E. Cuoco

90.4k total citations
21 papers, 240 citations indexed

About

E. Cuoco is a scholar working on Astronomy and Astrophysics, Geophysics and Nuclear and High Energy Physics. According to data from OpenAlex, E. Cuoco has authored 21 papers receiving a total of 240 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Astronomy and Astrophysics, 8 papers in Geophysics and 4 papers in Nuclear and High Energy Physics. Recurrent topics in E. Cuoco's work include Pulsars and Gravitational Waves Research (19 papers), Gamma-ray bursts and supernovae (8 papers) and Seismic Waves and Analysis (7 papers). E. Cuoco is often cited by papers focused on Pulsars and Gravitational Waves Research (19 papers), Gamma-ray bursts and supernovae (8 papers) and Seismic Waves and Analysis (7 papers). E. Cuoco collaborates with scholars based in Italy, United Kingdom and Spain. E. Cuoco's co-authors include José A. Font, A. Torres-Forné, J. Powell, A. Iess, F. Morawski, Antonio Marquina, I. S. Heng, M. Cavaglià, G. M. Guidi and B. Patricelli and has published in prestigious journals such as Monthly Notices of the Royal Astronomical Society, Sensors and Astronomy and Astrophysics.

In The Last Decade

E. Cuoco

20 papers receiving 225 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. Cuoco Italy 10 211 81 45 33 30 21 240
S. Kandhasamy United States 8 251 1.2× 70 0.9× 32 0.7× 32 1.0× 49 1.6× 12 278
Hunter Gabbard United Kingdom 3 242 1.1× 66 0.8× 28 0.6× 74 2.2× 29 1.0× 3 287
M. Rakhmanov United States 7 151 0.7× 39 0.5× 30 0.7× 17 0.5× 18 0.6× 12 185
Walid A. Majid United States 12 297 1.4× 35 0.4× 61 1.4× 22 0.7× 24 0.8× 50 349
A. S. Sengupta India 9 255 1.2× 59 0.7× 38 0.8× 12 0.4× 47 1.6× 19 285
J. L. Bougeret France 13 542 2.6× 55 0.7× 70 1.6× 22 0.7× 22 0.7× 26 564
E. Katsavounidis United States 12 518 2.5× 140 1.7× 99 2.2× 70 2.1× 82 2.7× 29 557
R. J. Riddolls Canada 13 204 1.0× 70 0.9× 21 0.5× 16 0.5× 118 3.9× 42 421
Yi Feng China 12 365 1.7× 27 0.3× 90 2.0× 11 0.3× 37 1.2× 42 402
S. Shah Netherlands 12 458 2.2× 49 0.6× 23 0.5× 8 0.2× 29 1.0× 17 503

Countries citing papers authored by E. Cuoco

Since Specialization
Citations

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

Fields of papers citing papers by E. Cuoco

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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

This figure shows the co-authorship network connecting the top 25 collaborators of E. Cuoco. A scholar is included among the top collaborators of E. Cuoco 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. Cuoco. E. Cuoco 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.
Cuoco, E., M. Cavaglià, I. S. Heng, D. Keitel, & C. Messenger. (2025). Applications of machine learning in gravitational-wave research with current interferometric detectors. ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam). 28(1). 7 indexed citations
2.
Razzano, M., et al.. (2025). Can Transformers help us perform parameter estimation of overlapping signals in gravitational wave detectors?. Classical and Quantum Gravity. 42(18). 185012–185012. 1 indexed citations
3.
Powell, J., A. Iess, M. Obergaulinger, et al.. (2024). Determining the core-collapse supernova explosion mechanism with current and future gravitational-wave observatories. Physical review. D. 109(6). 10 indexed citations
4.
Iess, A., et al.. (2022). LSTM and CNN application for core-collapse supernova search in gravitational wave real data. Astronomy and Astrophysics. 669. A42–A42. 12 indexed citations
5.
Cuoco, E., B. Patricelli, A. Iess, & F. Morawski. (2022). Computational challenges for multimodal astrophysics. Nature Computational Science. 2(8). 479–485. 5 indexed citations
6.
Patricelli, B., M. G. Bernardini, Michela Mapelli, et al.. (2022). Prospects for multimessenger detection of binary neutron star mergers in the fourth LIGO–Virgo–KAGRA observing run. Monthly Notices of the Royal Astronomical Society. 513(3). 4159–4168. 23 indexed citations
7.
Cuoco, E., B. Patricelli, A. Iess, & F. Morawski. (2021). Multimodal Analysis of Gravitational Wave Signals and Gamma-Ray Bursts from Binary Neutron Star Mergers. Universe. 7(11). 394–394. 5 indexed citations
8.
Iess, A., E. Cuoco, F. Morawski, & J. Powell. (2020). Core-Collapse supernova gravitational-wave search and deep learning classification. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 25 indexed citations
9.
Torres-Forné, A., E. Cuoco, José A. Font, & Antonio Marquina. (2020). Application of dictionary learning to denoise LIGO’s blip noise transients. Physical review. D. 102(2). 24 indexed citations
10.
Lerga, Jonatan, et al.. (2020). Gravitational-Wave Burst Signals Denoising Based on the Adaptive Modification of the Intersection of Confidence Intervals Rule. Sensors. 20(23). 6920–6920. 6 indexed citations
11.
Cuoco, E., M. Razzano, & A. Utina. (2018). Wavelet-Based Classification of Transient Signals for Gravitational Wave Detectors. 2648–2652. 9 indexed citations
12.
Torres-Forné, A., E. Cuoco, Antonio Marquina, José A. Font, & José María Ibáñez. (2018). Total-variation methods for gravitational-wave denoising: Performance tests on Advanced LIGO data. Physical review. D. 98(8). 14 indexed citations
13.
Cuoco, E., I. S. Heng, José A. Font, et al.. (2017). Strategy for signal classification to improve data quality for Advanced Detectors gravitational-wave searches. Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna). 40(3). 124. 1 indexed citations
14.
Powell, J., A. Torres-Forné, E. Cuoco, et al.. (2017). Classification methods for noise transients in advanced gravitational-wave detectors II: Performance tests on Advanced LIGO data. Figshare. 47 indexed citations
15.
Colacino, C. N., et al.. (2007). Prospects for stochastic background searches using Virgo and LSC interferometers. Classical and Quantum Gravity. 24(19). S639–S648. 13 indexed citations
16.
Fabbroni, L., Marina Vannucci, E. Cuoco, et al.. (2005). Wavelet Tests for the Detection of Transients in the VIRGO Interferometric Gravitational Wave Detector. IEEE Transactions on Instrumentation and Measurement. 54(1). 151–162. 3 indexed citations
17.
Guidi, G. M., E. Cuoco, & A. Viceré. (2004). A power filter for the detection of burst events based on time–frequency spectrum estimation. Classical and Quantum Gravity. 21(5). S815–S820. 2 indexed citations
18.
Viceré, A., E. Cuoco, & G. M. Guidi. (2004). Performance of a  generalized  -filter  for the detection of burst events. Classical and Quantum Gravity. 21(5). S741–S747. 1 indexed citations
19.
Cuoco, E., et al.. (2004). Whitening of non-stationary noise from gravitational wave detectors. Classical and Quantum Gravity. 21(5). S801–S806. 10 indexed citations
20.
Cuoco, E., G Curci, & Michele Beccaria. (1998). Adaptive Identification of VIRGO-Like Noise Spectrum. CERN Bulletin. 524. 3 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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