A. Mahabal

15.0k total citations
160 papers, 3.1k citations indexed

About

A. Mahabal is a scholar working on Astronomy and Astrophysics, Instrumentation and Nuclear and High Energy Physics. According to data from OpenAlex, A. Mahabal has authored 160 papers receiving a total of 3.1k indexed citations (citations by other indexed papers that have themselves been cited), including 106 papers in Astronomy and Astrophysics, 29 papers in Instrumentation and 26 papers in Nuclear and High Energy Physics. Recurrent topics in A. Mahabal's work include Gamma-ray bursts and supernovae (63 papers), Galaxies: Formation, Evolution, Phenomena (37 papers) and Stellar, planetary, and galactic studies (36 papers). A. Mahabal is often cited by papers focused on Gamma-ray bursts and supernovae (63 papers), Galaxies: Formation, Evolution, Phenomena (37 papers) and Stellar, planetary, and galactic studies (36 papers). A. Mahabal collaborates with scholars based in United States, United Kingdom and France. A. Mahabal's co-authors include S. G. Djorgovski, M. J. Graham, A. J. Drake, Daniel Stern, C. Donalek, E. Christensen, Eilat Glikman, Steve Larson, S. Castro and S. M. Larson and has published in prestigious journals such as Nature, Science and SHILAP Revista de lepidopterología.

In The Last Decade

A. Mahabal

147 papers receiving 2.9k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
A. Mahabal 2.5k 615 554 195 175 160 3.1k
M. J. Graham 2.8k 1.1× 594 1.0× 539 1.0× 184 0.9× 173 1.0× 167 3.4k
A. J. Drake 2.1k 0.8× 480 0.8× 407 0.7× 162 0.8× 162 0.9× 88 2.5k
C. Donalek 854 0.3× 198 0.3× 187 0.3× 158 0.8× 88 0.5× 43 1.3k
Jake Vanderplas 981 0.4× 278 0.5× 194 0.4× 90 0.5× 108 0.6× 23 1.7k
Ramin Skibba 2.8k 1.1× 1.7k 2.8× 253 0.5× 284 1.5× 48 0.3× 45 3.0k
Michael S. Warren 2.0k 0.8× 772 1.3× 505 0.9× 114 0.6× 393 2.2× 77 3.3k
Kyle Willett 1.6k 0.6× 840 1.4× 160 0.3× 403 2.1× 107 0.6× 24 2.1k
Robert J. Brunner 2.6k 1.0× 916 1.5× 474 0.9× 237 1.2× 161 0.9× 61 3.5k
G. Fasano 3.1k 1.2× 1.8k 3.0× 340 0.6× 160 0.8× 39 0.2× 98 3.6k
A. Franceschini 4.4k 1.7× 2.0k 3.3× 1.1k 2.0× 94 0.5× 49 0.3× 147 4.9k

Countries citing papers authored by A. Mahabal

Since Specialization
Citations

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

Fields of papers citing papers by A. Mahabal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of A. Mahabal

