J. Dittmann
Impact in
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- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Quantum Chromodynamics and Particle Interactions
- Particle Detector Development and Performance
- Black Holes and Theoretical Physics
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- Anomaly Detection Techniques and Applications
- Algorithms and Data Compression
Papers in
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- Anomaly Detection Techniques and Applications 4
- Neural Networks and Applications 1
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- COVID-19 diagnosis using AI 3
- Co-authors
- V. Sorı́n (1 shared paper)V. D. Elvira (1 shared paper)Stephen Ellis (1 shared paper)S. Grinstein (1 shared paper)G. Blazey (1 shared paper)Dieter Zeppenfeld (1 shared paper)R. Piegaia (1 shared paper)R. Snihur (1 shared paper)
- Journals
- Sensors (2 papers)PHM Society European Conference (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesNorwayBelgium
In The Last Decade
J. Dittmann
5 papers receiving 48 citations
Peers
Comparison fields: 5 of 14
- Nuclear and High Energy Physics 47
- Artificial Intelligence 16
- History and Philosophy of Science 1
- Computational Theory and Mathematics 3
- Computer Networks and Communications 4
Countries citing papers authored by J. Dittmann
This map shows the geographic impact of J. Dittmann'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 J. Dittmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites J. Dittmann more than expected).
Fields of papers citing papers by J. Dittmann
This network shows the impact of papers produced by J. Dittmann. 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 J. Dittmann. The network helps show where J. Dittmann may publish in the future.
Co-authors
The 25 scholars most cited alongside J. Dittmann, 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 | Run II jet physics | 2000 | 43 |
| 2 | 2021 | 5 | |
| 3 | 2023 | 4 | |
| 4 | 2022 | 3 | |
| 5 | A Simple Introduction to Particle Physics. Part I - Foundations and the Standard Model | 2008 | 2 |
| 6 | 2025 | 0 | |
| 7 | 2024 | 0 |
About J. Dittmann
J. Dittmann is a scholar working on Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Computer Networks and Communications, Astronomy and Astrophysics and Control and Systems Engineering, having authored 7 papers that have together received 57 indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (4 papers), COVID-19 diagnosis using AI (3 papers), Particle Detector Development and Performance (1 paper), Time Series Analysis and Forecasting (1 paper), Neural Networks and Applications (1 paper), Aerodynamics and Acoustics in Jet Flows (1 paper), Fault Detection and Control Systems (1 paper) and Relativity and Gravitational Theory (1 paper). The work is most often cited by research in Nuclear and High Energy Physics (47 citations), Artificial Intelligence (16 citations), History and Philosophy of Science (1 citation), Computational Theory and Mathematics (3 citations) and Computer Networks and Communications (4 citations). J. Dittmann has collaborated with scholars based in United States, Norway and Belgium. Frequent co-authors include V. Sorı́n, V. D. Elvira, Stephen Ellis, S. Grinstein, G. Blazey, Dieter Zeppenfeld, R. Piegaia, R. Snihur, H. Schellman and R. Hirosky. Their work appears in journals such as Sensors, PHM Society European Conference 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.