A. Hammad

2.9k total citations
26 papers, 239 citations indexed

About

A. Hammad is a scholar working on Nuclear and High Energy Physics, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, A. Hammad has authored 26 papers receiving a total of 239 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Nuclear and High Energy Physics, 2 papers in Computer Networks and Communications and 2 papers in Artificial Intelligence. Recurrent topics in A. Hammad's work include Particle physics theoretical and experimental studies (22 papers), Neutrino Physics Research (13 papers) and High-Energy Particle Collisions Research (7 papers). A. Hammad is often cited by papers focused on Particle physics theoretical and experimental studies (22 papers), Neutrino Physics Research (13 papers) and High-Energy Particle Collisions Research (7 papers). A. Hammad collaborates with scholars based in United Kingdom, Egypt and South Korea. A. Hammad's co-authors include Stefano Moretti, Stefan Antusch, Shaaban Khalil, Oliver Fischer, Mihoko M. Nojiri, Waleed Abdallah, Ahmed Rashed, Myeonghun Park, Kechen Wang and A. A. Abdelalim and has published in prestigious journals such as Nuclear Physics B, Physics Letters B and Computer Physics Communications.

In The Last Decade

A. Hammad

25 papers receiving 236 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
A. Hammad United Kingdom 11 219 30 19 9 9 26 239
Silvia Ferrario Ravasio Switzerland 13 365 1.7× 23 0.8× 16 0.8× 15 1.7× 15 1.7× 22 375
Alba Soto-Ontoso France 13 346 1.6× 22 0.7× 15 0.8× 4 0.4× 8 0.9× 27 357
M. Campanelli Switzerland 7 281 1.3× 8 0.3× 27 1.4× 3 0.3× 5 0.6× 15 300
Sergei Gleyzer United States 6 62 0.3× 12 0.4× 29 1.5× 7 0.8× 3 0.3× 26 95
F. Petriello United States 5 258 1.2× 34 1.1× 11 0.6× 4 0.4× 8 0.9× 7 274
M. Cepeda Spain 3 284 1.3× 83 2.8× 14 0.7× 6 0.7× 7 0.8× 5 287
Ilya Feige United States 6 165 0.8× 18 0.6× 11 0.6× 3 0.3× 6 0.7× 9 186
Michael Spannowsky United Kingdom 6 230 1.1× 22 0.7× 57 3.0× 10 1.1× 12 1.3× 6 268
Alexander Karlberg Switzerland 11 487 2.2× 43 1.4× 18 0.9× 10 1.1× 19 2.1× 20 495
Federico Demartin Belgium 4 248 1.1× 24 0.8× 13 0.7× 9 1.0× 9 1.0× 4 255

Countries citing papers authored by A. Hammad

Since Specialization
Citations

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

Fields of papers citing papers by A. Hammad

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of A. Hammad

This figure shows the co-authorship network connecting the top 25 collaborators of A. Hammad. A scholar is included among the top collaborators of A. Hammad 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 A. Hammad. A. Hammad 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.
Hammad, A. & Adil Jueid. (2025). Progress in ${\cal CP}$ violating top-Higgs coupling at the LHC with machine learning. Nuclear Physics B. 1021. 117137–117137. 2 indexed citations
2.
Hammad, A., et al.. (2025). Boosting probes of $$ \mathcal{CP} $$ violation in the top Yukawa coupling with Deep Learning. Journal of High Energy Physics. 2025(12). 1 indexed citations
3.
Hammad, A., et al.. (2025). DLScanner: A parameter space scanner package assisted by deep learning methods. Computer Physics Communications. 314. 109659–109659. 2 indexed citations
4.
Hammad, A. & Mihoko M. Nojiri. (2025). Transformer Networks for Heavy Flavor Jet Tagging. Journal of the Physical Society of Japan. 94(3).
5.
Hammad, A., Pyungwon Ko, Chih-Ting Lu, & Myeonghun Park. (2024). Exploring exotic decays of the Higgs boson to multi-photons at the LHC via multimodal learning approaches. Journal of High Energy Physics. 2024(9). 2 indexed citations
6.
Hammad, A., Stefano Moretti, & Mihoko M. Nojiri. (2024). Multi-scale cross-attention transformer encoder for event classification. Journal of High Energy Physics. 2024(3). 16 indexed citations
7.
Hammad, A., et al.. (2023). Exploration of parameter spaces assisted by machine learning. Computer Physics Communications. 293. 108902–108902. 11 indexed citations
8.
Datta, Alakabha, A. Hammad, Danny Marfatia, Lopamudra Mukherjee, & Ahmed Rashed. (2023). Dark photon and dark Z mediated B meson decays. Journal of High Energy Physics. 2023(3). 12 indexed citations
9.
Hammad, A., Shaaban Khalil, & Stefano Moretti. (2023). Search for mono-Higgs signals in bb¯ final states using deep neural networks. Physical review. D. 107(7). 6 indexed citations
10.
Hammad, A., et al.. (2023). Sharpening the A → Z(*)h signature of the Type-II 2HDM at the LHC through advanced Machine Learning. Journal of High Energy Physics. 2023(11). 13 indexed citations
11.
Hammad, A. & Myeonghun Park. (2023). Riemannian data preprocessing in machine learning to focus on QCD color structure. Journal of the Korean Physical Society. 83(4). 235–242. 3 indexed citations
12.
Abdallah, Waleed, A. Hammad, Shaaban Khalil, & Stefano Moretti. (2019). Dark matter spin characterization in mono-Z channels. Physical review. D. 100(9). 3 indexed citations
13.
Abdallah, Waleed, A. Hammad, Shaaban Khalil, & Stefano Moretti. (2018). Searching for charged Higgs bosons in the B − L supersymmetric standard model at the high luminosity large hadron collider. Physics Letters B. 788. 65–69. 2 indexed citations
14.
Antusch, Stefan, Eros Cazzato, Oliver Fischer, A. Hammad, & Kechen Wang. (2018). Lepton flavor violating dilepton dijet signatures from sterile neutrinos at proton colliders. Journal of High Energy Physics. 2018(10). 22 indexed citations
15.
Abdallah, Waleed, A. Hammad, A. Kasem, & Shaaban Khalil. (2018). Long-lived B−L symmetric SSM particles at the LHC. Physical review. D. 98(9). 4 indexed citations
16.
Hammad, A., Shaaban Khalil, & Cem Salih Ün. (2017). Large BR(h→τμ) in supersymmetric models. Physical review. D. 95(5). 9 indexed citations
17.
Abdallah, Waleed, A. Hammad, Shaaban Khalil, & Stefano Moretti. (2017). Search for mono-Higgs signals at the LHC in the B−L supersymmetric standard model. Physical review. D. 95(5). 16 indexed citations
18.
Hammad, A., Shaaban Khalil, & Stefano Moretti. (2016). LHC signals of aB−Lsupersymmetric standard modelCP-even Higgs boson. Physical review. D. 93(11). 12 indexed citations
19.
Hammad, A., Shaaban Khalil, & Stefano Moretti. (2015). Higgs boson decays intoγγandZγin the MSSM and theB−Lsupersymmetric SM. Physical review. D. Particles, fields, gravitation, and cosmology. 92(9). 12 indexed citations
20.
Abdelalim, A. A., A. Hammad, & Shaaban Khalil. (2014). B−Lheavy neutrinos and neutral gauge bosonZ′at the LHC. Physical review. D. Particles, fields, gravitation, and cosmology. 90(11). 11 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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