Verónica Sanz

6.5k total citations · 1 hit paper
93 papers, 2.9k citations indexed

About

Verónica Sanz is a scholar working on Nuclear and High Energy Physics, Astronomy and Astrophysics and Statistical and Nonlinear Physics. According to data from OpenAlex, Verónica Sanz has authored 93 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 77 papers in Nuclear and High Energy Physics, 35 papers in Astronomy and Astrophysics and 6 papers in Statistical and Nonlinear Physics. Recurrent topics in Verónica Sanz's work include Particle physics theoretical and experimental studies (69 papers), Cosmology and Gravitation Theories (34 papers) and Dark Matter and Cosmic Phenomena (34 papers). Verónica Sanz is often cited by papers focused on Particle physics theoretical and experimental studies (69 papers), Cosmology and Gravitation Theories (34 papers) and Dark Matter and Cosmic Phenomena (34 papers). Verónica Sanz collaborates with scholars based in United Kingdom, Spain and Switzerland. Verónica Sanz's co-authors include John Ellis, Tevong You, Johannes Hirn, Ken Mimasu, José Miguel No, Nuria Rius, Myeonghun Park, Hyun Min Lee, Djuna Croon and Maeve Madigan and has published in prestigious journals such as Physical Review Letters, SHILAP Revista de lepidopterología and Nuclear Physics B.

In The Last Decade

Verónica Sanz

89 papers receiving 2.8k citations

Hit Papers

Top, Higgs, diboson and electroweak fit to the Standard M... 2020 2026 2022 2024 2020 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Top, Higgs, diboson and electroweak fit to the Standard Model effective field theory
    2020 161 citations John Ellis, Maeve Madigan et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Verónica Sanz United Kingdom 30 2.7k 1.0k 122 88 63 93 2.9k
João P. Silva Portugal 28 3.5k 1.3× 828 0.8× 118 1.0× 66 0.8× 47 0.7× 93 3.6k
Wei Xue United States 27 2.0k 0.8× 1.6k 1.5× 25 0.2× 204 2.3× 85 1.3× 80 2.3k
M. Chala Spain 24 1.5k 0.6× 812 0.8× 53 0.4× 77 0.9× 42 0.7× 52 1.6k
N. H. Bian United Kingdom 20 782 0.3× 1.1k 1.1× 67 0.5× 55 0.6× 81 1.3× 57 1.3k
M. Ahlers United States 27 2.7k 1.0× 1.4k 1.4× 31 0.3× 141 1.6× 60 1.0× 77 2.7k
M. Kuhlen United States 25 1.1k 0.4× 1.7k 1.6× 26 0.2× 124 1.4× 107 1.7× 42 2.0k
Jessie Shelton United States 27 1.8k 0.7× 1.2k 1.1× 42 0.3× 144 1.6× 82 1.3× 52 2.0k
W. J. Stirling United Kingdom 29 4.6k 1.7× 334 0.3× 72 0.6× 79 0.9× 34 0.5× 101 4.7k
Kimmo Tuominen Finland 29 3.1k 1.2× 1.5k 1.4× 29 0.2× 161 1.8× 79 1.3× 105 3.2k
F. del Águila Spain 35 3.6k 1.4× 734 0.7× 75 0.6× 79 0.9× 96 1.5× 117 3.7k

Countries citing papers authored by Verónica Sanz

Since Specialization
Citations

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

Fields of papers citing papers by Verónica Sanz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Verónica Sanz

This figure shows the co-authorship network connecting the top 25 collaborators of Verónica Sanz. A scholar is included among the top collaborators of Verónica Sanz 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 Verónica Sanz. Verónica Sanz 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.
Hirn, Johannes, Verónica Sanz, J. E. García Navarro, et al.. (2024). Transfer learning of species co-occurrence patterns between plant communities. Ecological Informatics. 83. 102826–102826. 1 indexed citations
2.
Madigan, Maeve, et al.. (2024). Di-Higgs production via axion-like particles. Journal of High Energy Physics. 2024(10).
3.
Sanchis-Lozano, Miguel-Angel & Verónica Sanz. (2024). Observable imprints of primordial gravitational waves on the temperature anisotropies of the cosmic microwave background. Physical review. D. 109(6). 1 indexed citations
4.
Hirsch, M., et al.. (2024). Faking ZZZ vertices at the LHC. Journal of High Energy Physics. 2024(12). 2 indexed citations
5.
Muñoz, Enrique, et al.. (2024). Improving Compton camera imaging of multi-energy radioactive sources by using machine learning algorithms for event selection. Radiation Physics and Chemistry. 226. 112166–112166. 1 indexed citations
6.
Lessa, André & Verónica Sanz. (2024). Going beyond Top EFT. Journal of High Energy Physics. 2024(4). 2 indexed citations
7.
8.
Khosa, Charanjit K. & Verónica Sanz. (2023). Anomaly Awareness. SciPost Physics. 15(2). 8 indexed citations
9.
Gómez-Ambrosio, Raquel, et al.. (2023). Unbinned multivariate observables for global SMEFT analyses from machine learning. Journal of High Energy Physics. 2023(3). 16 indexed citations
10.
Hirsch, M., et al.. (2023). SMEFT goes dark: Dark Matter models for four-fermion operators. Journal of High Energy Physics. 2023(9). 6 indexed citations
11.
Castillo, F. L., C. Escobar, C. Garcı́a, et al.. (2023). Forecasting Geomagnetic Storm Disturbances and Their Uncertainties Using Deep Learning. Space Weather. 21(11). 8 indexed citations
12.
Hirn, Johannes, et al.. (2022). A deep Generative Artificial Intelligence system to predict species coexistence patterns. Methods in Ecology and Evolution. 13(5). 1052–1061. 12 indexed citations
13.
Bagnaschi, Emanuele, John Ellis, Maeve Madigan, et al.. (2022). SMEFT analysis of mW. Research Portal (King's College London). 45 indexed citations
14.
Khosa, Charanjit K., et al.. (2022). A simple guide from machine learning outputs to statistical criteria in particle physics. SciPost Physics Core. 5(4). 2 indexed citations
15.
Gavela, M.B., José Miguel No, Verónica Sanz, & J. F. de Trocóniz. (2020). Nonresonant Searches for Axionlike Particles at the LHC. Physical Review Letters. 124(5). 51802–51802. 46 indexed citations
16.
Khosa, Charanjit K., et al.. (2019). Using Machine Learning to disentangle LHC signatures of Dark Matter candidates. SHILAP Revista de lepidopterología. 12 indexed citations
17.
Sanz, Verónica, et al.. (2018). Composite Higgs Models after Run 2. Advances in High Energy Physics. 2018. 1–8. 15 indexed citations
18.
Sanz, Verónica. (2016). On the compatibility of the diboson excess with a gg-initiated composite sector. Sussex Research Online (University of Sussex). 3 indexed citations
19.
Hirn, Johannes & Verónica Sanz. (2006). NegativeSParameter from Holographic Technicolor. Physical Review Letters. 97(12). 121803–121803. 84 indexed citations
20.
Martínez‐Castelao, Alberto, Miguel Hueso, Verónica Sanz, et al.. (1998). Treatment of hypertension after renal transplantation: Long-term efficacy of verapamil, enalapril, and doxazosin. Kidney International. 54. S130–S134. 42 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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