Benjamin Nachman

18.7k total citations · 1 hit paper
127 papers, 2.7k citations indexed

About

Benjamin Nachman is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence and Radiation. According to data from OpenAlex, Benjamin Nachman has authored 127 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 102 papers in Nuclear and High Energy Physics, 39 papers in Artificial Intelligence and 8 papers in Radiation. Recurrent topics in Benjamin Nachman's work include Particle physics theoretical and experimental studies (97 papers), High-Energy Particle Collisions Research (47 papers) and Particle Detector Development and Performance (44 papers). Benjamin Nachman is often cited by papers focused on Particle physics theoretical and experimental studies (97 papers), High-Energy Particle Collisions Research (47 papers) and Particle Detector Development and Performance (44 papers). Benjamin Nachman collaborates with scholars based in United States, Germany and Switzerland. Benjamin Nachman's 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 and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Reviews of Modern Physics.

In The Last Decade

Benjamin Nachman

119 papers receiving 2.7k citations

Hit Papers

CaloGAN: Simulating 3D high energy particle showers in mu... 2018 2026 2020 2023 2018 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. CaloGAN: Simulating 3D high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks
    2018 195 citations Benjamin Nachman et al. Physical review. D profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Benjamin Nachman United States 27 2.0k 1.1k 190 161 130 127 2.7k
M. Pierini Switzerland 27 2.2k 1.1× 449 0.4× 64 0.3× 126 0.8× 67 0.5× 104 2.6k
J. Vega Spain 21 1.1k 0.6× 484 0.4× 148 0.8× 101 0.6× 41 0.3× 228 2.0k
Jesse Thaler United States 36 4.6k 2.3× 498 0.4× 325 1.7× 58 0.4× 75 0.6× 117 4.9k
D. Whiteson United States 18 994 0.5× 337 0.3× 64 0.3× 45 0.3× 51 0.4× 59 1.3k
David Shih United States 28 2.2k 1.1× 506 0.4× 77 0.4× 57 0.4× 35 0.3× 77 2.8k
Tilman Plehn Germany 49 7.1k 3.6× 570 0.5× 112 0.6× 54 0.3× 45 0.3× 171 7.3k
Nils Strodthoff Germany 25 1.2k 0.6× 258 0.2× 156 0.8× 56 0.3× 109 0.8× 61 2.5k
Peter Skands Switzerland 22 9.2k 4.7× 474 0.4× 107 0.6× 39 0.2× 73 0.6× 78 9.4k
Andrei Alexandru United States 37 2.6k 1.3× 233 0.2× 654 3.4× 41 0.3× 17 0.1× 137 3.8k
S. Mrenna United States 23 9.5k 4.9× 490 0.4× 117 0.6× 35 0.2× 74 0.6× 60 9.7k

Countries citing papers authored by Benjamin Nachman

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Benjamin Nachman

This figure shows the co-authorship network connecting the top 25 collaborators of Benjamin Nachman. A scholar is included among the top collaborators of Benjamin Nachman based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Benjamin Nachman. Benjamin Nachman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Nachman, Benjamin & D. Noll. (2025). Neural refinement of sample weights. Physical review. D. 112(9).
2.
Krämer, Michael, et al.. (2025). Fundamental limit of jet tagging. Physical review. D. 112(9). 1 indexed citations
3.
Nachman, Benjamin, et al.. (2025). Optimizers for stabilizing likelihood-free inference. Physical review. D. 112(9).
4.
Mikuni, V. M., et al.. (2025). Tools for unbinned unfolding. Journal of Instrumentation. 20(5). P05034–P05034. 1 indexed citations
5.
Shmakov, Alexander, Sascha Diefenbacher, V. M. Mikuni, et al.. (2025). The landscape of unfolding with machine learning. SciPost Physics. 18(2). 11 indexed citations
6.
Bright-Thonney, S., Benjamin Nachman, & Jesse Thaler. (2024). Infrared-safe energy weighting does not guarantee small nonperturbative effects. Physical review. D. 110(1). 3 indexed citations
7.
Acosta, Fernando Torales, V. M. Mikuni, Benjamin Nachman, et al.. (2024). Comparison of point cloud and image-based models for calorimeter fast simulation. Journal of Instrumentation. 19(5). P05003–P05003. 17 indexed citations
8.
Mikuni, V. M. & Benjamin Nachman. (2024). High-dimensional and permutation invariant anomaly detection. SciPost Physics. 16(3). 17 indexed citations
9.
Pettee, M., et al.. (2024). Learning likelihood ratios with neural network classifiers. Journal of High Energy Physics. 2024(2). 10 indexed citations
10.
Kobylianskii, D., Nathalie Soybelman, N. Kakati, et al.. (2024). Advancing set-conditional set generation: Diffusion models for fast simulation of reconstructed particles. Physical review. D. 110(9). 2 indexed citations
11.
Dreyer, E., E. Gross, D. Kobylianskii, et al.. (2024). Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction. Physical Review Letters. 133(21). 211902–211902. 1 indexed citations
12.
Bauer, C., et al.. (2023). Quantum anomaly detection for collider physics. Journal of High Energy Physics. 2023(2). 220–220. 28 indexed citations
13.
Mikuni, V. M., Benjamin Nachman, & M. Pettee. (2023). Fast point cloud generation with diffusion models in high energy physics. Physical review. D. 108(3). 46 indexed citations
14.
Golling, T., et al.. (2023). Flow-enhanced transportation for anomaly detection. Physical review. D. 107(9). 28 indexed citations
15.
Kasieczka, Gregor, et al.. (2023). Anomaly detection under coordinate transformations. Physical review. D. 107(1). 16 indexed citations
16.
Nachman, Benjamin & Ramon Winterhalder. (2023). Elsa: enhanced latent spaces for improved collider simulations. The European Physical Journal C. 83(9). 14 indexed citations
17.
Isaacson, Joshua, Gregor Kasieczka, Claudius Krause, et al.. (2022). Classifying anomalies through outer density estimation. Physical review. D. 106(5). 63 indexed citations
18.
Terashi, K., M. Saito, C. Bauer, et al.. (2021). Quantum Gate Pattern Recognition and Circuit Optimization for Scientific Applications. SHILAP Revista de lepidopterología. 8 indexed citations
19.
Komiske, Patrick, Eric Metodiev, Benjamin Nachman, & Matthew D. Schwartz. (2018). Learning to Classify from Impure Samples. arXiv (Cornell University). 8 indexed citations
20.
Collins, Jack H., Kiel Howe, & Benjamin Nachman. (2018). CWoLa Hunting: Extending the Bump Hunt with Machine Learning. arXiv (Cornell University). 8 indexed citations

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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