M. Kado

55.4k total citations
9 papers, 137 citations indexed

About

M. Kado is a scholar working on Nuclear and High Energy Physics, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, M. Kado has authored 9 papers receiving a total of 137 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Nuclear and High Energy Physics, 5 papers in Artificial Intelligence and 2 papers in Computer Networks and Communications. Recurrent topics in M. Kado's work include Particle physics theoretical and experimental studies (7 papers), Particle Detector Development and Performance (3 papers) and Distributed and Parallel Computing Systems (2 papers). M. Kado is often cited by papers focused on Particle physics theoretical and experimental studies (7 papers), Particle Detector Development and Performance (3 papers) and Distributed and Parallel Computing Systems (2 papers). M. Kado collaborates with scholars based in Italy, Israel and Germany. M. Kado's co-authors include Amin Aboubrahim, M. Cepeda, Stefania Gori, Alexandre Alves, P. Ilten, Francesco Riva, Simone Alioli, Rabah Abdul Khalek, J. Alimena and F. A. Di Bello and has published in prestigious journals such as The European Physical Journal C, Annual Review of Nuclear and Particle Science and Comptes Rendus Physique.

In The Last Decade

M. Kado

8 papers receiving 136 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
M. Kado Italy	6	130	27	21	8	5	9	137
Д. Деркач Russia	6	134 1.0×	14 0.5×	11 0.5×	11 1.4×	5 1.0×	26	153
E. Gross Israel	6	73 0.6×	10 0.4×	29 1.4×	9 1.1×	5 1.0×	19	93
Sung Hak Lim United States	5	101 0.8×	40 1.5×	18 0.9×	6 0.8×	3 0.6×	8	133
Graeme Nail United Kingdom	6	290 2.2×	33 1.2×	20 1.0×	4 0.5×	4 0.8×	11	299
M. Pettee United States	5	58 0.4×	8 0.3×	33 1.6×	5 0.6×	9 1.8×	6	101
Johannes Bellm Germany	8	337 2.6×	39 1.4×	21 1.0×	7 0.9×	4 0.8×	13	346
D. Rocco United States	4	90 0.7×	10 0.4×	17 0.8×	5 0.6×	5 1.0×	5	117
T. R. Fernandez Perez Tomei Brazil	4	81 0.6×	30 1.1×	31 1.5×	3 0.4×	14 2.8×	9	93
M. Campanelli Switzerland	7	281 2.2×	8 0.3×	27 1.3×	4 0.5×	3 0.6×	15	300
A. Søgaard Canada	3	82 0.6×	16 0.6×	37 1.8×	4 0.5×	4 0.8×	8	94

Countries citing papers authored by M. Kado

Since Specialization

Citations

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

Fields of papers citing papers by M. Kado

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Kado

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

All Works

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9 of 9 papers shown

1.

Kakati, N., E. Dreyer, A. Ivina, et al.. (2025). HGPflow: extending hypergraph particle flow to collider event reconstruction. The European Physical Journal C. 85(8).

2.

Cranmer, K., F. A. Di Bello, E. Dreyer, et al.. (2023). Configurable calorimeter simulation for AI applications. Machine Learning Science and Technology. 4(3). 35042–35042. 4 indexed citations

3.

Bello, F. A. Di, E. Dreyer, S. Ganguly, et al.. (2023). Reconstructing particles in jets using set transformer and hypergraph prediction networks. The European Physical Journal C. 83(7). 20 indexed citations

4.

Soybelman, Nathalie, N. Kakati, L. Heinrich, et al.. (2023). Set-conditional set generation for particle physics. Machine Learning Science and Technology. 4(4). 45036–45036. 5 indexed citations

5.

Bello, F. A. Di, et al.. (2021). Efficiency Parameterization with Neural Networks. HAL (Le Centre pour la Communication Scientifique Directe). 5(1). 9 indexed citations

6.

Cepeda, M., Stefania Gori, P. Ilten, et al.. (2019). Report from Working Group 2. 7. 221–584. 87 indexed citations

7.

Heinemeyer, S., C. Mariotti, Andreas Weiler, M. Kado, & G. Weiglein. (2012). Implications of LHC results for TeV-scale physics: signals of electroweak symmetry breaking. DESY (CERN, DESY, Fermilab, IHEP, and SLAC). 1 indexed citations

8.

Janot, P. & M. Kado. (2002). Direct search for the Standard Model Higgs boson. Comptes Rendus Physique. 3(9). 1193–1202. 5 indexed citations

9.

Kado, M. & C. Tully. (2002). THE SEARCHES FOR HIGGS BOSONS AT LEP. Annual Review of Nuclear and Particle Science. 52(1). 65–113. 6 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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