Tom Charnock

910 citations
15 papers · 566 indexed · h-index 10

Impact in

Papers in

Tom Charnock

15 papers receiving 551 citations

Peers

Tom Charnock
Comparison fields: 5 of 52
  • Astronomy and Astrophysics 464
  • Instrumentation 72
  • Nuclear and High Energy Physics 211
  • Statistics and Probability 36
  • Artificial Intelligence 120
Replace Stephen M. Feeney with:
Stephen M. Feeney United Kingdom
Émille E. O. Ishida France
Florent Leclercq France
Tomasz Kacprzak Switzerland
Boris Leistedt United Kingdom
Benjamin Giblin United Kingdom
Yin Li United States
José Manuel Zorrilla Matilla United States
Elena Sellentin Netherlands
Elena Massara Canada
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 22 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 2019130
2 201594
3 201680
4 201873
5 202041
6 201735
7 202031
8 202228
9 201925
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 566 indexed citations. Recurring topics across this work include Galaxies: Formation, Evolution, Phenomena (10 papers), Cosmology and Gravitation Theories (4 papers), Advanced Image Processing Techniques (3 papers), Advanced Vision and Imaging (3 papers), Computational Physics and Python Applications (2 papers), Black Holes and Theoretical Physics (2 papers), Statistical and numerical algorithms (2 papers) and Computer Graphics and Visualization Techniques (2 papers). The work is most often cited by research in Astronomy and Astrophysics (464 citations), Instrumentation (72 citations), Nuclear and High Energy Physics (211 citations), Statistics and Probability (36 citations) and Artificial Intelligence (120 citations). Tom Charnock has collaborated with scholars based in France, United Kingdom and United States. Frequent co-authors include B. D. Wandelt, A. Moss, Richard A. Battye, Guilhem Lavaux, Justin Alsing, Stephen M. Feeney, Edmund J. Copeland, Anastasios Avgoustidis, Francisco Villaescusa-Navarro and Alan Heavens. Their work appears in journals such as Physical review. D, Monthly Notices of the Royal Astronomical Society, Journal of Cosmology and Astroparticle Physics, SHILAP Revista de lepidopterología 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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