J. Vega

4.0k citations
228 papers · 2.0k indexed · h-index 21

Impact in

Papers in

J. Vega

214 papers receiving 1.8k citations

Peers

J. Vega
Comparison fields: 5 of 102
  • Nuclear and High Energy Physics 1.1k
  • Signal Processing 242
  • Radiation 194
  • Artificial Intelligence 484
  • Aerospace Engineering 358
Replace M. Gelfusa with:
M. Gelfusa Italy
Kyoko Makino United States
Filippo Neri United States
Benjamin Nachman United States
David E. Muller United States
Joachim Reinhardt Germany
Alan H. Karp United States
Paul Coddington Australia
B. Liu United States
Robert D. Ryne United States
J. Vega relative to M. Gelfusa Italy M. Gelfusa's profile →
Citations per field
00.5×1.5×2.3×
M. Gelfusa · 1×
Citations per year

Countries citing papers authored by J. Vega

Since Specialization
Citations

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

Fields of papers citing papers by J. Vega

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside J. Vega, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with J. Vega Line = papers co-authored together J. Vega links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20244
3 20241
4 20243
5 20231
6 20231
7 20211
8 20213
9 202030
10 202011
11 201928
12 20193
13 20178
14 20165
15 20159
16
Prediction of disruptions at JET using the geodesic distance between wavelet distributions
20121
17
Results of the JET real-time disruption predictor in the ITER-like wall campaigns
20121
18 20102
19 20093
20
La experiencia del Mundo Técnico
20051

About J. Vega

J. Vega is a scholar working on Nuclear and High Energy Physics, Radiation, Signal Processing, Computer Networks and Communications and Artificial Intelligence, having authored 228 papers that have together received 2.0k indexed citations. Recurring topics across this work include Magnetic confinement fusion research (127 papers), Time Series Analysis and Forecasting (28 papers), Anomaly Detection Techniques and Applications (26 papers), Atomic and Subatomic Physics Research (25 papers), Fusion materials and technologies (25 papers), Nuclear Physics and Applications (23 papers), Superconducting Materials and Applications (22 papers) and Advanced Data Storage Technologies (21 papers). The work is most often cited by research in Nuclear and High Energy Physics (1.1k citations), Signal Processing (242 citations), Radiation (194 citations), Artificial Intelligence (484 citations) and Aerospace Engineering (358 citations). J. Vega has collaborated with scholars based in Spain, Italy and United Kingdom. Frequent co-authors include A. Murari, S. Dormido-Canto, G.A. Rattá, M. Ruíz, M. Gelfusa, A. Pereira, E. Barrera, A. Portas, R. Moreno and Juan Manuel López. Their work appears in journals such as Fusion Engineering and Design, Review of Scientific Instruments, Nuclear Fusion, Fusion Science & Technology and IEEE Transactions on Nuclear Science.

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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