C. Messenger

94.9k total citations · 1 hit paper
65 papers, 1.6k citations indexed

About

C. Messenger is a scholar working on Astronomy and Astrophysics, Geophysics and Oceanography. According to data from OpenAlex, C. Messenger has authored 65 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Astronomy and Astrophysics, 13 papers in Geophysics and 11 papers in Oceanography. Recurrent topics in C. Messenger's work include Pulsars and Gravitational Waves Research (52 papers), Gamma-ray bursts and supernovae (29 papers) and Astrophysical Phenomena and Observations (16 papers). C. Messenger is often cited by papers focused on Pulsars and Gravitational Waves Research (52 papers), Gamma-ray bursts and supernovae (29 papers) and Astrophysical Phenomena and Observations (16 papers). C. Messenger collaborates with scholars based in United Kingdom, United States and Germany. C. Messenger's co-authors include I. S. Heng, M. J. Williams, J. Read, Hunter Gabbard, J. Veitch, R. Prix, M. Chan, F. J. Hayes, Francesco Tonolini and Roderick Murray‐Smith and has published in prestigious journals such as Physical Review Letters, The Astrophysical Journal and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

C. Messenger

59 papers receiving 1.6k citations

Hit Papers

Bayesian parameter estimation using conditional variation... 2021 2026 2022 2024 2021 40 80 120

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
C. Messenger United Kingdom 22 1.5k 295 230 211 186 65 1.6k
T. B. Littenberg United States 25 1.8k 1.2× 419 1.4× 388 1.7× 233 1.1× 161 0.9× 53 1.9k
A. Nitz Germany 26 2.0k 1.3× 383 1.3× 253 1.1× 356 1.7× 111 0.6× 55 2.0k
I. S. Heng United Kingdom 17 1.0k 0.7× 184 0.6× 119 0.5× 259 1.2× 99 0.5× 64 1.1k
J. Veitch United Kingdom 27 2.5k 1.6× 330 1.1× 351 1.5× 478 2.3× 114 0.6× 86 2.6k
S. Babak France 20 1.9k 1.3× 221 0.7× 220 1.0× 482 2.3× 68 0.4× 37 2.0k
R. C. Essick United States 20 1.4k 0.9× 362 1.2× 300 1.3× 251 1.2× 61 0.3× 35 1.4k
A. Vecchio United Kingdom 31 2.9k 1.9× 392 1.3× 476 2.1× 453 2.1× 126 0.7× 86 3.0k
V. Raymond United Kingdom 23 2.0k 1.3× 430 1.5× 385 1.7× 322 1.5× 75 0.4× 35 2.0k
C. P. L. Berry United Kingdom 24 2.4k 1.5× 177 0.6× 211 0.9× 686 3.3× 59 0.3× 48 2.5k
R. Prix Germany 22 1.3k 0.9× 465 1.6× 391 1.7× 130 0.6× 137 0.7× 60 1.4k

Countries citing papers authored by C. Messenger

Since Specialization
Citations

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

Fields of papers citing papers by C. Messenger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of C. Messenger

This figure shows the co-authorship network connecting the top 25 collaborators of C. Messenger. A scholar is included among the top collaborators of C. Messenger 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 C. Messenger. C. Messenger 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.
Hu, Qian, et al.. (2025). Decoding Long-duration Gravitational Waves from Binary Neutron Stars with Machine Learning: Parameter Estimation and Equations of State. The Astrophysical Journal Letters. 987(1). L17–L17. 3 indexed citations
2.
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
3.
Stachurski, F., C. Messenger, & M. Hendry. (2024). Cosmological inference using gravitational waves and normalizing flows. Physical review. D. 109(12). 5 indexed citations
4.
Williams, M. J., J. Veitch, & C. Messenger. (2023). Importance nested sampling with normalising flows. Machine Learning Science and Technology. 4(3). 35011–35011. 22 indexed citations
5.
Li, Yufeng, I. S. Heng, M. Chan, C. Messenger, & Xi-Long Fan. (2022). Exploring the sky localization and early warning capabilities of third generation gravitational wave detectors in three-detector network configurations. Physical review. D. 105(4). 28 indexed citations
6.
Galaudage, S., K. Wette, D. K. Galloway, & C. Messenger. (2021). Deep searches for X-ray pulsations from Scorpius X-1 and Cygnus X-2 in support of continuous gravitational wave searches. arXiv (Cornell University). 8 indexed citations
7.
Gabbard, Hunter, C. Messenger, I. S. Heng, Francesco Tonolini, & Roderick Murray‐Smith. (2021). Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy. Nature Physics. 18(1). 112–117. 127 indexed citations breakdown →
8.
Veitch, J., et al.. (2020). Are stellar-mass binary black hole mergers isotropically distributed?. Monthly Notices of the Royal Astronomical Society. 501(1). 970–977. 17 indexed citations
9.
Gray, R., I. Magaña Hernandez, H. Qi, et al.. (2020). Cosmological inference using gravitational wave standard sirens: A mock data analysis. Physical review. D. 101(12). 122 indexed citations
10.
Dreißigacker, Christoph, et al.. (2019). Deep-learning continuous gravitational waves. Physical review. D. 100(4). 55 indexed citations
11.
Gabbard, Hunter, M. J. Williams, F. J. Hayes, & C. Messenger. (2018). Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy. Physical Review Letters. 120(14). 141103–141103. 151 indexed citations
12.
Pitkin, M., C. Messenger, & Xi-Long Fan. (2018). Hierarchical Bayesian method for detecting continuous gravitational waves from an ensemble of pulsars. Physical review. D. 98(6). 10 indexed citations
13.
Pozzo, W. Del, Tjonnie G. F. Li, & C. Messenger. (2017). Cosmological inference using only gravitational wave observations of binary neutron stars. Physical review. D. 95(4). 45 indexed citations
14.
Fan, Xi-Long, C. Messenger, & I. S. Heng. (2017). Probing Intrinsic Properties of Short Gamma-Ray Bursts with Gravitational Waves. Physical Review Letters. 119(18). 181102–181102. 14 indexed citations
15.
Chan, M., Yi-Ming Hu, C. Messenger, M. Hendry, & I. S. Heng. (2017). MAXIMIZING THE DETECTION PROBABILITY OF KILONOVAE ASSOCIATED WITH GRAVITATIONAL WAVE OBSERVATIONS. The Astrophysical Journal. 834(1). 84–84. 14 indexed citations
16.
Capano, C. D., T. Dent, C. Hanna, et al.. (2017). Systematic errors in estimation of gravitational-wave candidate significance. Physical review. D. 96(8). 16 indexed citations
17.
Messenger, C. & J. Read. (2012). Measuring a Cosmological Distance-Redshift Relationship Using Only Gravitational Wave Observations of Binary Neutron Star Coalescences. Physical Review Letters. 108(9). 91101–91101. 148 indexed citations
18.
19.
Messenger, C., R. Prix, & M. A. Papa. (2009). Random template banks and relaxed lattice coverings. Physical review. D. Particles, fields, gravitation, and cosmology. 79(10). 58 indexed citations
20.
Messenger, C., et al.. (2007). Dyscalculia in Harrow.. 39–44. 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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