E. Arganda

5.0k total citations
37 papers, 730 citations indexed

About

E. Arganda is a scholar working on Nuclear and High Energy Physics, Astronomy and Astrophysics and Artificial Intelligence. According to data from OpenAlex, E. Arganda has authored 37 papers receiving a total of 730 indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Nuclear and High Energy Physics, 9 papers in Astronomy and Astrophysics and 5 papers in Artificial Intelligence. Recurrent topics in E. Arganda's work include Particle physics theoretical and experimental studies (35 papers), Dark Matter and Cosmic Phenomena (15 papers) and Neutrino Physics Research (14 papers). E. Arganda is often cited by papers focused on Particle physics theoretical and experimental studies (35 papers), Dark Matter and Cosmic Phenomena (15 papers) and Neutrino Physics Research (14 papers). E. Arganda collaborates with scholars based in Spain, Argentina and United States. E. Arganda's co-authors include M. J. Herrero, Xabier Marcano, C. Weiland, Alejandro Szynkman, David Temes, Roberto A. Morales, J. Lorenzo Díaz-Cruz, A. M. Teixeira, J. Portolés and Aníbal D. Medina and has published in prestigious journals such as SHILAP Revista de lepidopterología, Nuclear Physics B and Physics Letters B.

In The Last Decade

E. Arganda

37 papers receiving 716 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
E. Arganda Spain 12 723 91 31 14 6 37 730
Susanne Westhoff Germany 11 510 0.7× 83 0.9× 27 0.9× 15 1.1× 6 1.0× 23 522
Tanmoy Modak Taiwan 15 453 0.6× 117 1.3× 22 0.7× 15 1.1× 4 0.7× 36 468
Andrei Angelescu France 8 510 0.7× 124 1.4× 31 1.0× 13 0.9× 3 0.5× 15 516
Chandan Hati India 12 472 0.7× 80 0.9× 28 0.9× 7 0.5× 4 0.7× 30 483
Xabier Marcano Spain 13 524 0.7× 72 0.8× 15 0.5× 14 1.0× 3 0.5× 27 534
O. M. Ogreid Norway 11 620 0.9× 208 2.3× 30 1.0× 8 0.6× 4 0.7× 27 642
L. Basso United Kingdom 14 554 0.8× 157 1.7× 13 0.4× 7 0.5× 5 0.8× 25 568
Marius Utheim Sweden 5 423 0.6× 52 0.6× 27 0.9× 10 0.7× 9 1.5× 6 450
A. E. Cárcamo Hernández Chile 20 1.1k 1.5× 198 2.2× 37 1.2× 9 0.6× 4 0.7× 90 1.1k
Hendrik Mantler Germany 7 410 0.6× 87 1.0× 15 0.5× 7 0.5× 5 0.8× 7 418

Countries citing papers authored by E. Arganda

Since Specialization
Citations

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

Fields of papers citing papers by E. Arganda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of E. Arganda

This figure shows the co-authorship network connecting the top 25 collaborators of E. Arganda. A scholar is included among the top collaborators of E. Arganda 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 E. Arganda. E. Arganda 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.
Arganda, E., et al.. (2025). Machine-Learning Analysis of Radiative Decays to Dark Matter at the LHC. Journal of High Energy Physics. 2025(7). 1 indexed citations
2.
Arganda, E., A. Delgado, A. Martin, et al.. (2024). Drell-Yan bounds on gapped continuum spectra. Journal of High Energy Physics. 2024(4). 1 indexed citations
3.
Arganda, E., et al.. (2024). Machine-learning performance on Higgs-pair production associated with dark matter at the LHC. The European Physical Journal Plus. 139(7). 4 indexed citations
4.
Arganda, E., et al.. (2024). LHC study of third-generation scalar leptoquarks with machine-learned likelihoods. Physical review. D. 109(5). 4 indexed citations
5.
Aguilar–Saavedra, J. A., et al.. (2024). Gradient boosting MUST taggers for highly-boosted jets. The European Physical Journal Plus. 139(11). 1 indexed citations
6.
Arganda, E., et al.. (2023). Machine-learned exclusion limits without binning. The European Physical Journal C. 83(12). 7 indexed citations
7.
Arganda, E., et al.. (2022). Discovery and Exclusion Prospects for Staus Produced by Heavy Higgs Boson Decays at the LHC. Advances in High Energy Physics. 2022. 1–21. 4 indexed citations
8.
Arganda, E., et al.. (2022). A method for approximating optimal statistical significances with machine-learned likelihoods. The European Physical Journal C. 82(11). 11 indexed citations
9.
Arganda, E., et al.. (2022). Towards a method to anticipate dark matter signals with deep learning at the LHC. SciPost Physics. 12(2). 6 indexed citations
10.
Arganda, E., A. Delgado, Roberto A. Morales, & M. Quirós. (2022). Search strategy for gluinos at the LHC with a Higgs boson decaying into tau leptons. The European Physical Journal C. 82(10). 1 indexed citations
11.
Arganda, E., et al.. (2021). Interpretation of LHC excesses in ditop and ditau channels as a 400-GeV pseudoscalar resonance. arXiv (Cornell University). 5 indexed citations
12.
Arganda, E., A. Delgado, Roberto A. Morales, & M. Quirós. (2021). Novel Higgsino dark matter signal interpretation at the LHC. Physical review. D. 104(5). 2 indexed citations
13.
Arganda, E., et al.. (2018). Potential discovery of staus through heavy Higgs boson decays at the LHC. Americanae (AECID Library). 9 indexed citations
14.
Arganda, E., M. J. Herrero, Xabier Marcano, & C. Weiland. (2016). Radiatively-induced LFV Higgs Decays from Massive ISS Neutrinos. Nuclear and Particle Physics Proceedings. 273-275. 1685–1691. 1 indexed citations
15.
Arganda, E., Jaume Guasch, W. Hollik, & S. Peñaranda. (2016). Discriminating between SUSY and non-SUSY Higgs sectors through the ratio H→bb¯/H→τ+τ−with a 125 GeV Higgs boson. Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona). 2 indexed citations
16.
Arganda, E., M. J. Herrero, Xabier Marcano, Roberto A. Morales, & Alejandro Szynkman. (2016). Effective LFV $H\ell_i\ell_j$ vertex from right-handed neutrinos within the Mass Insertion Approximation. arXiv (Cornell University). 1 indexed citations
17.
Arganda, E., J. Lorenzo Díaz-Cruz, & Alejandro Szynkman. (2013). Decays of H 0/A 0 in supersymmetric scenarios with heavy sfermions. The European Physical Journal C. 73(4). 31 indexed citations
18.
Arganda, E., J. Lorenzo Díaz-Cruz, & Alejandro Szynkman. (2012). Split Supersymmetry with Non-Universal Higgs Masses. arXiv (Cornell University). 1 indexed citations
19.
Arganda, E., et al.. (2011). Anisotropy and chemical composition of ultra-high energy cosmic rays using arrival directions measured by the Pierre Auger Observatory. Journal of Cosmology and Astroparticle Physics. 2011(6). 22–22. 11 indexed citations
20.
Arganda, E. & M. J. Herrero. (2006). Testing supersymmetry with lepton flavor violatingτandμdecays. Physical review. D. Particles, fields, gravitation, and cosmology. 73(5). 116 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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