M. Drago

86.4k total citations
28 papers, 680 citations indexed

About

M. Drago is a scholar working on Astronomy and Astrophysics, Geophysics and Oceanography. According to data from OpenAlex, M. Drago has authored 28 papers receiving a total of 680 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Astronomy and Astrophysics, 8 papers in Geophysics and 4 papers in Oceanography. Recurrent topics in M. Drago's work include Pulsars and Gravitational Waves Research (26 papers), Gamma-ray bursts and supernovae (18 papers) and Seismic Waves and Analysis (5 papers). M. Drago is often cited by papers focused on Pulsars and Gravitational Waves Research (26 papers), Gamma-ray bursts and supernovae (18 papers) and Seismic Waves and Analysis (5 papers). M. Drago collaborates with scholars based in Italy, United States and Switzerland. M. Drago's co-authors include G. A. Prodi, G. Vedovato, F. Salemi, S. Klimenko, C. Lazzaro, Shubhanshu Tiwari, G. Mitselmakher, V. Tiwari, K. Ackley and I. Di Palma and has published in prestigious journals such as Physical review. D, Classical and Quantum Gravity and Publications of the Astronomical Society of the Pacific.

In The Last Decade

M. Drago

27 papers receiving 661 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M. Drago Italy 13 662 185 95 87 66 28 680
T. Dal Canton United States 14 715 1.1× 116 0.6× 117 1.2× 80 0.9× 55 0.8× 26 748
K. Wette Australia 15 619 0.9× 179 1.0× 66 0.7× 171 2.0× 71 1.1× 31 643
K. C. Cannon United States 14 634 1.0× 120 0.6× 124 1.3× 109 1.3× 44 0.7× 27 651
L. K. Nuttall United Kingdom 11 791 1.2× 134 0.7× 216 2.3× 69 0.8× 34 0.5× 20 804
D. Davis United States 8 379 0.6× 124 0.7× 52 0.5× 89 1.0× 37 0.6× 13 407
V. Tiwari United Kingdom 11 568 0.9× 123 0.7× 84 0.9× 68 0.8× 29 0.4× 17 582
P. Leaci Italy 13 512 0.8× 133 0.7× 127 1.3× 131 1.5× 61 0.9× 36 547
K. Ackley United States 9 582 0.9× 109 0.6× 108 1.1× 60 0.7× 28 0.4× 17 596
T. D. Abbott United States 5 650 1.0× 113 0.6× 145 1.5× 82 0.9× 28 0.4× 9 691
S. Abraham United States 5 960 1.5× 164 0.9× 221 2.3× 114 1.3× 26 0.4× 5 1.0k

Countries citing papers authored by M. Drago

Since Specialization
Citations

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

Fields of papers citing papers by M. Drago

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Drago

This figure shows the co-authorship network connecting the top 25 collaborators of M. Drago. A scholar is included among the top collaborators of M. Drago 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 M. Drago. M. Drago 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.
Cerdá–Durán, P., et al.. (2025). Phenomenological gravitational waveforms for core-collapse supernovae. Physical review. D. 111(8). 1 indexed citations
2.
Xu, Yumeng, Shubhanshu Tiwari, & M. Drago. (2024). PycWB: A user-friendly, Modular, and python-based framework for gravitational wave unmodelled search. SoftwareX. 26. 101639–101639. 1 indexed citations
3.
Szczepańczyk, M. J., F. Salemi, S. Bini, et al.. (2023). Search for gravitational-wave bursts in the third Advanced LIGO-Virgo run with coherent WaveBurst enhanced by machine learning. Physical review. D. 107(6). 12 indexed citations
4.
Miani, A., C. Lazzaro, G. A. Prodi, et al.. (2023). Constraints on the amplitude of gravitational wave echoes from black hole ringdown using minimal assumptions. Physical review. D. 108(6). 3 indexed citations
5.
Tringali, M. C., Anna Puecher, C. Lazzaro, et al.. (2023). Morphology-independent characterization method of postmerger gravitational wave emission from binary neutron star coalescences. Classical and Quantum Gravity. 40(22). 225008–225008. 6 indexed citations
6.
Drago, M., Haakon Andresen, I. Di Palma, Irene Tamborra, & A. Torres-Forné. (2023). Multimessenger observations of core-collapse supernovae: Exploiting the standing accretion shock instability. Physical review. D. 108(10). 8 indexed citations
7.
Bini, S., G. Vedovato, M. Drago, F. Salemi, & G. A. Prodi. (2023). An autoencoder neural network integrated into gravitational-wave burst searches to improve the rejection of noise transients. Classical and Quantum Gravity. 40(13). 135008–135008. 12 indexed citations
8.
Bini, S., G. Ciani, R. Ciolfi, et al.. (2022). . Research Publications (Maastricht University). 16 indexed citations
9.
Tiwari, Shubhanshu, M. Drago, G. A. Prodi, D. Keitel, & C. Lazzaro. (2022). Prospects for detecting and localizing short-duration transient gravitational waves from glitching neutron stars without electromagnetic counterparts. arXiv (Cornell University). 14 indexed citations
10.
Drago, M., S. Klimenko, C. Lazzaro, et al.. (2021). coherent WaveBurst, a pipeline for unmodeled gravitational-wave data analysis. SoftwareX. 14. 100678–100678. 50 indexed citations
11.
Vedovato, G., E. Milotti, G. A. Prodi, et al.. (2021). Minimally-modeled search of higher multipole gravitational-wave radiation in compact binary coalescences. Classical and Quantum Gravity. 39(4). 45001–45001. 3 indexed citations
12.
Szczepańczyk, M. J., S. Klimenko, I. Bartos, et al.. (2021). Observing an intermediate-mass black hole GW190521 with minimal assumptions. Physical review. D. 103(8). 18 indexed citations
13.
Portilla, M. Lopez, et al.. (2021). Deep learning for core-collapse supernova detection. Physical review. D. 103(6). 38 indexed citations
14.
Salemi, F., E. Milotti, G. A. Prodi, et al.. (2019). Wider look at the gravitational-wave transients from GWTC-1 using an unmodeled reconstruction method. Physical review. D. 100(4). 19 indexed citations
15.
Astone, P., P. Cerdá–Durán, I. Di Palma, et al.. (2018). New method to observe gravitational waves emitted by core collapse supernovae. Physical review. D. 98(12). 46 indexed citations
16.
Kanner, J. B., T. B. Littenberg, Neil J. Cornish, et al.. (2016). Leveraging waveform complexity for confident detection of gravitational waves. Physical review. D. 93(2). 31 indexed citations
17.
Tiwari, V., M. Drago, В. В. Фролов, et al.. (2015). Regression of environmental noise in LIGO data. Classical and Quantum Gravity. 32(16). 165014–165014. 24 indexed citations
18.
Mazzolo, G., F. Salemi, M. Drago, et al.. (2014). Prospects for intermediate mass black hole binary searches with advanced gravitational-wave detectors. Physical review. D. Particles, fields, gravitation, and cosmology. 90(6). 2 indexed citations
19.
Klimenko, S., G. Vedovato, M. Drago, et al.. (2011). Localization of gravitational wave sources with networks of advanced detectors. Physical review. D. Particles, fields, gravitation, and cosmology. 83(10). 61 indexed citations
20.
Drago, M.. (2010). Search for transient gravitational wave signals with a known waveform in the LIGO Virgo network of interferometric detectors using a fully coherent algorithm. 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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