R. Armstrong

10.2k total citations
24 papers, 779 citations indexed

About

R. Armstrong is a scholar working on Astronomy and Astrophysics, Instrumentation and Electrical and Electronic Engineering. According to data from OpenAlex, R. Armstrong has authored 24 papers receiving a total of 779 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Astronomy and Astrophysics, 10 papers in Instrumentation and 3 papers in Electrical and Electronic Engineering. Recurrent topics in R. Armstrong's work include Galaxies: Formation, Evolution, Phenomena (13 papers), Astronomy and Astrophysical Research (10 papers) and Gamma-ray bursts and supernovae (7 papers). R. Armstrong is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (13 papers), Astronomy and Astrophysical Research (10 papers) and Gamma-ray bursts and supernovae (7 papers). R. Armstrong collaborates with scholars based in United States, Japan and Germany. R. Armstrong's co-authors include G. M. Bernstein, James Bosch, Hironao Miyatake, Rachel Mandelbaum, Melanie Simet, J. Meyers, P. Melchior, Masayuki Tanaka, Satoshi Miyazaki and Alexie Leauthaud and has published in prestigious journals such as The Astrophysical Journal, Monthly Notices of the Royal Astronomical Society and Astronomy and Astrophysics.

In The Last Decade

R. Armstrong

22 papers receiving 747 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
R. Armstrong United States 11 655 242 167 115 107 24 779
Mike Jarvis United States 13 667 1.0× 241 1.0× 180 1.1× 88 0.8× 125 1.2× 32 785
James Bosch United States 7 473 0.7× 186 0.8× 126 0.8× 83 0.7× 72 0.7× 10 560
J. Meyers United States 8 467 0.7× 195 0.8× 120 0.7× 65 0.6× 166 1.6× 29 603
S. L. Bridle United Kingdom 5 682 1.0× 246 1.0× 115 0.7× 43 0.4× 145 1.4× 6 743
Anupreeta More Japan 19 970 1.5× 393 1.6× 155 0.9× 45 0.4× 124 1.2× 57 1.1k
J. Zuntz United Kingdom 13 742 1.1× 197 0.8× 114 0.7× 79 0.7× 283 2.6× 29 829
Melanie Simet United States 14 942 1.4× 392 1.6× 166 1.0× 102 0.9× 338 3.2× 19 1.1k
R. Scaramella Italy 17 908 1.4× 356 1.5× 128 0.8× 59 0.5× 179 1.7× 56 996
G. Vernardos Netherlands 17 653 1.0× 253 1.0× 171 1.0× 56 0.5× 56 0.5× 40 754
G. Covone Italy 23 1.3k 2.0× 535 2.2× 158 0.9× 73 0.6× 325 3.0× 73 1.4k

Countries citing papers authored by R. Armstrong

Since Specialization
Citations

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

Fields of papers citing papers by R. Armstrong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of R. Armstrong

