D. Sengupta
Impact in
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- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Particle Detector Development and Performance
- Quantum Chromodynamics and Particle Interactions
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- Computational Physics and Python Applications
Papers in
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- Privacy-Preserving Technologies in Data 1
- Computational Physics and Python Applications 1
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- Particle physics theoretical and experimental studies 2
- Quantum Chromodynamics and Particle Interactions 1
- High-Energy Particle Collisions Research 1
- Co-authors
- T. Golling (5 shared papers)J. A. Raine (4 shared papers)Matthew Leigh (3 shared papers)G. Quétant (2 shared papers)David Shih (1 shared paper)Benjamin Nachman (1 shared paper)
- Journals
- Physical review. D (2 papers)Monthly Notices of the Royal Astronomical Society (1 paper)Journal of High Energy Physics (1 paper)SciPost Physics (1 paper)
- Partner nations
- SwitzerlandUnited StatesFrance
In The Last Decade
D. Sengupta
4 papers receiving 49 citations
Peers
Comparison fields: 5 of 17
- Nuclear and High Energy Physics 36
- Artificial Intelligence 13
- Statistics, Probability and Uncertainty 2
- Instrumentation 1
- Applied Mathematics 3
Countries citing papers authored by D. Sengupta
This map shows the geographic impact of D. Sengupta'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 D. Sengupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites D. Sengupta more than expected).
Fields of papers citing papers by D. Sengupta
This network shows the impact of papers produced by D. Sengupta. 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 D. Sengupta. The network helps show where D. Sengupta may publish in the future.
Co-authors
The 6 scholars most cited alongside D. Sengupta, 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 | 2024 | 24 | |
| 2 | 2024 | 16 | |
| 3 | 2024 | 5 | |
| 4 | 2024 | 4 | |
| 5 | 2025 | 0 |
About D. Sengupta
D. Sengupta is a scholar working on Artificial Intelligence, Nuclear and High Energy Physics, Information Systems and Management, Signal Processing and Management Science and Operations Research, having authored 5 papers that have together received 49 indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (2 papers), Privacy-Preserving Technologies in Data (1 paper), Blind Source Separation Techniques (1 paper), Simulation Techniques and Applications (1 paper), Quantum Chromodynamics and Particle Interactions (1 paper), Big Data Technologies and Applications (1 paper), High-Energy Particle Collisions Research (1 paper) and Computational Physics and Python Applications (1 paper). The work is most often cited by research in Nuclear and High Energy Physics (36 citations), Artificial Intelligence (13 citations), Statistics, Probability and Uncertainty (2 citations), Instrumentation (1 citation) and Applied Mathematics (3 citations). D. Sengupta has collaborated with scholars based in Switzerland, United States and France. Frequent co-authors include T. Golling, J. A. Raine, Matthew Leigh, G. Quétant, David Shih and Benjamin Nachman. Their work appears in journals such as Physical review. D, Monthly Notices of the Royal Astronomical Society, Journal of High Energy Physics and SciPost Physics.
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.