Gaia benchmark stars and their twins in the Gaia‐ESO Survey

Jofré, P.

doi:10.1002/asna.201612386

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Provided as a spectral library (Blanco-Cuaresma et al 2014b, hereafter Paper II) with which different instruments and resolutions can be simulated, key studies have extended the GBS known characteristics from stellar parameters (T eff and log g, Paper I) to metallicity , hereafter Paper III), α and Fe-peak element abundances (Jofré et al 2015b, hereafter Paper IV) and extensions of the sample towards low metallicities (Hawkins et al 2016a, Paper V). See also Jofré (2016) for summary.…”

Section: Introductionmentioning

confidence: 99%

GaiaFGK benchmark stars: opening the black box of stellar element abundance determination

Jofré

Heiter

Worley

et al. 2017

A&A

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Gaia and its complementary spectroscopic surveys combined will yield the most comprehensive database of kinematic and chemical information of stars in the Milky Way. The Gaia FGK benchmark stars play a central role in this matter as they are calibration pillars for the atmospheric parameters and chemical abundances for various surveys. The spectroscopic analyses of the benchmark stars are done by combining different methods, and the results will be affected by the systematic uncertainties inherent in each method. In this paper we explore some of these systematic uncertainties. We determined line abundances of Ca, Cr, Mn and Co for four benchmark stars using six different methods. We changed the default input parameters of the different codes in a systematic way and found in some cases significant differences between the results. Since there is no consensus on the correct values for many of these default parameters, we urge the community to raise discussions towards standard input parameters that could alleviate the difference in abundances obtained by different methods. In this work we provide quantitative estimates of uncertainties in elemental abundances due to the effect of differing technical assumptions in spectrum modelling.

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Section: Introductionmentioning

confidence: 99%

GaiaFGK benchmark stars: opening the black box of stellar element abundance determination

Jofré

Heiter

Worley

et al. 2017

A&A

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“…This includes only four main-sequence stars much cooler than the Sun, due to the paucity of such stars with asteroseismology. This has been accompanied by the release of high-resolution spectra (Blanco-Cuaresma et al 2014) and formed the basis of extensions to lower metallicities (Hawkins et al 2016c), stellar twin studies (Jofré 2016) and comparisons of stellar abundance determination pipelines (Jofré et al 2017). Furthermore, by combining asteroseismology with optical interferometry, it has been possible to determine fundamental parameters of main-sequence and giant stars with unprecedented precision (Huber et al 2012;White et al 2013White et al , 2015.…”

Section: Introductionmentioning

confidence: 99%

The Kepler Smear Campaign: Light Curves for 102 Very Bright Stars

Pope¹,

Davies²,

Hawkins³

et al. 2019

ApJS

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StelNet: Hierarchical Neural Network for Automatic Inference in Stellar Characterization

Garraffo

Protopapas

Drake

et al. 2021

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Characterizing the fundamental parameters of stars from observations is crucial for studying the stars themselves, their planets, and the galaxy as a whole. Stellar evolution theory predicting the properties of stars as a function of stellar age and mass enables translating observables into physical stellar parameters by fitting the observed data to synthetic isochrones. However, the complexity of overlapping evolutionary tracks often makes this task numerically challenging, and with a precision that can be highly variable, depending on the area of the parameter space the observation lies in. This work presents StelNet, a Deep Neural Network trained on stellar evolutionary tracks that quickly and accurately predicts mass and age from absolute luminosity and effective temperature for stars with close-to-solar metallicity. The underlying model makes no assumption on the evolutionary stage and includes the pre-main-sequence phase. We use bootstrapping and train many models to quantify the uncertainty of the model. To break the model’s intrinsic degeneracy resulting from overlapping evolutionary paths, we also built a hierarchical model that retrieves realistic posterior probability distributions of the stellar mass and age. We further test and train StelNet using a sample of stars with well-determined masses and ages from the literature.

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Gaia benchmark stars and their twins in the Gaia‐ESO Survey

Cited by 3 publications

References 18 publications

GaiaFGK benchmark stars: opening the black box of stellar element abundance determination

GaiaFGK benchmark stars: opening the black box of stellar element abundance determination

The Kepler Smear Campaign: Light Curves for 102 Very Bright Stars

StelNet: Hierarchical Neural Network for Automatic Inference in Stellar Characterization

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