J/A+A/556/A121 Identification of metal-poor stars with ANN (Giridhar+, 2013)
Identification of metal-poor stars using the artificial neural network.
Giridhar S., Goswami A., Kunder A., Muneer S., Selvakumar G.
<Astron. Astrophys., 556, A121 (2013)>
=2013A&A...556A.121G 2013A&A...556A.121G
ADC_Keywords: Stars, G-type ; Abundances, [Fe/H] ; Effective temperatures
Keywords: stars: solar-type - stars: fundamental parameters
Abstract:
Identification of metal-poor stars among field stars is extremely
useful for studying the structure and evolution of the Galaxy and of
external galaxies.
We search for metal-poor stars using the artificial neural network
(ANN) and extend its usage to determine absolute magnitudes.
We have constructed a library of 167 medium-resolution stellar spectra
(R∼1200) covering the stellar temperature range of 4200 to 8000K, logg
range of 0.5 to 5.0, and [Fe/H] range of -3.0 to dex. This empirical
spectral library was used to train ANNs, yielding an accuracy of
0.3dex in [Fe/H], 200K in temperature, and 0.3dex in logg. We found
that the independent calibrations of near-solar metallicity stars and
metal-poor stars decreases the errors in Teff and logg by nearly a
factor of two.
Description:
The spectra were obtained using a medium-resolution Cassegrain
spectrograph mounted on the 2.3m Vainu Bappu Telescope at VBO,
Kavalur, India. During the extended period of several years, over 200
medium-resolution spectra were obtained. The spectral coverage is
3800-6000Å.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table1.dat 86 167 List of observed stars and their parameters
table2.dat 52 69 Estimated atmospheric parameters for candidate
metal-poor stars
refs.dat 66 19 References
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Byte-by-byte Description of file: table1.dat
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Bytes Format Units Label Explanations
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1- 3 I3 --- Seq Sequential number
5- 14 A10 --- Name Star name
15 A1 --- n_Name [*] indicates a star with known metallicity
17- 21 I5 --- HIP ? Hipparcos identification number (I/311)
23- 28 F6.3 mag Vmag ? V magnitude
30- 34 F5.3 mag B-V ? B-V colour index
36- 39 F4.2 [cm/s2] loggl Literature surface gravity
41- 46 F6.1 K Teffl Literature effective temperature
48- 52 F5.2 [Sun] [Fe/H]l ? Literature metallicity
54- 58 F5.2 mag VMAGl ? Literature absolute V magnitude
60- 64 F5.3 [cm/s2] logg Artificial neural network surface gravity
66- 71 F6.1 K Teff Artificial neural network effective temperature
73- 78 F6.3 [Sun] [Fe/H] ? Artificial neural network metallicity
80- 83 F4.1 mag VMAG ? Artificial neural network absolute V magnitude
85- 86 I2 --- r_[Fe/H]l ? Reference for [Fe/H]l, in refs.dat file
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Byte-by-byte Description of file: table2.dat
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Bytes Format Units Label Explanations
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1- 13 A13 --- Name Star name
14 A1 --- m_Name [abc] Spectrum number when more than one for
the star (1)
17- 19 F3.1 [cm/s2] logg Artificial neural network surface gravity
21- 25 F5.2 [Sun] [Fe/H] Artificial neural network metallicity
27- 32 F6.1 K Teff Artificial neural network effective temperature
34- 39 F6.3 mag B-V ?=- B-V colour index
41- 46 F6.1 K DTeff ?=- Effective temperature difference,
Teff(ANN)-Teff(B-V)
48- 51 F4.1 mag VMAG Absolute V magnitude
52 A1 --- u_VMAG [*] Uncertainty flag on VMAG (2)
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Note (1): For a few objects more than one spectrum was available as indicated
by symbols a, b, and c, the difference in estimated values is indicative of
the internal error.
Note (2): *: The VMAG for hot metal-poor stars is uncertain because we did not
have good calibrators covering that temperature and metallicity range.
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Byte-by-byte Description of file: refs.dat
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Bytes Format Units Label Explanations
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1- 2 I2 --- Ref Reference number
4- 22 A19 --- BibCode BibCode
24- 47 A24 --- Aut Author's name
49- 66 A18 --- Com Comments
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History:
From electronic version of the journal
(End) Patricia Vannier [CDS] 25-Nov-2013