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Register a new userActivity indicators and stellar parameters of the Kepler targets. An application of the ROTFIT pipeline to LAMOST-Kepler stellar spectra
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The LAMOST-Kepler survey, whose spectra are analyzed in the present paper, is the first large spectroscopic project aimed at characterizing these sources. Our work is focused at selecting emission-line objects and chromospherically active stars and on the evaluation of the atmospheric parameters. We have used a version of the code ROTFIT that exploits a wide and homogeneous collection of real star spectra, i.e. the Indo US library. We provide a catalog with the atmospheric parameters (Teff, logg, [Fe/H]), the radial velocity (RV) and an estimate of the projected rotation velocity (vsini). For cool stars (Teff<6000 K) we have also calculated the H-alpha and CaII-IRT chromospheric fluxes. We have derived the RV and the atmospheric parameters for 61,753 spectra of 51,385 stars. Literature data for a few hundred stars have been used to do a quality control of our results. The final accuracy of RV, Teff, logg, and [Fe/H] measurements is about 14 km/s, 3.5%, 0.3 dex, and 0.2 dex, respectively. However, while the Teff values are in very good agreement with the literature, we noted some issues with the determination of [Fe/H] of metal poor stars and the tendency, for logg, to cluster around the values typical for main sequence and red giant stars. We propose correction relations based on these comparison. The RV distribution is asymmetric and shows an excess of stars with negative RVs which is larger at low metallicities. We could identify stars with variable RV, ultrafast rotators, and emission-line objects. Based on the H-alpha and CaII-IRT fluxes, we have found 442 chromospherically active stars, one of which is a likely accreting object. The availability of precise rotation periods from the Kepler photometry has allowed us to study the dependency of the chromospheric fluxes on the rotation rate for a quite large sample of field stars.
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With the advent of dedicated photometric space missions, the ability to rapidly process huge catalogues of stars has become paramount. Bellinger and Angelou et al. (2016) recently introduced a new method based on machine learning for inferring the stellar parameters of main-sequence stars exhibiting solar-like oscillations. The method makes precise predictions that are consistent with other methods, but with the advantages of being able to explore many more parameters while costing practically no time. Here we apply the method to 52 so-called LEGACY main-sequence stars observed by the Kepler space mission. For each star, we present estimates and uncertainties of mass, age, radius, luminosity, core hydrogen abundance, surface helium abundance, surface gravity, initial helium abundance, and initial metallicity as well as estimates of their evolutionary model parameters of mixing length, overshooting coefficient, and diffusion multiplication factor. We obtain median uncertainties in stellar age, mass, and radius of 14.8%, 3.6%, and 1.7%, respectively. The source code for all analyses and for all figures appearing in this manuscript can be found electronically at: https://github.com/earlbellinger/asteroseismology
Main sequence turn-off (MSTO) stars have advantages as indicators of Galactic evolution since their ages could be robustly estimated from atmospheric parameters. Hundreds of thousands of MSTO stars have been selected from the LAMOST Galactic sur- vey to study the evolution of the Galaxy, and it is vital to derive accurate stellar parameters. In this work, we select 150 MSTO star candidates from the MSTO stars sample of Xiang that have asteroseismic parameters and determine accurate stellar parameters for these stars combing the asteroseismic parameters deduced from the Kepler photometry and atmospheric parameters deduced from the LAMOST spectra.With this sample, we examine the age deter- mination as well as the contamination rate of the MSTO stars sample. A comparison of age between this work and Xiang shows a mean difference of 0.53 Gyr (7%) and a dispersion of 2.71 Gyr (28%). The results show that 79 of the candidates are MSTO stars, while the others are contaminations from either main sequence or sub-giant stars. The contamination rate for the oldest stars is much higher than that for the younger stars. The main cause for the high contamination rate is found to be the relatively large systematic bias in the LAMOST surface gravity estimates.
Using population synthesis tools we create a synthetic Kepler Input Catalogue (KIC) and subject it to the Kepler Stellar Classification Program (SCP) method for determining stellar parameters such as the effective temperature Teff and surface gravity g. We achieve a satisfactory match between the synthetic KIC and the real KIC in the log g vs log Teff diagram, while there is a significant difference between the actual physical stellar parameters and those derived by the SCP of the stars in the synthetic sample. We find a median difference Delta Teff=+500K and Delta log g =-0.2dex for main-sequence stars, and Delta Teff=+50K and Delta log g =-0.5dex for giants, although there is a large variation across parameter space. For a MS star the median difference in g would equate to a ~3% increase in stellar radius and a consequent ~3% overestimate of the radius for any transiting exoplanet. We find no significant difference between Delta Teff and Delta log g for single stars and the primary star in a binary system. We also re-created the Kepler target selection method and found that the binary fraction is unchanged by the target selection. Binaries are selected in similar proportions to single star systems; the fraction of MS dwarfs in the sample increases from about 75% to 80%, and the giant star fraction decreases from 25% to 20%.
Asteroseismology is a powerful tool to precisely determine the evolutionary status and fundamental properties of stars. With the unprecedented precision and nearly continuous photometric data acquired by the NASA Kepler mission, parameters of more than 10$^4$ stars have been determined nearly consistently. However, most studies still use photometric effective temperatures (Teff) and metallicities ([Fe/H]) as inputs, which are not sufficiently accurate as suggested by previous studies. We adopted the spectroscopic Teff and [Fe/H] values based on the LAMOST low-resolution spectra (R~1,800), and combined them with the global oscillation parameters to derive the physical parameters of a large sample of stars. Clear trends were found between {Delta}logg(LAMOST - seismic) and spectroscopic Teff as well as logg, which may result in an overestimation of up to 0.5 dex for the logg of giants in the LAMOST catalog. We established empirical calibration relations for the logg values of dwarfs and giants. These results can be used for determining the precise distances to these stars based on their spectroscopic parameters.
The NASA Transiting Exoplanet Survey Satellite (TESS) is observing tens of millions of stars with time spans ranging from $sim$ 27 days to about 1 year of continuous observations. This vast amount of data contains a wealth of information for variability, exoplanet, and stellar astrophysics studies but requires a number of processing steps before it can be fully utilized. In order to efficiently process all the TESS data and make it available to the wider scientific community, the TESS Data for Asteroseismology working group, as part of the TESS Asteroseismic Science Consortium, has created an automated open-source processing pipeline to produce light curves corrected for systematics from the short- and long-cadence raw photometry data and to classify these according to stellar variability type. We will process all stars down to a TESS magnitude of 15. This paper is the next in a series detailing how the pipeline works. Here, we present our methodology for the automatic variability classification of TESS photometry using an ensemble of supervised learners that are combined into a metaclassifier. We successfully validate our method using a carefully constructed labelled sample of Kepler Q9 light curves with a 27.4 days time span mimicking single-sector TESS observations, on which we obtain an overall accuracy of 94.9%. We demonstrate that our methodology can successfully classify stars outside of our labeled sample by applying it to all $sim$ 167,000 stars observed in Q9 of the Kepler space mission.
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