This figure shows the co-authorship network connecting the top 25 collaborators of R. Armstrong. A scholar is included among the top collaborators of R. Armstrong 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 R. Armstrong. R. Armstrong 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.
Thomas, Patrick, et al.. (2024). Developing a Paired Whole Genome Sequencing Service for Children With Cancer. Clinical Oncology. 38. 103623–103623.
2.
Wood‐Vasey, W. M., et al.. (2024). Testing the LSST Difference Image Analysis Pipeline Using Synthetic Source Injection Analysis. The Astrophysical Journal. 967(1). 10–10. 2 indexed citations
3.
Schneider, M., et al.. (2022). Star–Galaxy Image Separation with Computationally Efficient Gaussian Process Classification. The Astronomical Journal. 163(4). 148–148. 10 indexed citations
4.
Buchanan, J., et al.. (2022). Gaussian Process Classification for Galaxy Blend Identification in LSST. The Astrophysical Journal. 924(2). 94–94. 7 indexed citations
5.
Golovich, Nathan, et al.. (2021). A Search for L4 Earth Trojan Asteroids Using a Novel Track-before-detect Multiepoch Pipeline. The Astronomical Journal. 161(6). 282–282. 5 indexed citations
6.
Namikawa, Toshiya, Y. Chinone, Hironao Miyatake, et al.. (2019). Evidence for the cross-correlation between cosmic icrowave background polarization lensing from Polarbear and cosmic shear from Subaru Hyper Suprime-Cam. Figshare. 10 indexed citations
7.
8.
Hamana, Takashi, Masato Shirasaki, Satoshi Miyazaki, et al.. (2019). Cosmological constraints from cosmic shear two-point correlation functions with HSC survey first-year data. Publications of the Astronomical Society of Japan. 72(1). 164 indexed citations
9.
Mandelbaum, Rachel, François Lanusse, Alexie Leauthaud, et al.. (2018). Weak lensing shear calibration with simulations of the HSC survey. Monthly Notices of the Royal Astronomical Society. 481(3). 3170–3195. 80 indexed citations
10.
Bernstein, G. M., T. M. C. Abbott, R. Armstrong, et al.. (2018). Photometric Characterization of the Dark Energy Camera. Publications of the Astronomical Society of the Pacific. 130(987). 54501–54501. 4 indexed citations
11.
Bernstein, G. M., R. Armstrong, C. Krawiec, & M. March. (2016). An accurate and practical method for inference of weak gravitational lensing from galaxy images. Monthly Notices of the Royal Astronomical Society. 459(4). 4467–4484. 37 indexed citations
12.
Guo, Wei, Andries Potgieter, David Jordan, et al.. (2016). Automatic detecting and counting of sorghum heads in breeding field using RGB imagery from UAV.. 1–5. 5 indexed citations
13.
Rowe, Barnaby, Mike Jarvis, Rachel Mandelbaum, et al.. (2015). GalSim: The modular galaxy image simulation toolkit. Astronomy and Computing. 10. 121–150. 216 indexed citations
14.
Bernstein, G. M. & R. Armstrong. (2014). Bayesian lensing shear measurement. Monthly Notices of the Royal Astronomical Society. 438(2). 1880–1893. 45 indexed citations
15.
Mohr, J. J., R. Armstrong, E. Bertin, et al.. (2012). The Dark Energy Survey data processing and calibration system. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 8451. 84510D–84510D. 12 indexed citations
16.
Desai, S., R. Armstrong, J. J. Mohr, et al.. (2012). THE BLANCO COSMOLOGY SURVEY: DATA ACQUISITION, PROCESSING, CALIBRATION, QUALITY DIAGNOSTICS, AND DATA RELEASE. The Astrophysical Journal. 757(1). 83–83. 77 indexed citations
17.
Santiago, B. X., L. Girardi, J. I. B. Camargo, et al.. (2011). THE DARK ENERGY SURVEY: PROSPECTS FOR RESOLVED STELLAR POPULATIONS. The Astronomical Journal. 141(6). 185–185. 9 indexed citations
18.
Buckley‐Geer, E., H. Lin, E. Drabek-Maunder, et al.. (2011). THE SERENDIPITOUS OBSERVATION OF A GRAVITATIONALLY LENSED GALAXY ATz= 0.9057 FROM THE BLANCO COSMOLOGY SURVEY: THE ELLIOT ARC. The Astrophysical Journal. 742(1). 48–48. 6 indexed citations
19.
Šuhada, R., Jie Song, H. Böhringer, et al.. (2010). XMM-Newton detection of two clusters of galaxies with strong SPT Sunyaev-Zel'dovich effect signatures. Astronomy and Astrophysics. 514. L3–L3. 8 indexed citations
20.
White, Mark, Gareth D. Padfield, & R. Armstrong. (2004). Progress in Measuring Simulation Fidelity using an Adaptive Pilot Model. 4 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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