Benjamin Nachman
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- Particle physics theoretical and experimental studies 97
- High-Energy Particle Collisions Research 47
- Particle Detector Development and Performance 44
- Neutrino Physics Research 13
- Quantum Chromodynamics and Particle Interactions 13
- Astrophysics and Cosmic Phenomena 13
- Artificial Intelligence top 1%
- Computational Physics and Python Applications 22
- Gaussian Processes and Bayesian Inference 12
- Radiation top 10%
Benjamin Nachman
119 papers receiving 2.7k citations
Hit Papers
Peers
Comparison fields: 5 of 88
- Nuclear and High Energy Physics 2.0k
- Artificial Intelligence 1.1k
- Statistical and Nonlinear Physics 121
- Radiation 83
- Information Systems and Management 63
Countries citing papers authored by Benjamin Nachman
This map shows the geographic impact of Benjamin Nachman'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 Benjamin Nachman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Benjamin Nachman more than expected).
Fields of papers citing papers by Benjamin Nachman
This network shows the impact of papers produced by Benjamin Nachman. 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 Benjamin Nachman. The network helps show where Benjamin Nachman may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Benjamin Nachman, 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 | 2025 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 1 | |
| 5 | 2025 | 11 | |
| 6 | 2024 | 3 | |
| 7 | 2024 | 17 | |
| 8 | 2024 | 17 | |
| 9 | 2024 | 10 | |
| 10 | 2024 | 2 | |
| 11 | 2024 | 1 | |
| 12 | 2023 | 28 | |
| 13 | 2023 | 46 | |
| 14 | 2023 | 28 | |
| 15 | 2023 | 16 | |
| 16 | 2023 | 14 | |
| 17 | 2022 | 63 | |
| 18 | 2021 | 8 | |
| 19 | Learning to Classify from Impure Samples | 2018 | 8 |
| 20 | CWoLa Hunting: Extending the Bump Hunt with Machine Learning | 2018 | 8 |
About Benjamin Nachman
Benjamin Nachman is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence, Radiation, Statistical and Nonlinear Physics and Discrete Mathematics and Combinatorics, having authored 127 papers that have together received 2.7k indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (97 papers), High-Energy Particle Collisions Research (47 papers), Particle Detector Development and Performance (44 papers), Computational Physics and Python Applications (22 papers), Neutrino Physics Research (13 papers), Quantum Chromodynamics and Particle Interactions (13 papers), Astrophysics and Cosmic Phenomena (13 papers) and Gaussian Processes and Bayesian Inference (12 papers). The work is most often cited by research in Nuclear and High Energy Physics (2.0k citations), Artificial Intelligence (1.1k citations), Statistical and Nonlinear Physics (121 citations), Radiation (83 citations) and Information Systems and Management (63 citations). Benjamin Nachman has collaborated with scholars based in United States, Germany and Switzerland. Frequent co-authors include David Shih, Luke de Oliveira, M. Paganini, V. M. Mikuni, Jesse Thaler, Eric Metodiev, Jack H. Collins, Kiel Howe, C. Bauer and Anders Andreassen. Their work appears in journals such as Physical review. D, Journal of High Energy Physics, Journal of Instrumentation, Physical Review Letters and Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment.
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.