Pablo Lemos

23.6k total citations
33 papers, 853 citations indexed

About

Pablo Lemos is a scholar working on Astronomy and Astrophysics, Nuclear and High Energy Physics and Artificial Intelligence. According to data from OpenAlex, Pablo Lemos has authored 33 papers receiving a total of 853 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Astronomy and Astrophysics, 6 papers in Nuclear and High Energy Physics and 5 papers in Artificial Intelligence. Recurrent topics in Pablo Lemos's work include Galaxies: Formation, Evolution, Phenomena (25 papers), Cosmology and Gravitation Theories (16 papers) and Gamma-ray bursts and supernovae (5 papers). Pablo Lemos is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (25 papers), Cosmology and Gravitation Theories (16 papers) and Gamma-ray bursts and supernovae (5 papers). Pablo Lemos collaborates with scholars based in United Kingdom, United States and Canada. Pablo Lemos's co-authors include Will Handley, G. Efstathiou, Andrei Cuceu, Andreu Font-Ribera, James R. Farr, Shirley Ho, Steven Gratton, Elizabeth Lee, Michael Eickenberg and Elena Massara and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and The Astrophysical Journal.

In The Last Decade

Pablo Lemos

32 papers receiving 801 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pablo Lemos United Kingdom 19 724 308 105 84 77 33 853
Elena Massara Canada 18 624 0.9× 239 0.8× 154 1.5× 84 1.0× 60 0.8× 27 746
Stephen M. Feeney United Kingdom 15 737 1.0× 289 0.9× 59 0.6× 64 0.8× 100 1.3× 30 865
S. Desai India 16 772 1.1× 377 1.2× 116 1.1× 82 1.0× 28 0.4× 93 901
Martin Reinecke Germany 12 905 1.3× 336 1.1× 177 1.7× 58 0.7× 51 0.7× 30 1.1k
Azadeh Moradinezhad Dizgah Switzerland 16 523 0.7× 276 0.9× 81 0.8× 56 0.7× 33 0.4× 34 583
Yin Li United States 16 808 1.1× 221 0.7× 247 2.4× 69 0.8× 83 1.1× 35 917
M. P. Hobson United Kingdom 16 706 1.0× 267 0.9× 72 0.7× 46 0.5× 89 1.2× 37 874
Zachary Slepian United States 16 720 1.0× 205 0.7× 203 1.9× 127 1.5× 27 0.4× 38 828
Saleem Zaroubi Netherlands 21 1.2k 1.6× 598 1.9× 138 1.3× 53 0.6× 40 0.5× 38 1.2k
Sergio Rodríguez-Torres Spain 19 1.1k 1.6× 336 1.1× 421 4.0× 91 1.1× 46 0.6× 29 1.2k

Countries citing papers authored by Pablo Lemos

Since Specialization
Citations

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

Fields of papers citing papers by Pablo Lemos

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pablo Lemos

This figure shows the co-authorship network connecting the top 25 collaborators of Pablo Lemos. A scholar is included among the top collaborators of Pablo Lemos 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 Pablo Lemos. Pablo Lemos 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.
Koziarski, Michał, Álex Hernández-García, Chenghao Liu, et al.. (2024). Towards equilibrium molecular conformation generation with GFlowNets. Digital Discovery. 3(5). 1038–1047.
2.
Hahn, ChangHoon, Pablo Lemos, Liam Parker, et al.. (2024). Cosmological constraints from non-Gaussian and nonlinear galaxy clustering using the SimBIG inference framework. Nature Astronomy. 8(11). 1457–1467. 11 indexed citations
3.
Blancard, Bruno Régaldo-Saint, ChangHoon Hahn, Shirley Ho, et al.. (2024). Galaxy clustering analysis with SimBIG and the wavelet scattering transform. Physical review. D. 109(8). 21 indexed citations
4.
Hahn, ChangHoon, Michael Eickenberg, Shirley Ho, et al.. (2024). Cosmological constraints from the nonlinear galaxy bispectrum. Physical review. D. 109(8). 18 indexed citations
5.
Lemos, Pablo, Liam Parker, ChangHoon Hahn, et al.. (2024). Field-level simulation-based inference of galaxy clustering with convolutional neural networks. Physical review. D. 109(8). 26 indexed citations
6.
Bevins, H. T. J., Will Handley, Pablo Lemos, et al.. (2023). Marginal post-processing of Bayesian inference products with normalizing flows and kernel density estimators. Monthly Notices of the Royal Astronomical Society. 526(3). 4613–4626. 9 indexed citations
7.
Lemos, Pablo, Miles Cranmer, ChangHoon Hahn, et al.. (2023). Robust simulation-based inference in cosmology with Bayesian neural networks. Machine Learning Science and Technology. 4(1). 01LT01–01LT01. 21 indexed citations
8.
Lemos, Pablo, N Jeffrey, Miles Cranmer, Shirley Ho, & Peter Battaglia. (2023). Rediscovering orbital mechanics with machine learning. Machine Learning Science and Technology. 4(4). 45002–45002. 41 indexed citations
9.
Massara, Elena, Francisco Villaescusa-Navarro, ChangHoon Hahn, et al.. (2023). Cosmological Information in the Marked Power Spectrum of the Galaxy Field. The Astrophysical Journal. 951(1). 70–70. 26 indexed citations
10.
Lemos, Pablo, et al.. (2023). Posterior sampling of the initial conditions of the universe from non-linear large scale structures using score-based generative models. Monthly Notices of the Royal Astronomical Society Letters. 527(1). L173–L178. 14 indexed citations
11.
Hahn, ChangHoon, Michael Eickenberg, Shirley Ho, et al.. (2023). SimBIG: mock challenge for a forward modeling approach to galaxy clustering. Journal of Cosmology and Astroparticle Physics. 2023(4). 10–10. 26 indexed citations
12.
Lemos, Pablo & Antony Lewis. (2023). CMB constraints on the early Universe independent of late-time cosmology. Physical review. D. 107(10). 16 indexed citations
13.
Charnock, Tom, et al.. (2022). The Cosmic Graph: Optimal Information Extraction from Large-Scale Structure using Catalogues. SHILAP Revista de lepidopterología. 5(1). 28 indexed citations
14.
Villaescusa-Navarro, Francisco, Shy Genel, Stephanie Tonnesen, et al.. (2022). Cosmology with One Galaxy?. The Astrophysical Journal. 929(2). 132–132. 29 indexed citations
15.
Joachimi, B, F. Köhlinger, Will Handley, & Pablo Lemos. (2021). When tension is just a fluctuation. Astronomy and Astrophysics. 647. L5–L5. 13 indexed citations
16.
Lahav, O., et al.. (2020). The impact of peculiar velocities on the estimation of the Hubble constant from gravitational wave standard sirens. Monthly Notices of the Royal Astronomical Society. 495(1). 90–97. 35 indexed citations
17.
Lemos, Pablo, F. Köhlinger, Will Handley, et al.. (2020). Quantifying Suspiciousness within correlated data sets. Monthly Notices of the Royal Astronomical Society. 496(4). 4647–4653. 18 indexed citations
18.
Handley, Will & Pablo Lemos. (2019). Quantifying tension: interpreting the DES evidence ratio. arXiv (Cornell University). 1 indexed citations
19.
Handley, Will & Pablo Lemos. (2019). Quantifying dimensionality: Bayesian cosmological model complexities. Physical review. D. 100(2). 43 indexed citations
20.
Efstathiou, G. & Pablo Lemos. (2018). Statistical inconsistencies in the KiDS-450 data set. Monthly Notices of the Royal Astronomical Society. 476(1). 151–157. 35 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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