Mario Pasquato

1.8k total citations
43 papers, 972 citations indexed

About

Mario Pasquato is a scholar working on Astronomy and Astrophysics, Instrumentation and Nuclear and High Energy Physics. According to data from OpenAlex, Mario Pasquato has authored 43 papers receiving a total of 972 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Astronomy and Astrophysics, 14 papers in Instrumentation and 4 papers in Nuclear and High Energy Physics. Recurrent topics in Mario Pasquato's work include Stellar, planetary, and galactic studies (21 papers), Astrophysics and Star Formation Studies (18 papers) and Astronomy and Astrophysical Research (14 papers). Mario Pasquato is often cited by papers focused on Stellar, planetary, and galactic studies (21 papers), Astrophysics and Star Formation Studies (18 papers) and Astronomy and Astrophysical Research (14 papers). Mario Pasquato collaborates with scholars based in Italy, United States and Canada. Mario Pasquato's co-authors include Michela Mapelli, Nicola Giacobbo, Mario Spera, Ugo N Di Carlo, Francesco Haardt, Enrico Vesperini, Long Wang, Michele Trenti, G. Beccari and Alessandro Ballone and has published in prestigious journals such as Nature, The Astrophysical Journal and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

Mario Pasquato

39 papers receiving 895 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mario Pasquato Italy 16 913 294 58 33 25 43 972
P. Arriagada Chile 10 754 0.8× 282 1.0× 34 0.6× 24 0.7× 21 0.8× 13 769
O. L. Creevey France 13 754 0.8× 391 1.3× 33 0.6× 43 1.3× 47 1.9× 31 771
C. C. Worley United Kingdom 16 833 0.9× 416 1.4× 91 1.6× 31 0.9× 17 0.7× 36 850
R. Andrae Germany 12 784 0.9× 386 1.3× 47 0.8× 18 0.5× 54 2.2× 26 822
Leigh C. Smith United Kingdom 18 950 1.0× 407 1.4× 34 0.6× 42 1.3× 64 2.6× 65 982
Fiorenzo Vincenzo United Kingdom 18 906 1.0× 385 1.3× 60 1.0× 19 0.6× 21 0.8× 34 956
A. E. Sansom United Kingdom 17 938 1.0× 478 1.6× 85 1.5× 33 1.0× 28 1.1× 55 958
Jamie Tayar United States 17 1.1k 1.2× 492 1.7× 33 0.6× 32 1.0× 68 2.7× 49 1.1k
Adrian S. Hamers United States 23 1.3k 1.4× 174 0.6× 59 1.0× 30 0.9× 24 1.0× 47 1.3k
P. North Switzerland 19 1.0k 1.1× 445 1.5× 54 0.9× 22 0.7× 47 1.9× 61 1.1k

Countries citing papers authored by Mario Pasquato

Since Specialization
Citations

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

Fields of papers citing papers by Mario Pasquato

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mario Pasquato

This figure shows the co-authorship network connecting the top 25 collaborators of Mario Pasquato. A scholar is included among the top collaborators of Mario Pasquato 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 Mario Pasquato. Mario Pasquato 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.
Trani, Alessandro A., et al.. (2025). Chaos in violent relaxation dynamics. Astronomy and Astrophysics. 698. A28–A28. 2 indexed citations
2.
Pang, Xiaoying, et al.. (2025). The 3D morphology of open clusters in the solar neighborhood. Astronomy and Astrophysics. 695. A22–A22.
3.
Israel, G. L., et al.. (2025). The hunt for new pulsating ultraluminous X-ray sources: A clustering approach. Astronomy and Astrophysics. 701. A96–A96. 1 indexed citations
4.
Kavanagh, Bradley J., et al.. (2025). Sharpening the dark matter signature in gravitational waveforms. II. Numerical simulations. Physical review. D. 111(6). 11 indexed citations
5.
Santoliquido, Filippo, U. Dupletsa, Jacopo Tissino, et al.. (2024). Classifying binary black holes from Population III stars with the Einstein Telescope: A machine-learning approach. Astronomy and Astrophysics. 690. A362–A362. 5 indexed citations
6.
Macciò, Andrea V., et al.. (2024). Quantitatively rating galaxy simulations against real observations with anomaly detection. Monthly Notices of the Royal Astronomical Society. 529(4). 3536–3549.
7.
Spina, L., G. Carraro, L. Magrini, et al.. (2023). Parameter Estimation for Open Clusters using an Artificial Neural Network with a QuadTree-based Feature Extractor. The Astronomical Journal. 167(1). 12–12. 28 indexed citations
8.
Pasquato, Mario, et al.. (2023). Dynamics of intermediate mass black holes in globular clusters. Astronomy and Astrophysics. 673. A8–A8. 6 indexed citations
9.
10.
Pasquato, Mario, et al.. (2021). Introducing a new multi-particle collision method for the evolution of dense stellar systems. Springer Link (Chiba Institute of Technology). 5 indexed citations
11.
Carlo, Ugo N Di, Michela Mapelli, Mario Pasquato, et al.. (2021). Intermediate-mass black holes from stellar mergers in young star clusters. Monthly Notices of the Royal Astronomical Society. 507(4). 5132–5143. 59 indexed citations
12.
Artale, M. Celeste, Y. Bouffanais, Michela Mapelli, et al.. (2020). An astrophysically motivated ranking criterion for low-latency electromagnetic follow-up of gravitational wave events. Monthly Notices of the Royal Astronomical Society. 495(2). 1841–1852. 18 indexed citations
13.
Artale, M. Celeste, Michela Mapelli, Y. Bouffanais, et al.. (2019). Mass and star formation rate of the host galaxies of compact binary mergers across cosmic time. Monthly Notices of the Royal Astronomical Society. 491(3). 3419–3434. 35 indexed citations
14.
Askar, Abbas, et al.. (2019). Stellar-mass Black Holes in Globular Clusters: Dynamical consequences and observational signatures. Proceedings of the International Astronomical Union. 14(S351). 395–399. 2 indexed citations
15.
Carrera, R., Mario Pasquato, A. Vallenari, et al.. (2019). Extended halo of NGC 2682 (M 67) from Gaia DR2. Astronomy and Astrophysics. 627. A119–A119. 49 indexed citations
16.
Pasquato, Mario & Chul Chung. (2016). Merged or monolithic? Using machine-learning to reconstruct the dynamical history of simulated star clusters. Astronomy and Astrophysics. 589. A95–A95. 6 indexed citations
17.
Pasquato, Mario, et al.. (2013). Core collapse and horizontal-branch morphology in Galactic globular clusters. Astronomy and Astrophysics. 554. A129–A129. 5 indexed citations
18.
Ferraro, F. R., B. Lanzoni, E. Dalessandro, et al.. (2012). Dynamical age differences among coeval star clusters as revealed by blue stragglers. Nature. 492(7429). 393–395. 116 indexed citations
19.
Pasquato, Mario & G. Bertin. (2010). On the fundamental line of galactic and extragalactic \nglobular clusters. Springer Link (Chiba Institute of Technology). 5 indexed citations
20.
Pasquato, Mario & G. Bertin. (2008). On the Fundamental Plane of the Galactic globular\ncluster system. Springer Link (Chiba Institute of Technology). 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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