This figure shows the co-authorship network connecting the top 25 collaborators of A. Mahabal. A scholar is included among the top collaborators of A. Mahabal 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 A. Mahabal. A. Mahabal 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.
Sharma, Y., A. Mahabal, J. Sollerman, et al.. (2025). CCSNscore: A Multi-input Deep Learning Tool for Classification of Core-collapse Supernovae Using SED-machine Spectra. Publications of the Astronomical Society of the Pacific. 137(3). 34507–34507. 2 indexed citations
2.
Graham, M. J., Barry McKernan, K. E. Saavik Ford, et al.. (2025). An extremely luminous flare recorded from a supermassive black hole. Nature Astronomy. 10(1). 154–164.
3.
Chan, M., J. McIver, A. Mahabal, et al.. (2024). GWSkyNet. II. A Refined Machine-learning Pipeline for Real-time Classification of Public Gravitational Wave Alerts. The Astrophysical Journal. 972(1). 50–50. 2 indexed citations
4.
Roestel, Jan van, Eric C. Bellm, J. S. Bloom, et al.. (2024). Four new eclipsing accreting ultracompact white dwarf binaries found with the Zwicky Transient Facility. Astronomy and Astrophysics. 683. L10–L10. 3 indexed citations
5.
Stein, Robert, A. Mahabal, Simeon Reusch, et al.. (2024). tdescore: An Accurate Photometric Classifier for Tidal Disruption Events. The Astrophysical Journal Letters. 965(2). L14–L14. 9 indexed citations
6.
Miller, Adam A., M. W. Coughlin, C. Fremling, et al.. (2024). The Zwicky Transient Facility Bright Transient Survey. III. BTSbot: Automated Identification and Follow-up of Bright Transients with Deep Learning. The Astrophysical Journal. 972(1). 7–7. 9 indexed citations
7.
Andreoni, Igor, M. W. Coughlin, A. W. Criswell, et al.. (2023). Enabling kilonova science with Nancy Grace Roman Space Telescope. Astroparticle Physics. 155. 102904–102904. 10 indexed citations
8.
Graham, M. J., Barry McKernan, K. E. Saavik Ford, et al.. (2023). A Light in the Dark: Searching for Electromagnetic Counterparts to Black Hole–Black Hole Mergers in LIGO/Virgo O3 with the Zwicky Transient Facility. The Astrophysical Journal. 942(2). 99–99. 58 indexed citations
9.
Rodriguez, Antonio C., S. R. Kulkarni, Thomas A. Prince, et al.. (2023). Discovery of Two Polars from a Crossmatch of ZTF and the SRG/eFEDS X-Ray Catalog. The Astrophysical Journal. 945(2). 141–141. 9 indexed citations
10.
Freytag, Johann Christoph, J. Nordin, Rahul Biswas, et al.. (2022). SNGuess: A method for the selection of young extragalactic transients. Astronomy and Astrophysics. 665. A99–A99. 6 indexed citations
11.
Cabero, M., et al.. (2021). GWSkyNet-Multi: A Machine Learning Multi-Class Classifier for LIGO-Virgo Public Alerts. arXiv (Cornell University). 8 indexed citations
12.
Sollerman, J., T. W. Chen, Erik C. Kool, et al.. (2021). Is supernova SN 2020faa an iPTF14hls look-alike?. Springer Link (Chiba Institute of Technology). 15 indexed citations
13.
Caiazzo, Ilaria, Kevin B. Burdge, Jeremy Heyl, et al.. (2021). A highly magnetized and rapidly rotating white dwarf as small as the Moon. Nature. 595(7865). 39–42. 78 indexed citations
14.
Ngeow, Chow‐Choong, Eric C. Bellm, Dmitry A. Duev, et al.. (2021). Zwicky Transient Facility and Globular Clusters: the Period–Luminosity and Period–Luminosity–Color Relations for Late-type Contact Binaries. The Astronomical Journal. 162(2). 63–63. 7 indexed citations
15.
Szkody, Paula, Jan van Roestel, Anna Y. Q. Ho, et al.. (2021). Cataclysmic Variables in the Second Year of the Zwicky Transient Facility. The Astronomical Journal. 162(3). 94–94. 7 indexed citations
16.
Duev, Dmitry A., Bryce Bolin, M. J. Graham, et al.. (2021). Tails: Chasing Comets with the Zwicky Transient Facility and Deep Learning. The Astronomical Journal. 161(5). 218–218. 5 indexed citations
17.
Roestel, Jan van, Thomas Kupfer, Paula Szkody, et al.. (2021). A Systematic Search for Outbursting AM CVn Systems with the Zwicky Transient Facility. The Astronomical Journal. 162(3). 113–113. 14 indexed citations
18.
Hammerstein, Erica, Suvi Gezari, Sjoert van Velzen, et al.. (2021). Tidal Disruption Event Hosts Are Green and Centrally Concentrated: Signatures of a Post-merger System. The Astrophysical Journal Letters. 908(1). L20–L20. 48 indexed citations
19.
Huppenkothen, Daniela, Lynne Jones, Bryce Bolin, et al.. (2021). Characterizing Sparse Asteroid Light Curves with Gaussian Processes. The Astronomical Journal. 163(1). 29–29. 2 indexed citations
20.
Duev, Dmitry A., A. Mahabal, Quanzhi Ye, et al.. (2019). DeepStreaks: identifying fast-moving objects in the Zwicky Transient Facility data with deep learning. Monthly Notices of the Royal Astronomical Society. 486(3). 4158–4165. 26 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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