Connor Bottrell

1.5k total citations
45 papers, 974 citations indexed

About

Connor Bottrell is a scholar working on Astronomy and Astrophysics, Instrumentation and Computer Vision and Pattern Recognition. According to data from OpenAlex, Connor Bottrell has authored 45 papers receiving a total of 974 indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Astronomy and Astrophysics, 22 papers in Instrumentation and 16 papers in Computer Vision and Pattern Recognition. Recurrent topics in Connor Bottrell's work include Galaxies: Formation, Evolution, Phenomena (38 papers), Astronomy and Astrophysical Research (22 papers) and Advanced Vision and Imaging (13 papers). Connor Bottrell is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (38 papers), Astronomy and Astrophysical Research (22 papers) and Advanced Vision and Imaging (13 papers). Connor Bottrell collaborates with scholars based in Canada, Japan and Australia. Connor Bottrell's co-authors include Sara L. Ellison, Luc Simard, David R. Patton, Paul Torrey, Maan H Hani, Stephen Gwyn, Hossen Teimoorinia, Jean‐Charles Cuillandre, Jorge Moreno and Christopher C. Hayward and has published in prestigious journals such as The Astrophysical Journal, Monthly Notices of the Royal Astronomical Society and Astronomy and Astrophysics.

In The Last Decade

Connor Bottrell

42 papers receiving 875 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Connor Bottrell Canada 17 860 513 169 70 66 45 974
H. Domínguez Sánchez Spain 18 897 1.0× 595 1.2× 129 0.8× 38 0.5× 51 0.8× 39 1.0k
Hossen Teimoorinia Canada 16 859 1.0× 505 1.0× 94 0.6× 50 0.7× 48 0.7× 32 965
Rebecca Smethurst United Kingdom 24 969 1.1× 539 1.1× 136 0.8× 30 0.4× 41 0.6× 35 1.1k
Sandor Kruk United Kingdom 19 739 0.9× 390 0.8× 119 0.7× 24 0.3× 50 0.8× 39 899
Jillian M. Scudder United States 14 1.1k 1.2× 597 1.2× 96 0.6× 71 1.0× 35 0.5× 24 1.1k
Preethi Nair United States 13 730 0.8× 433 0.8× 88 0.5× 37 0.5× 19 0.3× 20 792
Shiyin Shen China 16 1.3k 1.5× 758 1.5× 54 0.3× 50 0.7× 36 0.5× 71 1.3k
Maan H Hani Canada 15 573 0.7× 319 0.6× 94 0.6× 53 0.8× 39 0.6× 24 640
Joanna Woo Canada 18 1.2k 1.4× 754 1.5× 58 0.3× 34 0.5× 26 0.4× 27 1.2k
R. Armstrong United States 11 655 0.8× 242 0.5× 115 0.7× 167 2.4× 33 0.5× 24 779

Countries citing papers authored by Connor Bottrell

Since Specialization
Citations

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

Fields of papers citing papers by Connor Bottrell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Connor Bottrell

