ATHOS

• Verfasst von: Hanke, Michael [VerfasserIn]
• Verfasst von: Hansen, Camilla Juul [VerfasserIn]
• Verfasst von: Koch-Hansen, Andreas [VerfasserIn]
• Verfasst von: Grebel, Eva K. [VerfasserIn]
• Titel: ATHOS
• Titelzusatz: on-the-fly stellar parameter determination of FGK stars based on flux ratios from optical spectra
• Verf.angabe: Michael Hanke, Camilla Juul Hansen, Andreas Koch, and Eva K. Grebel
• E-Jahr: 2018
• Jahr: 16 November 2018
• Umfang: 27 S.
• Fussnoten: Gesehen am 11.12.2019
• Titel Quelle: Enthalten in: Astronomy and astrophysics
• Ort Quelle: Les Ulis : EDP Sciences, 1969
• Jahr Quelle: 2018
• Band/Heft Quelle: 619(2018) Artikel-Nummer A134, 27 Seiten
• ISSN Quelle: 1432-0746
• DOI: doi:10.1051/0004-6361/201833351
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1587599724

Abstract

The rapidly increasing number of stellar spectra obtained by existing and future large-scale spectroscopic surveys feeds a demand for fast and efficient tools for the spectroscopic determination of fundamental stellar parameters. Such tools should not only comprise customized solutions for one particular survey or instrument, but, in order to enable cross-survey comparability, they should also be capable of dealing with spectra from a variety of spectrographs, resolutions, and wavelength coverages. To meet these ambitious specifications, we developed ATHOS (A Tool for HOmogenizing Stellar parameters), a fundamentally new analysis tool that adopts easy-to-use, computationally inexpensive analytical relations tying flux ratios (FRs) of designated wavelength regions in optical spectra to the stellar parameters effective temperature (<i>T<i/> <sub>eff<sub/>), iron abundance ([Fe/H]), and surface gravity (log <i>g<i/>). Our <i>T<i/> <sub>eff<sub/> estimator is based on FRs from nine pairs of wavelength ranges around the Balmer lines H<i>β<i/> and H<i>α<i/>, while for [Fe/H] and log <i>g<i/> we provide 31 and 11 FRs, respectively, which are spread between ∼4800 Å and ∼6500 Å; a region covered by most optical surveys. The analytical relations employing these FRs were trained on <i>N<i/> = 124 real spectra of a stellar benchmark sample that covers a large parameter space of <i>T<i/> <sub>eff<sub/> ≈ 4000-6500 K (spectral types F to K), [Fe/H] ≈ −4.5 to 0.3 dex, and log <i>g<i/> ≈ 1-5 dex, which at the same time reflects ATHOS’ range of applicability. We find accuracies of 97 K for <i>T<i/> <sub>eff<sub/>, 0.16 dex for [Fe/H], and 0.26 dex for log <i>g<i/>, which are merely bounded by finite uncertainties in the training sample parameters. ATHOS’ internal precisions can be better by up to 70%. We tested ATHOS on six independent large surveys spanning a wide range of resolutions (<i>R<i/> = <i>λ<i/>/Δ <i>λ<i/> ≈ 2000-52 000), amongst which are the <i>Gaia<i/>-ESO and the SDSS/SEGUE surveys. The exceptionally low execution time (< 30 ms per spectrum per CPU core) together with a comparison to the literature parameters showed that ATHOS can successfully achieve its main objectives, in other words fast stellar parametrization with cross-survey validity, high accuracy, and high precision. These are key to homogenize the output from future surveys, such as 4MOST or WEAVE.