F. Pantaleo

44.1k total citations
22 papers, 51 citations indexed

About

F. Pantaleo is a scholar working on Nuclear and High Energy Physics, Computer Networks and Communications and Radiation. According to data from OpenAlex, F. Pantaleo has authored 22 papers receiving a total of 51 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Nuclear and High Energy Physics, 10 papers in Computer Networks and Communications and 5 papers in Radiation. Recurrent topics in F. Pantaleo's work include Particle Detector Development and Performance (15 papers), Particle physics theoretical and experimental studies (13 papers) and Distributed and Parallel Computing Systems (6 papers). F. Pantaleo is often cited by papers focused on Particle Detector Development and Performance (15 papers), Particle physics theoretical and experimental studies (13 papers) and Distributed and Parallel Computing Systems (6 papers). F. Pantaleo collaborates with scholars based in Switzerland, Italy and United States. F. Pantaleo's co-authors include M. Rovere, A. Bocci, Vincenzo Innocente, M. J. Kortelainen, Antonio Di Pilato, W. Redjeb, Andrzej P. Nowak, Z. Chen, A. Biagioni and Antonio Carta and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment and Journal of Instrumentation.

In The Last Decade

F. Pantaleo

18 papers receiving 50 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
F. Pantaleo Switzerland 4 34 20 12 11 6 22 51
M. Rovere Switzerland 4 32 0.9× 12 0.6× 7 0.6× 6 0.5× 9 1.5× 10 47
Sverre Jarp Switzerland 5 26 0.8× 29 1.4× 25 2.1× 7 0.6× 4 0.7× 18 57
E. Meschi Switzerland 4 24 0.7× 16 0.8× 13 1.1× 4 0.4× 5 0.8× 18 41
S. Binet France 4 30 0.9× 33 1.6× 14 1.2× 8 0.7× 5 0.8× 14 60
L. Tompkins United States 3 34 1.0× 18 0.9× 9 0.8× 6 0.5× 4 0.7× 7 44
M. Krzewicki Germany 2 40 1.2× 24 1.2× 4 0.3× 7 0.6× 3 0.5× 3 51
K. Kordas Greece 4 18 0.5× 12 0.6× 5 0.4× 6 0.5× 4 0.7× 8 31
L. Sartori Italy 4 33 1.0× 23 1.1× 15 1.3× 12 1.1× 8 1.3× 12 54
C. Leggett United States 5 39 1.1× 34 1.7× 9 0.8× 8 0.7× 2 0.3× 14 55
S. Pagan Griso United States 3 48 1.4× 10 0.5× 4 0.3× 4 0.4× 4 0.7× 13 60

Countries citing papers authored by F. Pantaleo

Since Specialization
Citations

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

Fields of papers citing papers by F. Pantaleo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of F. Pantaleo

