Determination of stellar parameters for Ariel targets: a comparison analysis between different spectroscopic methods

Brucalassi, A.; Tsantaki, M.; Magrini, L.; Sousa, S.; Danielski, C.; Biazzo, K.; Casali, G.; Van der Swaelmen, M.; Rainer, M.; Adibekyan, V.; Delgado-Mena, E.; Sanna, N.

doi:10.48550/arxiv.2101.02242

Cited by 7 publications

(11 citation statements)

References 44 publications

(50 reference statements)

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“…7) with our T eff and log g values alongside the PARSEC isochrones (Bressan et al 2012). The isochrones we represent in the Kiel diagram encompass our points and they are well-behaved even for the coolest stars in the main-sequence, contrary to what has been found in previous works (see Mortier et al 2014;Montes et al 2018;Tsantaki et al 2019;Brucalassi et al 2021). Finally, we compared our V broad values to those of other late-type stars.…”

Section: Stellar Atmospheric Parameterssupporting

confidence: 61%

“…The surface gravities derived with spectroscopic data are sometimes not entirely in agreement with those calculated using evolutionary models, visual magnitudes, and parallaxes (Tsantaki et al 2019;Brucalassi et al 2021). The use of evolutionary models provides us with an alternative method that can be used to assess the precision of the spectroscopic log g values.…”

Section: Trigonometric Gravities Radii and Massesmentioning

confidence: 84%

“…The determination of stellar parameters is also of great interest to exoplanetary science because the masses and radii of the host stars are key to characterising the planets orbiting around them (see, e.g. Torres et al 2012;Santos et al 2013;Brewer et al 2016;Sousa et al 2018;Schweitzer et al 2019;Brucalassi et al 2021). Moreover, they are critical to the high-resolution transmission spectroscopy since they are employed to model the centre-to-limb variation, the limb-darkening, and the Rossiter-McLaughlin effect (see, e.g.…”

Section: Introductionmentioning

confidence: 99%

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SteParSyn: A Bayesian code to infer stellar atmospheric parameters using spectral synthesis

Tabernero,

Marfil,

Montes

et al. 2021

Preprint

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Context. SteParSyn is an automatic code written in Python 3.X designed to infer the stellar atmospheric parameters T eff , log g, and [Fe/H] of FGKM-type stars following the spectral synthesis method. Aims. We present a description of the SteParSyn code and test its performance against a sample of late-type stars that were observed with the HERMES spectrograph mounted at the 1.2-m Mercator Telescope. This sample contains 35 late-type targets with well-known stellar parameters determined independently from spectroscopy. The code is available to the astronomical community in a GitHub repository. Methods. SteParSyn uses a Markov chain Monte Carlo (MCMC) sampler to explore the parameter space by comparing synthetic model spectra generated on the fly to the observations. The synthetic spectra are generated with an spectral emulator. Results. We computed T eff , log g, and [Fe/H] for our sample stars and discussed the performance of the code. We calculated an internal scatter for these targets of −12 ± 117 K in T eff , 0.04 ± 0.14 dex in log g, and 0.05 ± 0.09 dex in [Fe/H]. In addition, we find that the log g values obtained with SteParSyn are consistent with the trigonometric surface gravities to the 0.1 dex level. Finally, SteParSyn can compute stellar parameters that are accurate down to 50 K, 0.1 dex, and 0.05 dex for Teff, log g, and [Fe/H] for stars with sin i ≤ 30 km s −1 .

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Section: Stellar Atmospheric Parameterssupporting

confidence: 61%

Section: Trigonometric Gravities Radii and Massesmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

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SteParSyn: A Bayesian code to infer stellar atmospheric parameters using spectral synthesis

Tabernero,

Marfil,

Montes

et al. 2021

Preprint

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“…These are a small representation of the statistical studies performed focusing on the potential correlations between the star's proprieties and its hosted planets. Detailed knowledge of the nature of indi-vidual stars is crucial for the thorough understanding of planetary systems currently being characterized by CHEOPS (Benz et al 2021) and ESPRESSO (Pepe et al 2021), and for preparing the selection of targets for future missions such as ARIEL (Tinetti et al 2018;Brucalassi et al 2021), and PLATO (Rauer et al 2014). SWEET-Cat is a catalog with homogeneous spectroscopic parameters for planet hosts, with near-full completeness for the RV detected planets; it was introduced by Santos et al (2013).…”

Section: Introductionmentioning

confidence: 99%

SWEET-Cat 2.0: The Cat just got SWEETer; Higher quality spectra and precise parallaxes from GAIA eDR3

