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J/ApJ/562/528       Teff and log(g) of low-metallicity stars     (Snider+, 2001)

Three-dimensional spectral classification of low-metallicity stars using artificial neural networks. Snider S., Allende Prieto C., von Hippel T., Beers T.C., Sneden C., Qu Y., Rossi S. <Astrophys. J. 562, 528 (2001)> =2001ApJ...562..528S
ADC_Keywords: Stars, population II ; Spectroscopy ; Effective temperatures Keywords: Galaxy: halo - methods: data analysis - nuclear reactions, nucleosynthesis, abundances - stars: abundances - stars: Population II Abstract: We explore the application of artificial neural networks (ANNs) for the estimation of atmospheric parameters (Teff, log(g), and [Fe/H]) for Galactic F- and G-type stars. The ANNs are fed with medium-resolution (Δλ∼1-2Å) nonflux-calibrated spectroscopic observations. From a sample of 279 stars with previous high-resolution determinations of metallicity and a set of (external) estimates of temperature and surface gravity, our ANNs are able to predict Teff with an accuracy of σ(Teff)=135-150K over the range 4250K≤Teff≤6500K, logg with an accuracy of σ(logg)=0.25-0.30dex over the range 1.0≤logg≤5.0, and [Fe/H] with an accuracy σ([Fe/H])=0.15-0.20dex over the range -4.0≤[Fe/H]≤0.3. Such accuracies are competitive with the results obtained by fine analysis of high-resolution spectra. It is noteworthy that the ANNs are able to obtain these results without consideration of photometric information for these stars. We have also explored the impact of the signal-to-noise ratio (S/N) on the behavior of ANNs and conclude that, when analyzed with ANNs trained on spectra of commensurate S/N, it is possible to extract physical parameter estimates of similar accuracy with stellar spectra having S/N as low as 13. Taken together, these results indicate that the ANN approach should be of primary importance for use in present and future large-scale spectroscopic surveys. The stars that comprise our study are a subset of the calibration stars used in the Beers et al. (1999, Cat. J/AJ/117/981) medium-resolution surveys. File Summary:
FileName Lrecl Records Explanations
ReadMe 80 . This file table2.dat 55 209 Catalog and ANN parameters for the training sample table3.dat 55 70 Catalog and ANN parameters for the testing sample
See also: J/AJ/117/981 : Estimation of stellar metal abundance. II. (Beers+, 1999) Byte-by-byte Description of file: table2.dat table3.dat
Bytes Format Units Label Explanations
1- 12 A12 --- Name Metal-poor star name 14 A1 --- Source Spectrum source (1) 16 A1 --- f_Source [*] Indicates star is a member of the `nearby' subsample 18- 21 I4 K CTeff The catalog effective temperature 22 A1 --- f_CTeff [:] Indicates a large discrepancy with ATeff 24- 27 I4 K ATeff The artificial neural network effective temperature 28 A1 --- f_ATeff [:] Indicates a large discrepancy with CTeff 30- 33 F4.2 [cm/s2] Clog(g) Log of the catalog surface gravity 34 A1 --- f_Clog(g) [:] Indicates a large discrepancy with Alog(g) 36- 39 F4.2 [cm/s2] Alog(g) Log of the artificial neural network surface gravity 40 A1 --- f_Alog(g) [:] Indicates a large discrepancy with Clog(g) 42- 46 F5.2 --- CFe/H Catalog [Fe/H] (2) 47 A1 --- f_CFe/H [:] Indicates a large discrepancy with AFe/H 49- 53 F5.2 --- AFe/H Artificial neural network [Fe/H] (2) 54 A1 --- f_AFe/H [:] Indicates a large discrepancy with CFe/H
Note (1): Table 1: The spectroscopic data sets ------------------------------------------------------------------------------ Telescope Detector Coverage Disersion Number (Å) (Å/px) ------------------------------------------------------------------------------ E: ESO 1.5 m Ford + Loral 2048x2048 3750-4750 0.65+0.50 52 K: KPNO 2.1 m Tek 2048x2048 3750-5000 0.65 115 L: LCO 2.5 m Reticon + 2D-Frutti 3700-4500 0.65 50 O: ORM INT 2.5 m Tek 1024x1024 3750-4700 0.85 3 P: PAL 5 m Reticon + 2D-Frutti 3700-4500 0.65 3 S: SSO 2.3 m SITe 1752x532 3750-4600 0.50 58 ------------------------------------------------------------------------------ Note : ESO: European Southern Observatory (Chile) KPNO: Kitt Peak National Observatory (USA) LCO: Las Campanas Observatory (Chile) ORM: Observatorio del Roque de los Muchachos (Spain) PAL: Palomar Observatory (USA) SSO: Siding Spring Observatory (Australia) ------------------------------------------------------------------------------ Note (2): Where [Fe/H] = log(N(Fe)/N(H))star - log(N(Fe)/N(H))sun
History: From electronic version of the journal
(End) Greg Schwarz [AAS], Patricia Bauer [CDS] 22-Jan-2002
The document above follows the rules of the Standard Description for Astronomical Catalogues.From this documentation it is possible to generate f77 program to load files into arrays or line by line

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