M. Manteiga

30.9k total citations
47 papers, 461 citations indexed

About

M. Manteiga is a scholar working on Astronomy and Astrophysics, Instrumentation and Computational Mechanics. According to data from OpenAlex, M. Manteiga has authored 47 papers receiving a total of 461 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Astronomy and Astrophysics, 17 papers in Instrumentation and 9 papers in Computational Mechanics. Recurrent topics in M. Manteiga's work include Stellar, planetary, and galactic studies (26 papers), Astronomy and Astrophysical Research (17 papers) and Astronomical Observations and Instrumentation (9 papers). M. Manteiga is often cited by papers focused on Stellar, planetary, and galactic studies (26 papers), Astronomy and Astrophysical Research (17 papers) and Astronomical Observations and Instrumentation (9 papers). M. Manteiga collaborates with scholars based in Spain, Mexico and Italy. M. Manteiga's co-authors include Carlos Dafonte, A. Ulla, A. Manchado, B. Arcay, Diego Fustes, S. R. Pottasch, P. García-Lario, Diego H. Peluffo-Ordóńez, I. González-Santamaría and K. W. Smith and has published in prestigious journals such as Nature Communications, The Astrophysical Journal and Monthly Notices of the Royal Astronomical Society.

In The Last Decade

M. Manteiga

42 papers receiving 438 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
M. Manteiga Spain 12 322 153 67 49 36 47 461
Roman V. Baluev Russia 12 397 1.2× 133 0.9× 50 0.7× 28 0.6× 28 0.8× 32 487
S. Shahaf Israel 11 327 1.0× 161 1.1× 36 0.5× 32 0.7× 11 0.3× 31 458
Vinesh Rajpaul United Kingdom 11 496 1.5× 201 1.3× 40 0.6× 23 0.5× 16 0.4× 22 555
K. Nienartowicz Switzerland 14 432 1.3× 215 1.4× 84 1.3× 18 0.4× 27 0.8× 25 504
Dalya Baron Israel 14 448 1.4× 139 0.9× 24 0.4× 66 1.3× 20 0.6× 23 607
J. Brinkmann United States 9 707 2.2× 253 1.7× 51 0.8× 22 0.4× 27 0.8× 12 780
J. Debosscher Belgium 20 770 2.4× 385 2.5× 109 1.6× 21 0.4× 40 1.1× 37 867
J. Cuypers Belgium 14 607 1.9× 260 1.7× 115 1.7× 22 0.4× 45 1.3× 46 697
R. D. Haywood United States 11 553 1.7× 203 1.3× 42 0.6× 28 0.6× 14 0.4× 27 604
L. P. Guy Switzerland 10 235 0.7× 111 0.7× 54 0.8× 20 0.4× 26 0.7× 21 520

