P. Melchior

19.7k total citations
47 papers, 789 citations indexed

About

P. Melchior is a scholar working on Astronomy and Astrophysics, Instrumentation and Computer Vision and Pattern Recognition. According to data from OpenAlex, P. Melchior has authored 47 papers receiving a total of 789 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Astronomy and Astrophysics, 15 papers in Instrumentation and 7 papers in Computer Vision and Pattern Recognition. Recurrent topics in P. Melchior's work include Galaxies: Formation, Evolution, Phenomena (22 papers), Astronomy and Astrophysical Research (15 papers) and Stellar, planetary, and galactic studies (11 papers). P. Melchior is often cited by papers focused on Galaxies: Formation, Evolution, Phenomena (22 papers), Astronomy and Astrophysical Research (15 papers) and Stellar, planetary, and galactic studies (11 papers). P. Melchior collaborates with scholars based in United States, Germany and United Kingdom. P. Melchior's co-authors include Matthias Bartelmann, James Bosch, R. Armstrong, Rachel Mandelbaum, J. P. Dietrich, Laura E. Condon, R. M. Maxwell, E. Sheldon, Barnaby Rowe and Hironao Miyatake and has published in prestigious journals such as The Astrophysical Journal, Geophysical Research Letters and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

P. Melchior

41 papers receiving 739 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
P. Melchior United States 14 586 249 137 120 65 47 789
F. Vogt Australia 17 668 1.1× 191 0.8× 188 1.4× 29 0.2× 147 2.3× 61 981
Jiansheng Chen China 16 596 1.0× 194 0.8× 56 0.4× 54 0.5× 132 2.0× 78 850
Antonio J. Cuesta Spain 18 2.1k 3.6× 540 2.2× 42 0.3× 30 0.3× 782 12.0× 26 2.3k
R. Scalzo Australia 17 904 1.5× 101 0.4× 50 0.4× 23 0.2× 477 7.3× 61 1.2k
Jeffrey A. Mendenhall United States 12 301 0.5× 41 0.2× 36 0.3× 30 0.3× 124 1.9× 38 823
Farhan Feroz United Kingdom 13 693 1.2× 115 0.5× 31 0.2× 32 0.3× 468 7.2× 31 1.0k
Anupreeta More Japan 19 970 1.7× 393 1.6× 155 1.1× 45 0.4× 124 1.9× 57 1.1k
Myra Blaylock United States 19 2.2k 3.7× 791 3.2× 47 0.3× 22 0.2× 298 4.6× 52 2.5k
C. Muñoz–Tuñón Spain 24 1.4k 2.4× 610 2.4× 276 2.0× 50 0.4× 90 1.4× 127 1.8k
Sarah Appleby United Kingdom 9 844 1.4× 361 1.4× 22 0.2× 19 0.2× 177 2.7× 13 1.1k

Countries citing papers authored by P. Melchior

Since Specialization
Citations

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

Fields of papers citing papers by P. Melchior

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of P. Melchior

This figure shows the co-authorship network connecting the top 25 collaborators of P. Melchior. A scholar is included among the top collaborators of P. Melchior 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 P. Melchior. P. Melchior 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.
Bennett, Andrew, et al.. (2025). A Deep‐Learning Based Parameter Inversion Framework for Large‐Scale Groundwater Models. Geophysical Research Letters. 52(8). 1 indexed citations
2.
Ward, Charlotte, et al.. (2025). Disentangling transients and their host galaxies with scarlet2: A framework to forward model multi-epoch imaging. Astronomy and Computing. 51. 100930–100930. 1 indexed citations
3.
Melchior, P., et al.. (2024). Score-matching neural networks for improved multi-band source separation. Astronomy and Computing. 49. 100875–100875. 1 indexed citations
4.
Leonarduzzi, Elena, Hoang Tran, Andrew Bennett, et al.. (2024). Simulation-based inference for parameter estimation of complex watershed simulators. Hydrology and earth system sciences. 28(20). 4685–4713. 2 indexed citations
5.
Hahn, ChangHoon, Francisco Villaescusa-Navarro, P. Melchior, & Romain Teyssier. (2024). Cosmology with Galaxy Photometry Alone. The Astrophysical Journal Letters. 966(1). L18–L18. 3 indexed citations
6.
Bennett, Andrew, Hoang Tran, Yueling Ma, et al.. (2024). Spatio‐Temporal Machine Learning for Regional to Continental Scale Terrestrial Hydrology. Journal of Advances in Modeling Earth Systems. 16(6). 8 indexed citations
7.
Melchior, P., et al.. (2023). Outlier Detection in the DESI Bright Galaxy Survey. The Astrophysical Journal Letters. 956(1). L6–L6. 4 indexed citations
8.
Greene, Jenny E., Johnny P. Greco, Song Huang, et al.. (2023). Beyond Ultra-diffuse Galaxies. I. Mass–Size Outliers among the Satellites of Milky Way Analogs. The Astrophysical Journal. 955(1). 1–1. 11 indexed citations
9.
Winn, Joshua N., et al.. (2023). AESTRA: Deep Learning for Precise Radial Velocity Estimation in the Presence of Stellar Activity. The Astronomical Journal. 167(1). 23–23. 6 indexed citations
10.
Melchior, P., et al.. (2023). Autoencoding Galaxy Spectra. II. Redshift Invariance and Outlier Detection. The Astronomical Journal. 166(2). 75–75. 12 indexed citations
11.
Melchior, P., et al.. (2023). Autoencoding Galaxy Spectra. I. Architecture. The Astronomical Journal. 166(2). 74–74. 13 indexed citations
12.
Chen, Yen‐Chi, Shirley Ho, J. Blazek, et al.. (2019). Detecting galaxy–filament alignments in the Sloan Digital Sky Survey III. Monthly Notices of the Royal Astronomical Society. 485(2). 2492–2504. 29 indexed citations
13.
Chary, Ranga‐Ram, L. Armus, Andreas L. Faisst, et al.. (2019). Cosmology in the 2020s Needs Precision and Accuracy: The Case for Euclid/LSST/WFIRST Joint Survey Processing. Bulletin of the American Astronomical Society. 51(3). 44. 1 indexed citations
14.
Smith, Arfon M., Rob Pike, William O’Mullane, et al.. (2019). Astronomy should be in the clouds. Bulletin of the American Astronomical Society. 51(7). 55. 2 indexed citations
15.
Siegel, Seth R., Jack Sayers, Andisheh Mahdavi, et al.. (2018). Constraints on the Mass, Concentration, and Nonthermal Pressure Support of Six CLASH Clusters from a Joint Analysis of X-Ray, SZ, and Lensing Data. The Astrophysical Journal. 861(1). 71–71. 16 indexed citations
16.
Melchior, P., et al.. (2015). Optical broad-band photometry and reference image for APMUKS(BJ) B215839.70-615403.9 / ASASSN-15lh from the Dark Energy Survey. The astronomer's telegram. 7843. 1.
17.
Bartelmann, Matthias, Massimo Viola, P. Melchior, & B Schäfer. (2012). Calibration biases in measurements of weak\n lensing. Springer Link (Chiba Institute of Technology). 9 indexed citations
18.
Melchior, P., et al.. (2010). Limitations on shapelet-based weak-lensing measurements. Springer Link (Chiba Institute of Technology). 30 indexed citations
19.
Melchior, P., R. Andrae, M. Maturi, & Matthias Bartelmann. (2008). Deconvolution with shapelets. Springer Link (Chiba Institute of Technology). 5 indexed citations
20.
Melchior, P., et al.. (1958). A propos de la constante d'aberration. 74. 188. 1 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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