J/MNRAS/491/2025 Synthetic stellar spectra in SPS models (Coelho+, 2020)
To use or not to use synthetic stellar spectra in population synthesis models?
Coelho P.R.T., Bruzual G., Charlot S.
<Mon. Not. R. Astron. Soc., 491, 2025-2042 (2020)>
=2020MNRAS.491.2025C 2020MNRAS.491.2025C (SIMBAD/NED BibCode)
ADC_Keywords: Stars, atmospheres ; Effective temperatures ;
Abundances, [Fe/H] ; Spectra, optical
Keywords: stars: atmospheres - galaxies: stellar content
Abstract:
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.
Description:
We adopt the MILES atmospheric stellar parameters mainly from Prugniel
et al. (2011A&A...531A.165P 2011A&A...531A.165P, Cat. J/A+A/531/A165), with the revision
for cool stars provided by Sharma et al. (2016A&A...585A..64S 2016A&A...585A..64S, Cat.
J/A+A/585/A64). For 26 stars, we use the parameters from Cenarro et
al. (2007MNRAS.374..664C 2007MNRAS.374..664C, Cat. J/MNRAS/374/664), either because
Prugniel et al. (2011A&A...531A.165P 2011A&A...531A.165P, Cat. J/A+A/531/A165) do not
provide an independent determination, or because we concluded by
visual comparison that the Cenarro et al. (2007MNRAS.374..664C 2007MNRAS.374..664C, Cat.
J/MNRAS/374/664) parameters permit a closer match between observed and
model spectra. For stars HD001326B and HD199478, we modified slightly
the reported parameters, as we could not obtain converged models for
the nominal parameters. The changes in Teff and logg are
nevertheless small - the smallest needed to achieve convergence - and
are within the reported errors.
We list in Table 1 the atmospheric parameters adopted for each star in
MILES and SynCoMiL, along with their respective sources. Unlisted
MILES stars are not used in the SPS models in this work and are
reported separately in Table 2.
File Summary:
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FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
table1.dat 42 914 Atmospheric parameters used as input values in
the computation of SynCoMiL
table2.dat 41 71 MILES stars unsuitable for SPS modelling
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See also:
J/MNRAS/374/664 : Stellar atmospheric parameters in MILES library
(Cenarro+, 2007)
J/A+A/531/A165 : MILES atmospheric parameters (Prugniel+, 2011)
J/A+A/585/A64 : New atmospheric parameters of MILES cool stars
(Sharma+, 2016)
Byte-by-byte Description of file: table1.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 3 I3 --- ID [1/985] MILES identifier
5- 20 A16 --- Name Star name
22- 26 I5 K Teff Effective temperature
28- 32 F5.2 [cm/s2] logg Surface gravity
34- 38 F5.2 [-] [Fe/H] Iron to hydrogen abundance ratio
40- 42 A3 --- ref Reference (G1)
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Byte-by-byte Description of file: table2.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
1- 3 I3 --- ID [29/970] MILES identifier
5- 14 A10 --- Name Star name
16- 20 I5 K Teff Effective temperature
22- 25 F4.2 [cm/s2] logg Surface gravity
27- 31 F5.2 [-] [Fe/H] Iron to hydrogen abundance ratio
33- 35 A3 --- ref Reference (G1)
37- 41 A5 --- Notes Reason(s) why the star was discarded (1)
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Note (1): Notes as follows:
1 = Excessive noise or corrupted spectrum
2 = Visible continuum distortions
3 = Visible emission lines
4 = Peculiar features
5 = E(B-V)>0.3 in Prugniel et al. (2011A&A...531A.165P 2011A&A...531A.165P, Cat. J/A+A/531/A165)
6 = Removed by the cut in χ2
7 = Removed by the cut in Δ
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Global Notes:
Note (G1): References as follows:
a = Cenarro et al. (2007MNRAS.374..664C 2007MNRAS.374..664C, Cat. J/MNRAS/374/664)
b = Prugniel et al. (2011A&A...531A.165P 2011A&A...531A.165P, Cat. J/A+A/531/A165)
c = Sharma et al. (2016A&A...585A..64S 2016A&A...585A..64S, Cat. J/A+A/585/A64)
d = Different parameters than proposed in the literature were used to
ensure model convergence
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History:
From electronic version of the journal
(End) Ana Fiallos [CDS] 14-Feb-2023