Countries citing papers authored by M. Manteiga

Since Specialization
Citations

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

Fields of papers citing papers by M. Manteiga

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of M. Manteiga

This figure shows the co-authorship network connecting the top 25 collaborators of M. Manteiga. A scholar is included among the top collaborators of M. Manteiga 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 M. Manteiga. M. Manteiga 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.
Manteiga, M., et al.. (2025). Finding White Dwarfs’ Hidden Companions Using an Unsupervised Machine Learning Technique. The Astrophysical Journal. 988(1). 51–51.
2.
Navarro, S. G., et al.. (2024). Analyzing Supervised Machine Learning Models for Classifying Astronomical Objects Using Gaia DR3 Spectral Features. Applied Sciences. 14(19). 9058–9058. 1 indexed citations
3.
Pallas-Quintela, L., et al.. (2024). Identifying New High-confidence Polluted White Dwarf Candidates Using Gaia XP Spectra and Self-organizing Maps. The Astrophysical Journal. 977(1). 31–31. 3 indexed citations
4.
Vázquez, C. Viscasillas, E. Solano, A. Ulla, et al.. (2024). Advanced classification of hot subdwarf binaries using artificial intelligence techniques and Gaia DR3 data. Astronomy and Astrophysics. 691. A223–A223. 1 indexed citations
5.
González-Santamaría, I., et al.. (2021). Planetary nebulae in Gaia EDR3: Central star identification, properties, and binarity. Astronomy and Astrophysics. 656. A51–A51. 30 indexed citations
6.
Masseron, T., D. A. García–Hernández, R. Santoveña, et al.. (2020). Phosphorus-rich stars with unusual abundances are challenging theoretical predictions. Nature Communications. 11(1). 3759–3759. 19 indexed citations
7.
González-Santamaría, I., et al.. (2020). Wide binaries in planetary nebulae withGaiaDR2. Astronomy and Astrophysics. 644. A173–A173. 8 indexed citations
8.
Recio–Blanco, A., P. de Laverny, C. Allende Prieto, et al.. (2015). Stellar parametrization fromGaiaRVS spectra. Astronomy and Astrophysics. 585. A93–A93. 53 indexed citations
9.
Miranda, L. F., A. Ulla, R. Vázquez, et al.. (2013). Detection of a multishell planetary nebula around the hot subdwarf O-type star 2MASS J19310888+4324577. Springer Link (Chiba Institute of Technology). 12 indexed citations
10.
Fustes, Diego, M. Manteiga, Carlos Dafonte, et al.. (2013). An approach to the analysis of SDSS spectroscopic outliers based on self-organizing maps. Astronomy and Astrophysics. 559. A7–A7. 22 indexed citations
11.
Fustes, Diego, Carlos Dafonte, M. Manteiga, et al.. (2012). SOM ensemble for unsupervised outlier analysis. Application to outlier identification in the Gaia astronomical survey. Expert Systems with Applications. 40(5). 1530–1541. 20 indexed citations
12.
Peluffo-Ordóńez, Diego H., Carlos Dafonte, B. Arcay, & M. Manteiga. (2011). HSC: A multi-resolution clustering strategy in Self-Organizing Maps applied to astronomical observations. Applied Soft Computing. 12(1). 204–215. 9 indexed citations
13.
Manteiga, M., et al.. (2009). STARMIND: A FUZZY LOGIC KNOWLEDGE-BASED SYSTEM FOR THE AUTOMATED CLASSIFICATION OF STARS IN THE MK SYSTEM. The Astronomical Journal. 137(2). 3245–3253. 23 indexed citations
14.
Manchado, A., et al.. (2006). A spectroscopic atlas of post-AGB stars and planetary nebulae selected from the IRAS point source catalogue. Springer Link (Chiba Institute of Technology). 76 indexed citations
15.
Dafonte, Carlos, et al.. (2004). Automated knowledge-based analysis and classification of stellar spectra using fuzzy reasoning. Expert Systems with Applications. 27(2). 237–244. 12 indexed citations
16.
Manteiga, M., A. Manchado, P. García-Lario, & C. Pérez de los Heros. (2004). PN G000.2+06.1 and PN G002.3+02.2: Two New Type I Planetary Nebulae in the Galactic Bulge. The Astronomical Journal. 127(6). 3437–3443. 1 indexed citations
17.
Mampaso, A., et al.. (2001). A Closer View of the Nucleus of NGC 4314. Astrophysics and Space Science. 276(2-4). 539–543.
18.
García-Lario, P., A. Manchado, A. Ulla, & M. Manteiga. (1999). Infrared Space ObservatoryObservations of IRAS 16594−4656: A New Proto–Planetary Nebula with a Strong 21 Micron Dust Feature. The Astrophysical Journal. 513(2). 941–946. 38 indexed citations
19.
Caputo, F., et al.. (1993). An atlas of theoretical constraints for horizontal branch stars. 276(1). 41–51. 1 indexed citations
20.
Caputo, F., et al.. (1991). The Galactic globular cluster system - Theoretical constraints for alpha-enhanced compositions. The Astrophysical Journal. 380. 484–484. 6 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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