Michelle Kuchera

601 total citations · 1 hit paper
16 papers, 286 citations indexed

About

Michelle Kuchera is a scholar working on Nuclear and High Energy Physics, Radiation and Astronomy and Astrophysics. According to data from OpenAlex, Michelle Kuchera has authored 16 papers receiving a total of 286 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Nuclear and High Energy Physics, 6 papers in Radiation and 3 papers in Astronomy and Astrophysics. Recurrent topics in Michelle Kuchera's work include Nuclear Physics and Applications (6 papers), Nuclear physics research studies (6 papers) and Particle physics theoretical and experimental studies (5 papers). Michelle Kuchera is often cited by papers focused on Nuclear Physics and Applications (6 papers), Nuclear physics research studies (6 papers) and Particle physics theoretical and experimental studies (5 papers). Michelle Kuchera collaborates with scholars based in United States, China and Norway. Michelle Kuchera's co-authors include D. Bazin, N. Sato, A. Boehnlein, W. Nazarewicz, P. N. Ostroumov, Markus Diefenthaler, Kostas Orginos, M. S. Smith, T. Horn and Xin-Nian Wang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Reviews of Modern Physics and The Astrophysical Journal.

In The Last Decade

Michelle Kuchera

16 papers receiving 282 citations

Hit Papers

align trajectories

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.

Colloquium: Machine learning in nuclear physics

2022 140 citations A. Boehnlein, Markus Diefenthaler et al. Reviews of Modern Physics profile →

Peers

Michelle Kuchera

NHEP

RADIA

AE

AMPO

AI

C. Fanelli United States

A. Boehnlein United States

Markus Diefenthaler United States

S. Kwak Germany

Serkan Akkoyun Türkiye

Jie Pu China

A. W. P. Poon United States

V. Ziegler United States

F. Belli Italy

C. McParland United States

C. Fanelli United States

Michelle Kuchera

237 ×1.2

NHEP

51 ×0.5

RADIA

35 ×0.5

AE

27 ×1.0

AMPO

21 ×0.8

AI

Citations per year, relative to Michelle Kuchera Michelle Kuchera (= 1×) peers C. Fanelli

Countries citing papers authored by Michelle Kuchera

Since Specialization

Citations

This map shows the geographic impact of Michelle Kuchera'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 Michelle Kuchera with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michelle Kuchera more than expected).

Fields of papers citing papers by Michelle Kuchera

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michelle Kuchera. 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 Michelle Kuchera. The network helps show where Michelle Kuchera may publish in the future.

Co-authorship network of co-authors of Michelle Kuchera

This figure shows the co-authorship network connecting the top 25 collaborators of Michelle Kuchera. A scholar is included among the top collaborators of Michelle Kuchera 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 Michelle Kuchera. Michelle Kuchera is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

16 of 16 papers shown

1.

Vassh, Nicole, et al.. (2025). Classifying Metal-poor Stars with Machine Learning Using Nucleosynthesis Calculations. The Astrophysical Journal. 992(1). 36–36. 1 indexed citations

2.

Tarasov, O., D. Bazin, M. Hausmann, et al.. (2023). LISE cute++, the latest generation of the LISE ++ package, to simulate rare isotope production with fragment-separators. Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms. 541. 4–7. 8 indexed citations

3.

Boehnlein, A., Markus Diefenthaler, N. Sato, et al.. (2022). Colloquium: Machine learning in nuclear physics. Reviews of Modern Physics. 94(3). 140 indexed citations breakdown →

4.

Ambrozewicz, P., M. Battaglieri, A. N. Hiller Blin, et al.. (2022). Machine learning-based event generator for electron-proton scattering. Physical review. D. 106(9). 10 indexed citations

5.

Bazin, D., et al.. (2021). Unsupervised learning for identifying events in active target experiments. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 1010. 165461–165461. 5 indexed citations

6.

Blue, John, et al.. (2021). Conditional Wasserstein Generative Adversarial Networks for Fast Detector Simulation. SHILAP Revista de lepidopterología. 251. 3055–3055. 1 indexed citations

7.

Bedaque, Paulo F., A. Boehnlein, M. Cromaz, et al.. (2021). A.I. for nuclear physics. The European Physical Journal A. 57(3). 27 indexed citations

8.

Thompson, K. L., et al.. (2021). Modelling magnetohydrodynamic equilibrium in magnetars with applications to continuous gravitational wave production. Monthly Notices of the Royal Astronomical Society. 503(2). 2764–2775. 3 indexed citations

9.

Sato, N., et al.. (2021). Variational Autoencoder Inverse Mapper: An End-to-End Deep Learning Framework for Inverse Problems. 1–8. 7 indexed citations

10.

Kuchera, Michelle, et al.. (2020). Bayesian neural networks for fast SUSY predictions. Physics Letters B. 813. 136041–136041. 14 indexed citations

11.

Sato, N., P. Ambrozewicz, Tianbo Liu, et al.. (2020). cFAT-GAN: Conditional Simulation of Electron-Proton Scattering Events with Variate Beam Energies by a Feature Augmented and Transformed Generative Adversarial Network. 372–375. 3 indexed citations

12.

Kuchera, Michelle, et al.. (2019). Machine learning methods for track classification in the AT-TPC. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 940. 156–167. 19 indexed citations

13.

Bazin, D., T. Ahn, Y. Ayyad, et al.. (2017). Commissioning of the Active-Target Time Projection Chamber. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 875. 65–79. 23 indexed citations

14.

Tabor, S. L., Vandana Tripathi, Alexander Volya, et al.. (2015). Higher-spin structures inF21andNa25. Physical Review C. 92(3). 8 indexed citations

15.

Kuchera, Michelle, et al.. (2015). Plans for performance and model improvements in the LISE++ software. Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms. 376. 168–170. 7 indexed citations

16.

Kuchera, Michelle, et al.. (2015). LISE++ Software Updates and Future Plans. Journal of Physics Conference Series. 664(7). 72029–72029. 10 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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