J/A+A/605/A40 Example of FERRE code spectra (Aguado+, 2017)
WHT follow-up observations of extremely metal-poor stars identified from
SDSS and LAMOST.
Aguado D.S., Gonzalez Hernandez J.I., Allende Prieto C., Rebolo R.
<Astron. Astrophys. 605, A40 (2017)>
=2017A&A...605A..40A 2017A&A...605A..40A (SIMBAD/NED BibCode)
ADC_Keywords: Models ; Spectroscopy
Keywords: stars: abundances - stars: fundamental parameters -
stars: Population II - galaxies: stellar content - Galaxy: halo
Abstract:
We have identified several tens of extremely metal poor star
candidates from SDSS and LAMOST, which we follow-up with the 4.2m
William Herschel Telescope (WHT) telescope to confirm their
metallicity.
We followed a robust two-step methodology. We first analyzed the SDSS
and LAMOST spectra. A first set of stellar parameters was derived from
these spectra with the FERRE code, taking advantage of the continuum
shape to determine the atmospheric parameters, in particular, the
effective temperature. Second, we selected interesting targets for
follow-up observations, some of them with very low-quality SDSS or
LAMOST data. We then obtained and analyzed higher-quality
medium-resolution spectra obtained with the Intermediate dispersion
Spectrograph and Imaging System (ISIS) on the WHT to arrive at a
second more reliable set of atmospheric parameters. This allowed us to
derive the metallicity with accuracy, and we confirm the extremely
metal-poor nature in most cases. In this second step we also employed
FERRE, but we took a running mean to normalize both the observed and
the synthetic spectra, and therefore the final parameters do not rely
on having an accurate flux calibration or continuum placement. We have
analyzed with the same tools and following the same procedure six
well-known metal-poor stars, five of them at [Fe/H]←4 to verify
our results. This showed that our methodology is able to derive
accurate metallicity determinations down to [Fe/H]←5.0.
The results for these six reference stars give us confidence on the
metallicity scale for the rest of the sample. In addition, we present
12 new extremely metal-poor candidates: 2 stars at [Fe/H]~=-4, 6 more
in the range -4<[Fe/H]←3.5, and 4 more at -3.5<[Fe/H]←3.0.
We conclude that we can reliably determine metallicities for extremely
metal-poor stars with a precision of 0.2dex from medium-resolution
spectroscopy with our improved methodology. This provides a highly
effective way of verifying candidates from lower quality data. Our
model spectra and the details of the fitting algorithm are made public
to facilitate the standardization of the analysis of spectra from the
same or similar instruments.
Description:
FERRE matches physical models to observed data. It was created to deal
with the common problem of having numerical models that are costly to
evaluate, and need to be used to interpret large data sets.
ferre.pdf file contains the FERRE uses's guide.
The code can be obtained from http://hebe.as.utexas.edu/ferre
Example :
f_crump3h.dat is a tool usable with FERRE with the parameters shown in
its header:
Resolving Power:10.000
3600 ≤ λ ≤ 9000Å,
-6 ≤ [Fe/H] ≤-2,
-1 ≤ [C/Fe] ≤ 5,
4750 ≤ Tefff ≤ 7000,
1.0 ≤ logg ≤ 5.0,
It is the grid used for the paper.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
ferre.pdf 512 353 Instructions
f_crump3h.dat . 5698 Example file (28 header lines + 5670 spectra)
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Note on f_crump3h.dat: the file contains 28 header lines and
5670 spectra (one per line).
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Acknowledgements:
David Aguado, aguado(at)iac.es
(End) Patricia Vannier [CDS] 31-May-2017