J. Weller

36.5k total citations
69 papers, 2.7k citations indexed

About

J. Weller is a scholar working on Astronomy and Astrophysics, Nuclear and High Energy Physics and Instrumentation. According to data from OpenAlex, J. Weller has authored 69 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Astronomy and Astrophysics, 28 papers in Nuclear and High Energy Physics and 6 papers in Instrumentation. Recurrent topics in J. Weller's work include Galaxies: Formation, Evolution, Phenomena (45 papers), Cosmology and Gravitation Theories (39 papers) and Dark Matter and Cosmic Phenomena (13 papers). J. Weller is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (45 papers), Cosmology and Gravitation Theories (39 papers) and Dark Matter and Cosmic Phenomena (13 papers). J. Weller collaborates with scholars based in Germany, United Kingdom and United States. J. Weller's co-authors include Andreas Albrecht, Richard A. Battye, Jeremiah P. Ostriker, Antony Lewis, B. Hoyle, G. Efstathiou, S. L. Bridle, L. Shaw, Paul Bode and Nico Hamaus and has published in prestigious journals such as Physical Review Letters, The Astrophysical Journal and Biophysical Journal.

In The Last Decade

J. Weller

68 papers receiving 2.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
J. Weller Germany 32 2.3k 1.1k 465 199 118 69 2.7k
Thomas A. Prince United States 33 2.9k 1.2× 691 0.6× 246 0.5× 40 0.2× 155 1.3× 159 3.8k
Andrei Mesinger Italy 47 6.3k 2.7× 3.4k 3.0× 1.3k 2.7× 160 0.8× 77 0.7× 132 6.6k
D. Mortlock United Kingdom 30 2.6k 1.1× 731 0.6× 595 1.3× 102 0.5× 71 0.6× 80 2.9k
Fabian Schmidt Germany 39 4.4k 1.9× 2.0k 1.8× 780 1.7× 331 1.7× 210 1.8× 103 4.6k
Francisco-Shu Kitaura Germany 33 3.0k 1.3× 1.1k 0.9× 790 1.7× 309 1.6× 63 0.5× 80 3.2k
Alexandre Réfrégier Switzerland 37 3.9k 1.7× 1.1k 1.0× 1.2k 2.6× 253 1.3× 48 0.4× 129 4.2k
V. Petrosian United States 38 4.2k 1.8× 1.6k 1.4× 466 1.0× 97 0.5× 63 0.5× 161 4.6k
Will Saunders Australia 19 3.2k 1.4× 1.5k 1.3× 799 1.7× 165 0.8× 132 1.1× 71 3.4k
George D. Becker United States 40 5.3k 2.3× 2.4k 2.1× 1.2k 2.6× 205 1.0× 48 0.4× 109 5.7k
T. Kitching United Kingdom 23 1.8k 0.8× 608 0.5× 498 1.1× 112 0.6× 23 0.2× 76 2.0k

Countries citing papers authored by J. Weller

Since Specialization
Citations

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

Fields of papers citing papers by J. Weller

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of J. Weller

This figure shows the co-authorship network connecting the top 25 collaborators of J. Weller. A scholar is included among the top collaborators of J. Weller 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 J. Weller. J. Weller 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.
Küchemann, Stefan, Karina E. Avila, Steffen Steinert, et al.. (2025). On opportunities and challenges of large multimodal foundation models in education. npj Science of Learning. 10(1). 11–11. 9 indexed citations
2.
Weller, J. & Remo Rohs. (2025). BPS2025 - Automated structure-based drug design with generative deep learning. Biophysical Journal. 124(3). 323a–323a. 1 indexed citations
3.
Krippendorf, Sven, et al.. (2025). Learning optimal summary statistics of galaxy catalogs with SBI. Journal of Cosmology and Astroparticle Physics. 2025(12). 32–32.
4.
Roster, W., M. Salvato, Sven Krippendorf, et al.. (2024). PICZL: Image-based photometric redshifts for AGN. Astronomy and Astrophysics. 692. A260–A260. 1 indexed citations
5.
Kerscher, M. & J. Weller. (2024). On marginals and profiled posteriors for cosmological parameter estimation. Journal of Cosmology and Astroparticle Physics. 2024(11). 35–35. 1 indexed citations
6.
Hamaus, Nico, et al.. (2024). Why cosmic voids matter: mitigation of baryonic physics. Journal of Cosmology and Astroparticle Physics. 2024(8). 65–65. 2 indexed citations
7.
Komatsu, Eiichiro, et al.. (2023). Lattice simulations of axion-U(1) inflation. Physical review. D. 108(4). 46 indexed citations
8.
Hamaus, Nico, et al.. (2023). Why cosmic voids matter: nonlinear structure & linear dynamics. Journal of Cosmology and Astroparticle Physics. 2023(5). 31–31. 13 indexed citations
9.
Krippendorf, Sven, Esra Bülbül, M. Kara, et al.. (2023). The eROSITA Final Equatorial-Depth Survey (eFEDS): A machine learning approach to inferring galaxy cluster masses from eROSITA X-ray images. Astronomy and Astrophysics. 682. A132–A132. 4 indexed citations
10.
Ross, M. P., Jens H. Gundlach, E. G. Adelberger, et al.. (2023). Pseudoplane-wave gravitational calibrator for gravitational wave observatories. Physical review. D. 107(6). 1 indexed citations
11.
Komatsu, Eiichiro, et al.. (2022). Lattice simulations of Abelian gauge fields coupled to axions during inflation. Physical review. D. 105(12). 20 indexed citations
12.
Kiiveri, K., D. Gruen, A. Finoguenov, et al.. (2020). CODEX weak lensing mass catalogue and implications on the mass–richness relation. Monthly Notices of the Royal Astronomical Society. 502(1). 1494–1526. 6 indexed citations
13.
Finoguenov, A., E. S. Rykoff, N. Clerc, et al.. (2020). CODEX clusters. Astronomy and Astrophysics. 638. A114–A114. 33 indexed citations
14.
Landry, Guillaume, David C. Hansen, Florian Kamp, et al.. (2018). Comparing Unet training with three different datasets to correct CBCT images for prostate radiotherapy dose calculations. Physics in Medicine and Biology. 64(3). 35011–35011. 71 indexed citations
15.
Pollina, G, Nico Hamaus, Klaus Dolag, et al.. (2017). On the linearity of tracer bias around voids. Monthly Notices of the Royal Astronomical Society. 469(1). 787–799. 44 indexed citations
16.
Hamaus, Nico, Alice Pisani, Guilhem Lavaux, et al.. (2016). Constraints on Cosmology and Gravity from the Dynamics of Voids. Physical Review Letters. 117(9). 91302–91302. 110 indexed citations
17.
Soergel, Bjoern, T. Giannantonio, J. Weller, & Richard A. Battye. (2015). Constraining dark sector perturbations II: ISW and CMB lensing tomography. Journal of Cosmology and Astroparticle Physics. 2015(2). 37–37. 8 indexed citations
18.
Weller, J., et al.. (2013). Combining clustering and abundances of galaxy clusters to test cosmology and primordial non-Gaussianity. Monthly Notices of the Royal Astronomical Society. 434(1). 684–695. 42 indexed citations
19.
Mena, Olga, José Santiago, & J. Weller. (2006). Constraining Inverse-Curvature Gravity with Supernovae. Physical Review Letters. 96(4). 41103–41103. 79 indexed citations
20.
Weller, J. & Andreas Albrecht. (2001). Opportunities for Future Supernova Studies of Cosmic Acceleration. Physical Review Letters. 86(10). 1939–1942. 116 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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