Leonardo Angelini
- Cognitive Neuroscience top 5%
- Statistical and Nonlinear Physics top 5%
- Nuclear and High Energy Physics top 10%
- Psychiatry and Mental health
- Cardiology and Cardiovascular Medicine
- Co-authors
- M. PellicoroSebastiano StramagliaDaniele MarinazzoL. NittiJesús M. CortésMarina de TommasoGuo‐Rong WuG. Preparata
- Topics
- Particle physics theoretical and experimental studies (18 papers)High-Energy Particle Collisions Research (16 papers)Quantum Chromodynamics and Particle Interactions (16 papers)
In The Last Decade
Leonardo Angelini
50 papers receiving 589 citations
Peers
Comparison fields: 5 of 87
- Cognitive Neuroscience 287
- Statistical and Nonlinear Physics 105
- Nuclear and High Energy Physics 94
- Psychiatry and Mental health 71
- Cardiology and Cardiovascular Medicine 65
Countries citing papers authored by Leonardo Angelini
This map shows the geographic impact of Leonardo Angelini'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 Leonardo Angelini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leonardo Angelini more than expected).
Fields of papers citing papers by Leonardo Angelini
This network shows the impact of papers produced by Leonardo Angelini. 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 Leonardo Angelini. The network helps show where Leonardo Angelini may publish in the future.
Co-authorship network of co-authors of Leonardo Angelini
This figure shows the co-authorship network connecting the top 25 collaborators of Leonardo Angelini. A scholar is included among the top collaborators of Leonardo Angelini 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 Leonardo Angelini. Leonardo Angelini is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 16 | |
| 2 | 0 | |
| 3 | 57 | |
| 4 | 17 | |
| 5 | 30 | |
| 6 | 2 | |
| 7 | 1 | |
| 8 | 4 | |
| 9 | 13 | |
| 10 | Causality and communities in neural networks | 2 |
| 11 | 57 | |
| 12 | 40 | |
| 13 | 89 | |
| 14 | 12 | |
| 15 | Jet analysis by Deterministic Annealing | 6 |
| 16 | 2 | |
| 17 | 1 | |
| 18 | 4 | |
| 19 | 4 | |
| 20 | 2 |
About Leonardo Angelini
Leonardo Angelini is a scholar working on Nuclear and High Energy Physics, Statistical and Nonlinear Physics and Cognitive Neuroscience, having authored 51 papers that have together received 600 indexed citations. Recurring topics across this work include Particle physics theoretical and experimental studies (18 papers), High-Energy Particle Collisions Research (16 papers) and Quantum Chromodynamics and Particle Interactions (16 papers). The work is most often cited by research in Cognitive Neuroscience (287 citations), Statistical and Nonlinear Physics (105 citations) and Nuclear and High Energy Physics (94 citations). Leonardo Angelini has collaborated with scholars based in Italy, Belgium and Spain. Frequent co-authors include M. Pellicoro, Sebastiano Stramaglia, Daniele Marinazzo, L. Nitti, Jesús M. Cortés, Marina de Tommaso, Guo‐Rong Wu, G. Preparata, Marco Guido and G. Nardulli. Their work appears in journals such as Physical Review Letters, PLoS ONE and Nuclear Physics B.
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.