T. Britton
Impact in
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- Nuclear Physics and Applications
Papers in
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- Anomaly Detection Techniques and Applications 4
- Gaussian Processes and Bayesian Inference 2
- Adversarial Robustness in Machine Learning 1
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- Time Series Analysis and Forecasting 3
- Co-authors
- Dan Lu (3 shared papers)Lasitha Vidyaratne (2 shared papers)Malachi Schram (3 shared papers)Majdi I. Radaideh (3 shared papers)Chris H. Pappas (3 shared papers)Sarah Cousineau (3 shared papers)David Lawrence (5 shared papers)Kaukab Rajput (1 shared paper)
- Journals
- Journal of Instrumentation (2 papers)Machine Learning Science and Technology (1 paper)Digital Signal Processing (1 paper)SSRN Electronic Journal (1 paper)DOAJ (DOAJ: Directory of Open Access Journals) (1 paper)
- Partner nations
- United States
In The Last Decade
T. Britton
6 papers receiving 43 citations
Peers
Comparison fields: 5 of 29
- Computational Mathematics 1
- Radiation 10
- Medical Laboratory Technology 1
- Signal Processing 6
- Control and Systems Engineering 12
Countries citing papers authored by T. Britton
This map shows the geographic impact of T. Britton'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 T. Britton with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Britton more than expected).
Fields of papers citing papers by T. Britton
This network shows the impact of papers produced by T. Britton. 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 T. Britton. The network helps show where T. Britton may publish in the future.
Co-authors
The 10 scholars most cited alongside T. Britton, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 33 | |
| 2 | 2022 | 5 | |
| 3 | 2020 | 2 | |
| 4 | 2021 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2024 | 1 | |
| 7 | 2019 | 0 | |
| 8 | 2024 | 0 | |
| 9 | 2022 | 0 |
About T. Britton
T. Britton is a scholar working on Artificial Intelligence, Signal Processing, Nuclear and High Energy Physics, Computer Vision and Pattern Recognition and Computer Networks and Communications, having authored 9 papers that have together received 43 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (4 papers), Time Series Analysis and Forecasting (3 papers), Particle physics theoretical and experimental studies (2 papers), Digital Media Forensic Detection (2 papers), Particle Detector Development and Performance (2 papers), Gaussian Processes and Bayesian Inference (2 papers), Adversarial Robustness in Machine Learning (1 paper) and Quantum Chromodynamics and Particle Interactions (1 paper). The work is most often cited by research in Computational Mathematics (1 citation), Radiation (10 citations), Medical Laboratory Technology (1 citation), Signal Processing (6 citations) and Control and Systems Engineering (12 citations). T. Britton has collaborated with scholars based in United States. Frequent co-authors include Dan Lu, Lasitha Vidyaratne, Malachi Schram, Majdi I. Radaideh, Chris H. Pappas, Sarah Cousineau, David Lawrence, Kaukab Rajput, N. S. Jarvis and Michael A. Goodrich. Their work appears in journals such as Journal of Instrumentation, Machine Learning Science and Technology, Digital Signal Processing, SSRN Electronic Journal and DOAJ (DOAJ: Directory of Open Access Journals).
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.