Arwa Dabbech

634 total citations
22 papers, 312 citations indexed

About

Arwa Dabbech is a scholar working on Astronomy and Astrophysics, Aerospace Engineering and Computational Mechanics. According to data from OpenAlex, Arwa Dabbech has authored 22 papers receiving a total of 312 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Astronomy and Astrophysics, 12 papers in Aerospace Engineering and 8 papers in Computational Mechanics. Recurrent topics in Arwa Dabbech's work include Radio Astronomy Observations and Technology (19 papers), Sparse and Compressive Sensing Techniques (8 papers) and Synthetic Aperture Radar (SAR) Applications and Techniques (6 papers). Arwa Dabbech is often cited by papers focused on Radio Astronomy Observations and Technology (19 papers), Sparse and Compressive Sensing Techniques (8 papers) and Synthetic Aperture Radar (SAR) Applications and Techniques (6 papers). Arwa Dabbech collaborates with scholars based in United Kingdom, France and South Africa. Arwa Dabbech's co-authors include Yves Wiaux, O. Smirnov, C. Ferrari, Jonathan S. Kenyon, E. Slezak, Matthieu Terris, Audrey Repetti, William Henry Jackson, R. A. Perley and Chao Tang and has published in prestigious journals such as Monthly Notices of the Royal Astronomical Society, The Astrophysical Journal Supplement Series and Astronomy and Astrophysics.

In The Last Decade

Arwa Dabbech

20 papers receiving 265 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Arwa Dabbech United Kingdom 12 245 130 79 74 67 22 312
Urvashi Rau United States 8 331 1.4× 86 0.7× 47 0.6× 40 0.5× 36 0.5× 17 373
Frederic R. Schwab United States 11 426 1.7× 97 0.7× 31 0.4× 43 0.6× 36 0.5× 18 501
J. E. Noordam Netherlands 8 244 1.0× 131 1.0× 11 0.1× 31 0.4× 37 0.6× 26 310
Matthieu Terris United Kingdom 7 50 0.2× 28 0.2× 67 0.8× 63 0.9× 16 0.2× 13 162
Luke Pratley New Zealand 9 199 0.8× 33 0.3× 21 0.3× 19 0.3× 13 0.2× 15 252
J. P. Hamaker Netherlands 8 463 1.9× 228 1.8× 21 0.3× 33 0.4× 69 1.0× 15 508
K. Golap United States 7 387 1.6× 111 0.9× 20 0.3× 28 0.4× 37 0.6× 19 414
Laura Wolz United Kingdom 13 568 2.3× 67 0.5× 30 0.4× 37 0.5× 38 0.6× 22 622
Philipp Arras Germany 7 164 0.7× 28 0.2× 10 0.1× 21 0.3× 13 0.2× 15 232
Yougang Wang China 14 446 1.8× 24 0.2× 12 0.2× 22 0.3× 15 0.2× 54 516

