 20 indexed citations
7.
Szkody, Paula, Jan van Roestel, Anna Y. Q. Ho, et al.. (2021). Cataclysmic Variables in the Second Year of the Zwicky Transient Facility. The Astronomical Journal. 162(3). 94–94. 7 indexed citations
8.
Rigault, M., James D. Neill, N. Blagorodnova, et al.. (2019). Fully automated integral field spectrograph pipeline for the SEDMachine: pysedm. Springer Link (Chiba Institute of Technology). 15 indexed citations
9.
Gezari, Suvi, T. Hung, S. B. Cenko, et al.. (2017). iPTF Discovery of the Rapid “Turn-on” of a Luminous Quasar. The Astrophysical Journal. 835(2). 144–144. 75 indexed citations
10.
Gezari, Suvi, T. Hung, N. Blagorodnova, et al.. (2016). iPTF16fnl: Likely Tidal Disruption Event at 65 Mpc. CaltechAUTHORS (California Institute of Technology). 9433. 1.
11.
Martin, D. Christopher, Mateusz Matuszewski, Patrick Morrissey, et al.. (2015). A giant protogalactic disk linked to the cosmic web. Nature. 524(7564). 192–195. 44 indexed citations
12.
Sahai, R., James D. Neill, A. Gil de Paz, & C. Sánchez Contreras. (2011). Strong variable ultraviolet emission from y gem: accretion activity in an asymptotic giant branch star with a binary companion?. LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas). 13 indexed citations
13.
Treyer, M., Ted K. Wyder, James D. Neill, Mark Seibert, & Janice Lee. (2011). UP2010 : have observations revealed a variable upper end of the initial mass function? : proceedings of a conference held at Sedona, Arizona, USA, 20-25 June 2010. Astronomical Society of the Pacific eBooks.
14.
Treyer, M., et al.. (2011). UP2010: Have Observations Revealed a Variable Upper End of the Initial Mass Function?. ASPC. 440. 30 indexed citations
15.
Gezari, Suvi, Karl Förster, James D. Neill, & D. C. Martin. (2010). Serendipitous GALEX Detection of CSS100217:102913+404220. ATel. 2554. 1.
16.
Bronder, T. J., I. Hook, D. A. Howell, et al.. (2007). Quantitative Spectroscopy of Distant Type Ia Supernovae. AIP conference proceedings. 415–420. 1 indexed citations
17.
Neill, James D., et al.. (2007). The Mass Loss History of Mira. AAS. 211. 1 indexed citations
18.
Howell, D. A., M. Sullivan, P. Nugent, et al.. (2006). The type Ia supernova SNLS-03D3bb from a super-Chandrasekhar-mass white dwarf star. Nature. 443(7109). 308–311. 264 indexed citations
19.
Nugent, P., M. Sullivan, Richard S. Ellis, et al.. (2006). Toward a Cosmological Hubble Diagram for Type II‐P Supernovae. The Astrophysical Journal. 645(2). 841–850. 51 indexed citations
20.
Neill, James D.. (2005). Nova in the Small Magellanic Cloud 2005. International Astronomical Union Circular. 8594. 4. 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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