E. Tolley

24.6k total citations
10 papers, 31 citations indexed

About

E. Tolley is a scholar working on Astronomy and Astrophysics, Nuclear and High Energy Physics and Computer Vision and Pattern Recognition. According to data from OpenAlex, E. Tolley has authored 10 papers receiving a total of 31 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Astronomy and Astrophysics, 5 papers in Nuclear and High Energy Physics and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in E. Tolley's work include Radio Astronomy Observations and Technology (4 papers), Particle physics theoretical and experimental studies (3 papers) and Galaxies: Formation, Evolution, Phenomena (3 papers). E. Tolley is often cited by papers focused on Radio Astronomy Observations and Technology (4 papers), Particle physics theoretical and experimental studies (3 papers) and Galaxies: Formation, Evolution, Phenomena (3 papers). E. Tolley collaborates with scholars based in Switzerland, Netherlands and United Kingdom. E. Tolley's co-authors include Jean‐Paul Kneib, Sambit K. Giri, Tianyue Chen, Florent Mertens, Andrei Mesinger, Stefano Corda, BoYu Gao, Meredith Franklin, P. Giromini and A. Galan and has published in prestigious journals such as The Astrophysical Journal, Monthly Notices of the Royal Astronomical Society and The European Physical Journal C.

In The Last Decade

E. Tolley

8 papers receiving 29 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
E. Tolley Switzerland	4	22	11	6	6	4	10	31
Daniela Breitman Italy	3	23 1.0×	11 1.0×	3 0.5×	9 1.5×	3 0.8×	4	31
C. Ravoux France	4	22 1.0×	8 0.7×	3 0.5×	4 0.7×	2 0.5×	9	36
Kushal Tirumala Israel	3	27 1.2×	7 0.6×	12 2.0×	5 0.8×	7 1.8×	3	46
B. Humphreys Australia	3	50 2.3×	26 2.4×	3 0.5×	13 2.2×	4 1.0×	4	54
Irene Abril-Cabezas United Kingdom	5	50 2.3×	10 0.9×	2 0.3×	10 1.7×	5 1.3×	6	62
M. H. Sarmiento Netherlands	2	23 1.0×	6 0.5×	3 0.5×	4 0.7×		2	32
Martina Karl Germany	3	19 0.9×	10 0.9×	2 0.3×	3 0.5×	3 0.8×	7	29
David MacMahon United States	3	70 3.2×	16 1.5×	2 0.3×	9 1.5×	5 1.3×	5	73
Hyoyin Gan China	5	34 1.5×	19 1.7×	2 0.3×	19 3.2×	7 1.8×	12	44
Martin Pokorný Czechia	3	30 1.4×	10 0.9×	4 0.7×	4 0.7×	2 0.5×	12	50

Countries citing papers authored by E. Tolley

Since Specialization

Citations

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

Fields of papers citing papers by E. Tolley

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

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

This figure shows the co-authorship network connecting the top 25 collaborators of E. Tolley. A scholar is included among the top collaborators of E. Tolley 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. Tolley. E. Tolley is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

10 of 10 papers shown

1.

Mishra, Abhilash & E. Tolley. (2025). SPINN: Advancing Cosmological Simulations of Fuzzy Dark Matter with Physics Informed Neural Networks. The Astrophysical Journal. 988(1). 114–114.

2.

Chen, Tianyue, et al.. (2024). The stability of deep learning for 21cm foreground removal across various sky models and frequency-dependent systematics. Monthly Notices of the Royal Astronomical Society. 532(2). 2615–2634. 1 indexed citations

3.

Giri, Sambit K., Tianyue Chen, Florent Mertens, et al.. (2024). Deep learning approach for identification of H ii regions during reionization in 21-cm observations – II. Foreground contamination. Monthly Notices of the Royal Astronomical Society. 528(3). 5212–5230. 13 indexed citations

4.

Tolley, E., et al.. (2024). Quantum radio astronomy: Data encodings and quantum image processing. Astronomy and Computing. 47. 100796–100796. 2 indexed citations

5.

Tolley, E., et al.. (2023). PINION: physics-informed neural network for accelerating radiative transfer simulations for cosmic reionization. Monthly Notices of the Royal Astronomical Society. 521(1). 902–915. 4 indexed citations

6.

Tolley, E., A. Galan, Austin Peel, et al.. (2022). Lightweight HI source finding for next generation radio surveys. Astronomy and Computing. 41. 100631–100631. 2 indexed citations

7.

Boveia, A., et al.. (2022). DarkFlux: A new tool to analyze indirect-detection spectra of next-generation dark matter models. Physics of the Dark Universe. 36. 101012–101012.

8.

Corda, Stefano, et al.. (2022). PMT: Power Measurement Toolkit. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 44–47. 4 indexed citations

9.

Chan, S. K., Meredith Franklin, P. Giromini, et al.. (2018). Search for the direct production of charginos and neutralinos in s√ = 13 TeV pp collisions with the ATLAS detector. The European Physical Journal C. 2 indexed citations

10.

Tolley, E.. (2016). Dark Matter searches with Mono-X signatures at the ATLAS experiment. CERN Bulletin. 107–107. 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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