期刊
MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY
卷 491, 期 2, 页码 2025-2042出版社
OXFORD UNIV PRESS
DOI: 10.1093/mnras/stz3023
关键词
stars: atmospheres; galaxies: stellar content
资金
- Conselho Nacional de Desenvolvimento Cientifico e Tecnologico [CNPq 310041/2018-0]
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo [FAPESP 2017/02375-2, 2018/05392-8]
- USP-COFECUB [2018.1.241.1.8-40449YB]
- National Autonomous University of Mexico (UNAM) [DGAPA/PAPIIT IG100319]
- CONACyT [CB2015-252364]
- Alfred P. Sloan Foundation
- National Science Foundation
- U.S. Department of Energy
- National Aeronautics and Space Administration
- Japanese Monbukagakusho
- Max Planck Society
- Higher Education Funding Council for England
- American Museum of Natural History
- Astrophysical Institute Potsdam
- University of Basel
- University of Cambridge
- Case-Western Reserve University
- University of Chicago
- Drexel University
- Fermilab
- Institute for Advanced Study
- Japan Participation Group
- Johns Hopkins University
- Joint Institute for Nuclear Astrophysics
- Kavli Institute for Particle Astrophysics and Cosmology
- Korean Scientist Group
- Chinese Academy of Sciences (LAMOST)
- Los Alamos National Laboratory
- Max-Planck-Institute for Astronomy (MPIA)
- Max-Planck-Institute for Astrophysics (MPA)
- New Mexico State University
- Ohio State University
- University of Pittsburgh
- University of Portsmouth
- Princeton University
- United States Naval Observatory
- University of Washington
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [17/02375-2] Funding Source: FAPESP
Stellar population synthesis (SPS) models are invaluable to study star clusters and galaxies. They provide means to extract stellar masses, stellar ages, star formation histories, chemical enrichment, and dust content of galaxies from their integrated spectral energy distributions, colours, or spectra. As most models, they contain uncertainties that can hamper our ability to model and interpret observed spectra. This work aims at studying a specific source of model uncertainty: the choice of an empirical versus a synthetic stellar spectral library. Empirical libraries suffer from limited coverage of parameter space, while synthetic libraries suffer from modelling inaccuracies. Given our current inability to have both ideal stellar-parameter coverage with ideal stellar spectra, what should one favour: better coverage of the parameters (synthetic library) or better spectra on a star-by-star basis (empirical library)? To study this question, we build a synthetic stellar library mimicking the coverage of an empirical library, and SPS models with different choices of stellar library tailored to these investigations. Through the comparison of model predictions and the spectral fitting of a sample of nearby galaxies, we learned that predicted colours are more affected by the coverage effect than the choice of a synthetic versus empirical library; the effects on predicted spectral indices are multiple and defy simple conclusions; derived galaxy ages are virtually unaffected by the choice of the library, but are underestimated when SPS models with limited parameter coverage are used; metallicities are robust against limited HRD coverage, but are underestimated when using synthetic libraries.
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