Luca Tortorelli

475 total citations
18 papers, 120 citations indexed

About

Luca Tortorelli is a scholar working on Astronomy and Astrophysics, Instrumentation and Ecology. According to data from OpenAlex, Luca Tortorelli has authored 18 papers receiving a total of 120 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Astronomy and Astrophysics, 11 papers in Instrumentation and 4 papers in Ecology. Recurrent topics in Luca Tortorelli's work include Galaxies: Formation, Evolution, Phenomena (17 papers), Astronomy and Astrophysical Research (11 papers) and Stellar, planetary, and galactic studies (4 papers). Luca Tortorelli is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (17 papers), Astronomy and Astrophysical Research (11 papers) and Stellar, planetary, and galactic studies (4 papers). Luca Tortorelli collaborates with scholars based in Germany, Switzerland and Italy. Luca Tortorelli's co-authors include Alexandre Réfrégier, A. Mercurio, Tomasz Kacprzak, A. Amara, G. B. Caminha, P. Rosati, C. Grillo, P. Bergamini, M. Nonino and D. Gruen and has published in prestigious journals such as Monthly Notices of the Royal Astronomical Society, Astronomy and Astrophysics and Journal of Cosmology and Astroparticle Physics.

In The Last Decade

Luca Tortorelli

17 papers receiving 108 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Luca Tortorelli Germany 7 100 66 15 10 10 18 120
C. Sánchez Spain 7 123 1.2× 51 0.8× 11 0.7× 12 1.2× 12 1.2× 12 147
Alex I. Malz United States 9 124 1.2× 50 0.8× 9 0.6× 8 0.8× 6 0.6× 23 161
S. Jouvel United States 7 144 1.4× 83 1.3× 16 1.1× 10 1.0× 12 1.2× 15 153
A. Boucaud France 4 79 0.8× 33 0.5× 13 0.9× 17 1.7× 5 0.5× 10 118
R. Joseph United States 7 107 1.1× 43 0.7× 24 1.6× 12 1.2× 5 0.5× 8 128
A. Galan Switzerland 6 146 1.5× 76 1.2× 35 2.3× 9 0.9× 6 0.6× 17 173
Lorenzo Zanisi United Kingdom 9 177 1.8× 104 1.6× 7 0.5× 16 1.6× 13 1.3× 17 228
Paul Thorman United States 6 135 1.4× 51 0.8× 9 0.6× 10 1.0× 8 0.8× 11 168
L. Delchambre Belgium 6 107 1.1× 64 1.0× 6 0.4× 8 0.8× 5 0.5× 13 155
Boliang He China 6 112 1.1× 63 1.0× 18 1.2× 11 1.1× 16 1.6× 10 150

Countries citing papers authored by Luca Tortorelli

Since Specialization
Citations

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

Fields of papers citing papers by Luca Tortorelli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luca Tortorelli

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

All Works

18 of 18 papers shown
1.
Kacprzak, Tomasz, et al.. (2025). galsbi: A Python package for the GalSBI galaxy population model. The Journal of Open Source Software. 10(114). 8766–8766. 1 indexed citations
2.
Kacprzak, Tomasz, Luca Tortorelli, Claudio Bruderer, et al.. (2025). UFig v1: The ultra-fast image generator. The Journal of Open Source Software. 10(113). 8697–8697. 2 indexed citations
3.
Réfrégier, Alexandre, et al.. (2024). Fast forward modelling of galaxy spatial and statistical distributions. Journal of Cosmology and Astroparticle Physics. 2024(4). 23–23. 4 indexed citations
4.
Kacprzak, Tomasz, et al.. (2024). Simulation-based inference of deep fields: galaxy population model and redshift distributions. Journal of Cosmology and Astroparticle Physics. 2024(5). 49–49. 11 indexed citations
5.
Tortorelli, Luca, J. McCullough, & D. Gruen. (2024). Impact of stellar population synthesis choices on forward modelling-based redshift distribution estimates. Astronomy and Astrophysics. 689. A144–A144. 7 indexed citations
6.
Csizi, B., Luca Tortorelli, M. Siudek, et al.. (2024). The PAU Survey: Galaxy stellar population properties estimates with narrowband data. Astronomy and Astrophysics. 689. A37–A37. 2 indexed citations
7.
Tortorelli, Luca & A. Mercurio. (2023). MORPHOFIT: An automated galaxy structural parameters fitting package. Frontiers in Astronomy and Space Sciences. 10. 6 indexed citations
8.
Tortorelli, Luca, A. Mercurio, G. Granata, et al.. (2023). The Kormendy relation of early-type galaxies as a function of wavelength in Abell S1063, MACS J0416.1-2403, and MACS J1149.5+2223. Astronomy and Astrophysics. 671. L9–L9. 4 indexed citations
9.
Granata, G., P. Bergamini, C. Grillo, et al.. (2023). Exploring the low-mass regime of galaxy-scale strong lensing: Insights into the mass structure of cluster galaxies. Astronomy and Astrophysics. 679. A124–A124. 5 indexed citations
10.
Angora, G., P. Rosati, M. Meneghetti, et al.. (2023). Searching for strong galaxy-scale lenses in galaxy clusters with deep networks. Astronomy and Astrophysics. 676. A40–A40. 4 indexed citations
11.
Angora, G., P. Rosati, M. Meneghetti, et al.. (2022). Simulating high-realistic galaxy scale strong lensing in galaxy clusters to train deep learning methods. Proceedings of the International Astronomical Union. 18(S381). 85–93.
12.
Granata, G., A. Mercurio, C. Grillo, et al.. (2022). Improved strong lensing modelling of galaxy clusters using the Fundamental Plane: Detailed mapping of the baryonic and dark matter mass distribution of Abell S1063. Astronomy and Astrophysics. 659. A24–A24. 13 indexed citations
13.
Réfrégier, Alexandre, et al.. (2022). Rapid simulations of halo and subhalo clustering. Journal of Cosmology and Astroparticle Physics. 2022(11). 2–2. 6 indexed citations
14.
Tortorelli, Luca, et al.. (2020). Measurement of the B-band galaxy Luminosity Function with Approximate Bayesian Computation. Journal of Cosmology and Astroparticle Physics. 2020(9). 48–48. 17 indexed citations
15.
Cabayol-Garcia, L., Martin Eriksen, A. Alarcon, et al.. (2019). The PAU Survey: background light estimation with deep learning techniques. Monthly Notices of the Royal Astronomical Society. 491(4). 5392–5405. 3 indexed citations
16.
Norberg, P., C. M. Baugh, A. Alarcon, et al.. (2018). The PAU Survey: spectral features and galaxy clustering using simulated narrow-band photometry. Monthly Notices of the Royal Astronomical Society. 481(3). 4221–4235. 12 indexed citations
17.
Tortorelli, Luca, A. Mercurio, M. Paolillo, et al.. (2018). The Kormendy relation of galaxies in the Frontier Fields clusters: Abell S1063 and MACS J1149.5+2223. Monthly Notices of the Royal Astronomical Society. 477(1). 648–668. 15 indexed citations
18.
Nicola, Andrina, et al.. (2018). Forward modeling of spectroscopic galaxy surveys: application to SDSS. Journal of Cosmology and Astroparticle Physics. 2018(11). 15–15. 8 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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