Tom Charnock

940 citations
15 papers · 574 · h-index 10

Impact in

Papers in

Tom Charnock

15 papers receiving 557 citations

Peers

Tom Charnock
Comparison fields: 5 of 52
  • Astronomy and Astrophysics 468
  • Instrumentation 68
  • Nuclear and High Energy Physics 217
  • Statistics and Probability 35
  • Artificial Intelligence 119
Replace Stephen M. Feeney with:
Stephen M. Feeney United Kingdom
Florent Leclercq France
Émille E. O. Ishida France
Tomasz Kacprzak Switzerland
Boris Leistedt United Kingdom
Benjamin Giblin United Kingdom
Yin Li United States
Elise Jennings United States
Elena Sellentin Netherlands
José Manuel Zorrilla Matilla United States
Tom Charnock relative to Stephen M. Feeney United Kingdom Stephen M. Feeney's profile →
Citations per field
00.5×1.5×
Stephen M. Feeney · 1×
Citations per year

Countries citing papers authored by Tom Charnock

Since Specialization
Citations

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

Fields of papers citing papers by Tom Charnock

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Tom Charnock, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tom Charnock Line = papers co-authored together Tom Charnock links everyone, so they are left out of the graph.

All Works

15 of 15 papers shown
#Work
1 2019132
2 201597
3 201681
4 201873
5 202041
6 201735
7 202032
8 202228
9 201926
10 202015
11
Automatic physical inference
20184
12 20254
13 20193
14
supernovae: Photometric classification of supernovae
20172
15 20211

About Tom Charnock

Tom Charnock is a scholar working on Astronomy and Astrophysics, Computer Vision and Pattern Recognition, Artificial Intelligence, Nuclear and High Energy Physics and Applied Mathematics, having authored 15 papers that have together received 574 indexed citations. Recurring topics across this work include Galaxies: Formation, Evolution, Phenomena (10 papers), Cosmology and Gravitation Theories (4 papers), Advanced Vision and Imaging (3 papers), Advanced Image Processing Techniques (3 papers), Black Holes and Theoretical Physics (2 papers), Computational Physics and Python Applications (2 papers), Statistical and numerical algorithms (2 papers) and Gaussian Processes and Bayesian Inference (2 papers). The work is most often cited by research in Astronomy and Astrophysics (468 citations), Instrumentation (68 citations), Nuclear and High Energy Physics (217 citations), Statistics and Probability (35 citations) and Artificial Intelligence (119 citations). Tom Charnock has collaborated with scholars based in France, United States and United Kingdom. Frequent co-authors include B. D. Wandelt, A. Moss, Richard A. Battye, Guilhem Lavaux, Stephen M. Feeney, Justin Alsing, Anastasios Avgoustidis, Edmund J. Copeland, Francisco Villaescusa-Navarro and Berta Margalef-Bentabol. Their work appears in journals such as Monthly Notices of the Royal Astronomical Society, Physical review. D, Journal of Cosmology and Astroparticle Physics, EPJ Web of Conferences and arXiv (Cornell University).

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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