This figure shows the co-authorship network connecting the top 25 collaborators of Connor Bottrell. A scholar is included among the top collaborators of Connor Bottrell 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 Connor Bottrell. Connor Bottrell 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.
Stone, Connor, et al.. (2025). Echoes in the Noise: Posterior Samples of Faint Galaxy Surface Brightness Profiles with Score-based Likelihoods and Priors. The Astronomical Journal. 169(5). 254–254. 1 indexed citations
2.
Ferreira, Leonardo, et al.. (2025). Galaxy evolution in the Post-Merger Regime – I. Most merger-induced in situ stellar mass growth happens post-coalescence. Monthly Notices of the Royal Astronomical Society Letters. 538(1). L31–L36. 3 indexed citations
3.
Ellison, Sara L., et al.. (2025). Interacting galaxies in the IllustrisTNG simulations – IX: Mini mergers trigger AGNs in cosmological simulations. Monthly Notices of the Royal Astronomical Society. 544(2). 1673–1687.
4.
Roshan, Mahmood, J. A. L. Aguerri, Virginia Cuomo, et al.. (2025). The Tremaine-Weinberg method at high redshifts. Astronomy and Astrophysics. 701. A160–A160.
5.
Ferreira, Leonardo, et al.. (2024). The effect of image quality on galaxy merger identification with deep learning. Monthly Notices of the Royal Astronomical Society. 534(3). 2533–2550. 3 indexed citations
6.
Bottrell, Connor, et al.. (2024). ERGO-ML: comparing IllustrisTNG and HSC galaxy images via contrastive learning. Monthly Notices of the Royal Astronomical Society. 528(4). 7411–7439. 9 indexed citations
7.
Power, Chris, et al.. (2024). Predicting the scaling relations between the dark matter halo mass and observables from generalised profiles II: Intracluster gas emission. Publications of the Astronomical Society of Australia. 41. 2 indexed citations
8.
Shabala, Stanislav S., et al.. (2024). Predicting the non-thermal pressure in galaxy clusters. Publications of the Astronomical Society of Australia. 41. 1 indexed citations
9.
Margalef-Bentabol, Berta, Liming Wang, Antonio La Marca, et al.. (2024). Galaxy merger challenge: A comparison study between machine learning-based detection methods. Springer Link (Chiba Institute of Technology). 3 indexed citations
10.
Lin, Yen‐Ting, Hsi-Yu Schive, Masamune Oguri, et al.. (2024). A Systematic Search of Distant Superclusters with the Subaru Hyper Suprime-Cam Survey. The Astrophysical Journal. 975(2). 200–200. 1 indexed citations
11.
Ferreira, Leonardo, Sara L. Ellison, David R. Patton, et al.. (2024). Galaxy mergers in UNIONS – I. A simulation-driven hybrid deep learning ensemble for pure galaxy merger classification. Monthly Notices of the Royal Astronomical Society. 533(3). 2547–2569. 11 indexed citations
12.
Tang, Shenli, J. D. Silverman, Hassen M. Yesuf, et al.. (2023). Morphological asymmetries of quasar host galaxies with Subaru Hyper Suprime-Cam. Monthly Notices of the Royal Astronomical Society. 521(4). 5272–5297. 10 indexed citations
13.
Kalita, Boris S., J. D. Silverman, E. Daddi, et al.. (2023). A Rest-frame Near-IR Study of Clumps in Galaxies at 1 < z < 2 Using JWST/NIRCam: Connection to Galaxy Bulges. The Astrophysical Journal. 960(1). 25–25. 13 indexed citations
14.
Jian, Hung-Yu, Lihwai Lin, Bau-Ching Hsieh, et al.. (2023). Radial and Local Density Dependence of Star Formation Properties in Galaxy Clusters from the Hyper Suprime-Cam Survey. The Astrophysical Journal. 957(2). 85–85. 2 indexed citations
15.
Silverman, J. D., Tilman Hartwig, Junyao Li, et al.. (2023). A Machine-learning Approach to Assessing the Presence of Substructure in Quasar-host Galaxies Using the Hyper Suprime-cam Subaru Strategic Program. The Astrophysical Journal. 947(1). 30–30. 1 indexed citations
16.
Sánchez, H. Domínguez, Garreth Martin, Ivana Damjanov, et al.. (2023). Identification of tidal features in deep optical galaxy images with convolutional neural networks. Monthly Notices of the Royal Astronomical Society. 521(3). 3861–3872. 18 indexed citations
17.
Bottrell, Connor, C. D. Wilson, Jorge Moreno, et al.. (2023). Molecular Gas and Star Formation in Nearby Starburst Galaxy Mergers. The Astrophysical Journal. 950(1). 56–56. 11 indexed citations
18.
Bottrell, Connor, Hassen M. Yesuf, Gergö Popping, et al.. (2023). IllustrisTNG in the HSC-SSP: image data release and the major role of mini mergers as drivers of asymmetry and star formation. Monthly Notices of the Royal Astronomical Society. 527(3). 6506–6539. 33 indexed citations
19.
Bluck, Asa F. L., Connor Bottrell, Hossen Teimoorinia, et al.. (2019). What shapes a galaxy? – unraveling the role of mass, environment, and star formation in forming galactic structure. Monthly Notices of the Royal Astronomical Society. 485(1). 666–696. 50 indexed citations
20.
Rodríguez-Gómez, Vicente, Dylan Nelson, Annalisa Pillepich, et al.. (2019). The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning. Monthly Notices of the Royal Astronomical Society. 489(2). 1859–1879. 60 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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