This figure shows the co-authorship network connecting the top 25 collaborators of F. Pantaleo. A scholar is included among the top collaborators of F. Pantaleo 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 F. Pantaleo. F. Pantaleo 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.
Brondolin, E., M. Rovere, & F. Pantaleo. (2024). The k4Clue package: Empowering future collider experiments with the CLUE algorithm. Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment. 1061. 169100–169100.
2.
Pilato, Antonio Di, et al.. (2024). qCLUE: a quantum clustering algorithm for multi-dimensional datasets. SHILAP Revista de lepidopterología. 3. 2 indexed citations
3.
Bocci, A., E. Cano, G. Hugo, et al.. (2024). Evaluating Performance Portability with the CMS Heterogeneous Pixel Reconstruction code. SHILAP Revista de lepidopterología. 295. 11008–11008. 1 indexed citations
4.
Pantaleo, F. & M. Rovere. (2023). The Iterative Clustering framework for the CMS HGCAL Reconstruction. Journal of Physics Conference Series. 2438(1). 12096–12096.
5.
Bocci, A., Antonio Di Pilato, F. Pantaleo, et al.. (2023). Performance portability for the CMS Reconstruction with Alpaka. Journal of Physics Conference Series. 2438(1). 12058–12058. 2 indexed citations
6.
Alves, Bruno Afonso Fontana Santos, F. Pantaleo, & M. Rovere. (2023). Clustering in the Heterogeneous Reconstruction Chain of the CMS HGCAL Detector. Journal of Physics Conference Series. 2438(1). 12015–12015. 2 indexed citations
7.
Alves, Bruno Afonso Fontana Santos, A. Bocci, M. J. Kortelainen, F. Pantaleo, & M. Rovere. (2021). Heterogeneous techniques for rescaling energy deposits in the CMS Phase-2 endcap calorimeter. SHILAP Revista de lepidopterología. 251. 4017–4017. 1 indexed citations
8.
Chen, Z., Antonio Di Pilato, F. Pantaleo, & M. Rovere. (2020). GPU-based Clustering Algorithm for the CMS High Granularity Calorimeter. SHILAP Revista de lepidopterología. 245. 5005–5005. 4 indexed citations
9.
Pilato, Antonio Di, Z. Chen, F. Pantaleo, & M. Rovere. (2020). Reconstruction in an imaging calorimeter for HL-LHC. Journal of Instrumentation. 15(6). C06023–C06023. 2 indexed citations
10.
Bocci, A., D. Dagenhart, Vincenzo Innocente, et al.. (2020). Bringing heterogeneity to the CMS software framework. SHILAP Revista de lepidopterología. 245. 5009–5009. 8 indexed citations
11.
Vlimant, Jean-Roch, F. Pantaleo, M. Pierini, et al.. (2019). Large-Scale Distributed Training Applied to Generative Adversarial Networks for Calorimeter Simulation. SHILAP Revista de lepidopterología. 214. 6025–6025. 3 indexed citations
12.
Florio, A. Di, F. Pantaleo, & Antonio Carta. (2018). Convolutional Neural Network for Track Seed Filtering at the CMS High-Level Trigger. Journal of Physics Conference Series. 1085. 42040–42040. 2 indexed citations
13.
Pantaleo, F.. (2017). New Track Seeding Techniques for the CMS Experiment. CERN Bulletin. 1 indexed citations
14.
Ammendola, Roberto, A. Biagioni, Ottorino Frezza, et al.. (2016). NaNet: a flexible and configurable low-latency NIC for real-time trigger systems based on GPUs.. 8 indexed citations
15.
Ammendola, Roberto, A. Biagioni, Luca Deri, et al.. (2014). GPUs for real-time processing in HEP trigger systems. Journal of Physics Conference Series. 523. 12007–12007. 3 indexed citations
16.
Ammendola, Roberto, A. Biagioni, R. Fantechi, et al.. (2014). NaNet: a low-latency NIC enabling GPU-based, real-time low level trigger systems. Journal of Physics Conference Series. 513(1). 12018–12018. 2 indexed citations
17.
Ammendola, Roberto, M. Bauce, A. Biagioni, et al.. (2013). The GAP project - GPU for realtime applications in high energy physics and medical imaging. CINECA IRIS Institutial research information system (University of Pisa). 396. 1–7.
18.
Collazuol, G., Vincenzo Innocente, G. Lamanna, F. Pantaleo, & M. Sozzi. (2012). Real-time use of GPUs in NA62 experiment. CERN Document Server (European Organization for Nuclear Research). 34. 1–5. 2 indexed citations
19.
Jarp, Sverre, et al.. (2011). Evaluation of Likelihood Functions for Data Analysis on Graphics Processing Units. 1349–1358. 3 indexed citations
20.
Jarp, Sverre, et al.. (2011). Parallelization of maximum likelihood fits with OpenMP and CUDA. Journal of Physics Conference Series. 331(3). 32021–32021. 3 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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