Kohei Hayashi

5.9k total citations
94 papers, 1.1k citations indexed

About

Kohei Hayashi is a scholar working on Nuclear and High Energy Physics, Astronomy and Astrophysics and Artificial Intelligence. According to data from OpenAlex, Kohei Hayashi has authored 94 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Nuclear and High Energy Physics, 29 papers in Astronomy and Astrophysics and 19 papers in Artificial Intelligence. Recurrent topics in Kohei Hayashi's work include Galaxies: Formation, Evolution, Phenomena (23 papers), Particle physics theoretical and experimental studies (17 papers) and Dark Matter and Cosmic Phenomena (17 papers). Kohei Hayashi is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (23 papers), Particle physics theoretical and experimental studies (17 papers) and Dark Matter and Cosmic Phenomena (17 papers). Kohei Hayashi collaborates with scholars based in Japan, United States and China. Kohei Hayashi's co-authors include Hisashi Kashima, Ryota Tomioka, Masashi Chiba, Masahiro Ibe, Miho N. Ishigaki, Taiji Suzuki, Nobuyuki Kudomi, Shigeki Matsumoto, H. Ohsumi and Kazuhiko Kume and has published in prestigious journals such as PLoS ONE, The Astrophysical Journal and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

Kohei Hayashi

91 papers receiving 999 citations

Peers

Kohei Hayashi

NHEP

Jiamin Hou Germany

Roger M. Smith United States

Andreas Stathopoulos United States

Fabian Lange Germany

Yang‐Hui He United Kingdom

J. S. R. Chisholm United Kingdom

A. B. Goncharov United States

Takayuki Tamura United States

Gudrun Heinrich Germany

Andrew Myers United States

Jiamin Hou Germany

Kohei Hayashi

205 ×0.5

NHEP

447 ×1.3

5 ×0.0

20 ×0.1

68 ×0.5

Citations per year, relative to Kohei Hayashi Kohei Hayashi (= 1×) peers Jiamin Hou

Countries citing papers authored by Kohei Hayashi

Since Specialization

Citations

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

Fields of papers citing papers by Kohei Hayashi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kohei Hayashi

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

All Works

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20 of 20 papers shown

Chiba, Masashi, et al.. (2025). Disentangling γ − β: The Fourth-order Velocity Moments Based on Spherical Jeans Analysis. The Astrophysical Journal. 982(2). 167–167. 2 indexed citations

Komiyama, Yutaka, Masashi Chiba, Mikito Tanaka, et al.. (2024). Detection of a Spatially Extended Stellar Population in M33: A Shallow Stellar Halo?. The Astrophysical Journal. 971(1). 107–107. 2 indexed citations

Hirai, Yutaka, Evan N. Kirby, Masashi Chiba, et al.. (2024). Chemo-dynamical Evolution of Simulated Satellites for a Milky Way–like Galaxy. The Astrophysical Journal. 970(2). 105–105. 4 indexed citations

Tanaka, Mikito, Yutaka Komiyama, Masashi Chiba, et al.. (2024). The structure of the stellar halo of the Andromeda galaxy explored with the NB515 for Subaru/HSC – I. New insights on the stellar halo up to 120 kpc. Monthly Notices of the Royal Astronomical Society. 536(1). 530–553. 7 indexed citations

Chiba, Masashi, Yutaka Komiyama, Kohei Hayashi, et al.. (2024). The Milky Way tomography with Subaru Hyper Suprime-Cam. I. Halo substructures. Publications of the Astronomical Society of Japan. 76(2). 205–218. 5 indexed citations

Hayashi, Kohei, Yutaka Hirai, Masashi Chiba, & Tomoaki Ishiyama. (2023). Dark Matter Halo Properties of the Galactic Dwarf Satellites: Implication for Chemo-dynamical Evolution of the Satellites and a Challenge to Lambda Cold Dark Matter. The Astrophysical Journal. 953(2). 185–185. 11 indexed citations

Takada, Masahiro, et al.. (2023). Study of structural parameters and systemic proper motion of Sextans dwarf spheroidal galaxy with Subaru Hyper Suprime-Cam data. Monthly Notices of the Royal Astronomical Society. 526(1). 1310–1323. 3 indexed citations

Hayashi, Kohei, et al.. (2023). Cosmological prior for the J-factor estimation of dwarf spheroidal galaxies. Physical review. D. 108(8). 1 indexed citations

Tanaka, Masayuki, et al.. (2022). The Missing Satellite Problem outside of the Local Group. II. Statistical Properties of Satellites of Milky Way–like Galaxies. The Astrophysical Journal. 936(1). 38–38. 12 indexed citations

10.

Hayashi, Kohei, et al.. (2021). N-body Self-consistent Stellar-halo Modeling of the Fornax Dwarf Galaxy. The Astrophysical Journal. 909(2). 147–147.

11.

Hayashi, Kohei, et al.. (2020). On Random Subsampling of Gaussian Process Regression: A Graphon-Based Analysis.. arXiv (Cornell University). 2055–2065. 1 indexed citations

12.

Hayashi, Kohei, Tomoaki Ishiyama, Go Ogiya, et al.. (2017). Universal Dark Halo Scaling Relation for the Dwarf Spheroidal Satellites. The Astrophysical Journal. 843(2). 97–97. 4 indexed citations

13.

Hayashi, Kohei & Yuichi Yoshida. (2017). Fitting Low-Rank Tensors in Constant Time. Neural Information Processing Systems. 30. 2473–2481. 1 indexed citations

14.

Hayashi, Kohei, et al.. (2017). Why does PairDiff work? - A Mathematical Analysis of Bilinear Relational Compositional Operators for Analogy Detection.. International Conference on Computational Linguistics. 2493–2504. 2 indexed citations

15.

Hayashi, Kohei, et al.. (2017). An Optimality Proof for the PairDiff operator for Representing Relations between Words.. arXiv (Cornell University). 1 indexed citations

16.

Hayashi, Kohei, et al.. (2016). Tensor Decomposition with Smoothness. IEICE Technical Report; IEICE Tech. Rep.. 116(300). 1597–1606. 2 indexed citations

17.

Hayashi, Kohei & Masashi Chiba. (2015). STRUCTURAL PROPERTIES OF NON-SPHERICAL DARK HALOS IN MILKY WAY AND ANDROMEDA DWARF SPHEROIDAL GALAXIES. The Astrophysical Journal. 810(1). 22–22. 19 indexed citations

18.

Hayashi, Kohei & Ryohei Fujimaki. (2013). Factorized Asymptotic Bayesian Inference for Latent Feature Models. Neural Information Processing Systems. 26. 1214–1222. 10 indexed citations

19.

Hayashi, Kohei, Takashi Takenouchi, Ryota Tomioka, & Hisashi Kashima. (2011). Self-measuring Similarity for Multi-task Gaussian Process. International Conference on Machine Learning. 145–153. 3 indexed citations

20.

Tomioka, Ryota, Kohei Hayashi, & Hisashi Kashima. (2010). On the extension of trace norm to tensors. arXiv (Cornell University). 17 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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