Sousa,

Adibekyan,

Delgado-Mena

et al. 2021

Preprint

Self Cite

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Aims. The catalog of Stars With ExoplanETs (SWEET-Cat) was originally introduced in 2013. Since then many more exoplanets have been confirmed, increasing significantly the number of host stars listed there. A crucial step toward a comprehensive understanding of these new worlds is the precise and homogeneous characterization of their host stars. Better spectroscopic stellar parameters along with new results from Gaia eDR3 provide updated and precise parameters for the discovered planets. A new version of the catalog, whose homogeneity in the derivation of the parameters is key to unraveling star-planet connections, is available to the community. Methods. We made use of high-resolution spectra for planet-host stars, either observed by our team or collected through public archives. The spectroscopic stellar parameters were derived for the spectra following the same homogeneous process using ARES and MOOG (ARES+MOOG) as for the previous SWEET-Cat releases. We re-derived parameters for the stars in the catalog using better quality spectra and/or using the most recent versions of the codes. Moreover, the new SWEET-Cat table can now be more easily combined with the planet properties listed both at the Extrasolar Planets Encyclopedia and at the NASA exoplanet archive to perform statistical analyses of exoplanets. We also made use of the recent GAIA eDR3 parallaxes and respective photometry to derive consistent and accurate surface gravity values for the host stars.Results. We increased the number of stars with homogeneous parameters by more than 40% (from 645 to 928). We reviewed and updated the metallicity distributions of stars hosting planets with different mass regimes comparing the low-mass planets (< 30M ⊕ ) with the high-mass planets. The new data strengthen previous results showing the possible trend in the metallicity-period-mass diagram for low-mass planets.

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“…Freeman & Bland-Hawthorn 2002). Nevertheless, it is common to observe a large dispersion in the values of stellar parameters and abundances when comparing literature results or the performance of different analysis methods applied to the same spectra (Lebzelter et al 2012;Hinkel et al 2014Hinkel et al , 2016Smiljanic et al 2014;Jofré et al 2017;Brucalassi et al 2021). This outcome is a consequence of the complexity of the task.…”

Section: Introductionmentioning

confidence: 99%

Titans metal-poor reference stars. I. Accurate effective temperatures and surface gravities for dwarfs and subgiants from 3D non-LTE H$α$ profiles and Gaia parallaxes

Giribaldi,

da Silva,

Smiljanic

et al. 2021

Preprint

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Context. Several large stellar spectroscopic surveys are producing overwhelming amounts of data that can be used for determining stellar atmospheric parameters and chemical abundances. Nonetheless, the accuracy achieved in the derived astrophysical parameters is still insufficient, mainly because of the paucity of adequate calibrators, particularly in the metal-poor regime ([Fe/H] ≤ −1.0). Aims. Our aim is to increase the number of metal-poor stellar calibrators that have accurate parameters. Here, we introduce the Titans metal-poor reference stars: a sample of 41 dwarf and subgiant stars with accurate, but model-dependent, parameters. Methods. Effective temperatures (T eff ) were derived by fitting observed Hα profiles with synthetic lines computed using threedimensional (3D) hydrodynamic model atmospheres that take into account departures from the local thermodynamic equilibrium (non-LTE effects). Surface gravities (log g) were computed using evolutionary tracks and parallaxes from Gaia early-data release 3. Results. The same methods recover the T eff values of the Gaia benchmark stars, which are mostly based on interferometric measurements, with a 1σ dispersion of ±50 K. We assume this to be the accuracy of the Hα profiles computed from 3D non-LTE models for metal-poor dwarfs and subgiants, although this is likely an upper-bound estimate dominated by the uncertainty of the standard T eff values. We achieved an internal precision typically between 30-40 K, these errors dominated by instrumental effects. The final total uncertainty for the T eff values of the Titans are thus estimated to be of the order of 1%. The typical error for log g is ≤ 0.04 dex. In addition, we identified a few members of Gaia-Enceladus, of Sequoia, and of the Helmi stream in our sample. These stars can pave the way for the accurate chemical characterization of these Galactic substructures. Conclusions. Using the Titans as reference, large stellar surveys will be able to improve the internal calibration of their astrophysical parameters. Ultimately, this sample will help users of data from Gaia and large surveys in reaching their goal of redefining our understanding of stars, stellar systems, and the Milky Way.

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Determination of stellar parameters for Ariel targets: a comparison analysis between different spectroscopic methods

Cited by 7 publications

References 44 publications

SteParSyn: A Bayesian code to infer stellar atmospheric parameters using spectral synthesis

SteParSyn: A Bayesian code to infer stellar atmospheric parameters using spectral synthesis

SWEET-Cat 2.0: The Cat just got SWEETer; Higher quality spectra and precise parallaxes from GAIA eDR3

Titans metal-poor reference stars. I. Accurate effective temperatures and surface gravities for dwarfs and subgiants from 3D non-LTE H$α$ profiles and Gaia parallaxes

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