G. Cowan

27.6k total citations
12 papers, 541 citations indexed

About

G. Cowan is a scholar working on Nuclear and High Energy Physics, Computer Networks and Communications and Statistics and Probability. According to data from OpenAlex, G. Cowan has authored 12 papers receiving a total of 541 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Nuclear and High Energy Physics, 2 papers in Computer Networks and Communications and 2 papers in Statistics and Probability. Recurrent topics in G. Cowan's work include Particle physics theoretical and experimental studies (5 papers), Particle Detector Development and Performance (3 papers) and Statistical Methods and Bayesian Inference (2 papers). G. Cowan is often cited by papers focused on Particle physics theoretical and experimental studies (5 papers), Particle Detector Development and Performance (3 papers) and Statistical Methods and Bayesian Inference (2 papers). G. Cowan collaborates with scholars based in United Kingdom, United States and Mexico. G. Cowan's co-authors include José G. Ramírez, Siegmund Brandt, Carole Adam, Cécile Germain‐Renaud, B. Kégl, Isabelle Guyon, D. Rousseau, William J. Owen, Isabelle Guyon and Cécile Germain and has published in prestigious journals such as SHILAP Revista de lepidopterología, Technometrics and Physics Today.

In The Last Decade

G. Cowan

10 papers receiving 528 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
G. Cowan United Kingdom	5	176	71	52	50	42	12	541
Siegmund Brandt Germany	9	61 0.3×	66 0.9×	61 1.2×	44 0.9×	110 2.6×	25	650
Byron P. Roe United States	15	393 2.2×	37 0.5×	103 2.0×	45 0.9×	58 1.4×	26	710
David Halliday United States	10	71 0.4×	45 0.6×	29 0.6×	59 1.2×	102 2.4×	33	598
D. Rousseau France	9	154 0.9×	14 0.2×	103 2.0×	25 0.5×	27 0.6×	34	541
I. Lupelli United Kingdom	17	383 2.2×	109 1.5×	42 0.8×	32 0.6×	25 0.6×	61	647
T. Craciunescu Romania	13	292 1.7×	37 0.5×	48 0.9×	242 4.8×	84 2.0×	96	671
Kevin Brown United States	17	195 1.1×	56 0.8×	130 2.5×	133 2.7×	75 1.8×	124	937
Xiaohui Song China	17	398 2.3×	263 3.7×	99 1.9×	12 0.2×	95 2.3×	51	696
Kyungmin Kim South Korea	12	39 0.2×	250 3.5×	51 1.0×	16 0.3×	64 1.5×	35	566
Christoph Überhuber Austria	4	62 0.4×	41 0.6×	27 0.5×	9 0.2×	119 2.8×	7	634

Countries citing papers authored by G. Cowan

Since Specialization

Citations

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

Fields of papers citing papers by G. Cowan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of G. Cowan

This figure shows the co-authorship network connecting the top 25 collaborators of G. Cowan. A scholar is included among the top collaborators of G. Cowan 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 G. Cowan. G. Cowan 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:

12 of 12 papers shown

1.

Canonero, Enzo & G. Cowan. (2025). Correlated systematic uncertainties and errors-on-errors in measurement combinations with an application to the 7–8 TeV ATLAS–CMS top quark mass combination. The European Physical Journal C. 85(2). 156–156. 1 indexed citations

2.

Canonero, Enzo, Alessandra Rosalba Brazzale, & G. Cowan. (2023). Higher-order asymptotic corrections and their application to the Gamma Variance Model. The European Physical Journal C. 83(12). 2 indexed citations

3.

Cowan, G.. (2022). Effect of Systematic Uncertainty Estimation on the Muong- 2 Anomaly. SHILAP Revista de lepidopterología. 258. 9002–9002. 4 indexed citations

4.

Adam, Carole, G. Cowan, Cécile Germain‐Renaud, et al.. (2015). The Higgs Machine Learning Challenge. Journal of Physics Conference Series. 664(7). 72015–72015. 51 indexed citations

5.

Cowan, G., D. Rousseau, & C. Adam Bourdarios. (2015). The ATLAS Higgs Machine Learning Challenge. CERN Document Server (European Organization for Nuclear Research).

6.

Cowan, G., Cécile Germain, Isabelle Guyon, Balázs Kégl, & David Rousseau. (2014). Proceedings of the 2014 International Conference on High-Energy Physics and Machine Learning - Volume 42. 4 indexed citations

7.

Cowan, G., K. Cranmer, Eilam Gross, & O. Vitells. (2010). Using the Profile Likelihood in Searches for New Physics. arXiv (Cornell University).

8.

Cowan, G.. (2007). BAYESIAN STATISTICAL METHODS FOR PARTON ANALYSES. 157–160. 1 indexed citations

9.

Cowan, G.. (2007). Data analysis: Frequently Bayesian. Physics Today. 60(4). 82–83. 3 indexed citations

10.

Owen, William J. & G. Cowan. (2000). Statistical Data Analysis. Technometrics. 42(3). 311–311. 13 indexed citations

11.

Ramírez, José G., et al.. (2000). Data Analysis: Statistical and Computational Methods for Scientists and Engineers. Technometrics. 42(3). 312–312. 80 indexed citations

12.

Cowan, G.. (1998). Statistical Data Analysis. 382 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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