Countries citing papers authored by Arwa Dabbech

Since Specialization
Citations

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

Fields of papers citing papers by Arwa Dabbech

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Arwa Dabbech

This figure shows the co-authorship network connecting the top 25 collaborators of Arwa Dabbech. A scholar is included among the top collaborators of Arwa Dabbech 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 Arwa Dabbech. Arwa Dabbech 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.
Dabbech, Arwa, et al.. (2025). Toward a Robust R2D2 Paradigm for Radio-interferometric Imaging: Revisiting Deep Neural Network Training and Architecture. The Astrophysical Journal Supplement Series. 280(2). 63–63.
2.
Dabbech, Arwa, et al.. (2024). CLEANing Cygnus A Deep and Fast with R2D2. The Astrophysical Journal Letters. 966(2). L34–L34. 8 indexed citations
3.
Dabbech, Arwa, et al.. (2024). The R2D2 Deep Neural Network Series Paradigm for Fast Precision Imaging in Radio Astronomy. The Astrophysical Journal Supplement Series. 273(1). 3–3. 11 indexed citations
4.
Dabbech, Arwa, et al.. (2023). Scalable precision wide-field imaging in radio interferometry – II. AIRI validated on ASKAP data. Monthly Notices of the Royal Astronomical Society. 522(4). 5576–5587. 11 indexed citations
5.
Dabbech, Arwa, et al.. (2023). Scalable precision wide-field imaging in radio interferometry: I. uSARA validated on ASKAP data. Monthly Notices of the Royal Astronomical Society. 522(4). 5558–5575. 8 indexed citations
6.
Dabbech, Arwa, et al.. (2022). Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA) – II. Code and real data proof of concept. Monthly Notices of the Royal Astronomical Society. 521(1). 20–34. 6 indexed citations
7.
Dabbech, Arwa, Matthieu Terris, William Henry Jackson, et al.. (2022). First AI for Deep Super-resolution Wide-field Imaging in Radio Astronomy: Unveiling Structure in ESO 137-006. The Astrophysical Journal Letters. 939(1). L4–L4. 21 indexed citations
8.
Terris, Matthieu, Arwa Dabbech, Chao Tang, & Yves Wiaux. (2022). Image reconstruction algorithms in radio interferometry: From handcrafted to learned regularization denoisers. Monthly Notices of the Royal Astronomical Society. 518(1). 604–622. 26 indexed citations
9.
Dabbech, Arwa, et al.. (2022). Parallel faceted imaging in radio interferometry via proximal splitting (Faceted HyperSARA): I. Algorithm and simulations. Monthly Notices of the Royal Astronomical Society. 521(1). 1–19. 14 indexed citations
10.
Dabbech, Arwa, Audrey Repetti, R. A. Perley, O. Smirnov, & Yves Wiaux. (2021). Cygnus A jointly calibrated and imaged via non-convex optimization from VLA data. Monthly Notices of the Royal Astronomical Society. 506(4). 4855–4876. 12 indexed citations
11.
Jiang, Ming, et al.. (2019). A Faceted Prior for Scalable Wideband Imaging: Application to Radio Astronomy.
12.
Dabbech, Arwa, et al.. (2019). Wideband super-resolution imaging in Radio Interferometry via low rankness and joint average sparsity models (HyperSARA). Monthly Notices of the Royal Astronomical Society. 489(1). 1230–1248. 14 indexed citations
13.
Repetti, Audrey, et al.. (2018). Time-Regularized Blind Deconvolution Approach for Radio Interferometry. 571. 475–479. 2 indexed citations
14.
Repetti, Audrey, et al.. (2017). Non-convex optimization for self-calibration of direction-dependent effects in radio interferometric imaging. Monthly Notices of the Royal Astronomical Society. 470(4). 3981–4006. 21 indexed citations
15.
Dabbech, Arwa, et al.. (2017). An accelerated splitting algorithm for radio-interferometric imaging: when natural and uniform weighting meet. Monthly Notices of the Royal Astronomical Society. 469(1). 938–949. 27 indexed citations
16.
Dabbech, Arwa, Laura Wolz, Luke Pratley, Jason D. McEwen, & Yves Wiaux. (2017). The w-effect in interferometric imaging: from a fast sparse measurement operator to superresolution. Monthly Notices of the Royal Astronomical Society. 471(4). 4300–4313. 11 indexed citations
17.
Dabbech, Arwa, et al.. (2015). MORESANE: MOdel REconstruction by Synthesis-ANalysis Estimators. Astronomy and Astrophysics. 576. A7–A7. 50 indexed citations
18.
Ferrari, C., Arwa Dabbech, O. Smirnov, et al.. (2015). Non-thermal emission from galaxy clusters: feasibility study with SKA. arXiv (Cornell University). 3 indexed citations
19.
20.
Dabbech, Arwa, et al.. (2012). Astronomical image deconvolution using sparse priors: An analysis-by-synthesis approach. 3665–3668. 5 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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