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Register a new userClassifying Single Stars and Spectroscopic Binaries Using Optical Stellar Templates
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Stellar spectral classification is a fundamental tool of modern astronomy, providing insight into physical characteristics such as effective temperature, surface gravity, and metallicity. Accurate and fast spectral typing is an integral need for large all-sky spectroscopic surveys like the SDSS and LAMOST. Here, we present the next version of PyHammer, stellar spectral classification software that uses optical spectral templates and spectral line index measurements. PyHammer v2.0 extends the classification power to include carbon (C) stars, DA white dwarf (WD) stars, and also double-lined spectroscopic binaries (SB2). This release also includes a new empirical library of luminosity-normalized spectra that can be used to flux calibrate observed spectra, or to create synthetic SB2 spectra. We have generated physically reasonable SB2 combinations as templates, adding to PyHammer the ability to spectrally type SB2s. We test classification success rates on SB2 spectra, generated from the SDSS, across a wide range of spectral types and signal-to-noise ratios. Within the defined range of pairings described, more than $95%$ of SB2s are correctly classified.
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We present the first analysis of results from the SuperWASP Variable Stars Zooniverse project, which is aiming to classify 1.6 million phase-folded light curves of candidate stellar variables observed by the SuperWASP all sky survey with periods detected in the SuperWASP periodicity catalogue. The resultant data set currently contains $>$1 million classifications corresponding to $>$500,000 object-period combinations, provided by citizen scientist volunteers. Volunteer-classified light curves have $sim$89 per cent accuracy for detached and semi-detached eclipsing binaries, but only $sim$9 per cent accuracy for rotationally modulated variables, based on known objects. We demonstrate that this Zooniverse project will be valuable for both population studies of individual variable types and the identification of stellar variables for follow up. We present preliminary findings on various unique and extreme variables in this analysis, including long period contact binaries and binaries near the short-period cutoff, and we identify 301 previously unknown binaries and pulsators. We are now in the process of developing a web portal to enable other researchers to access the outputs of the SuperWASP Variable Stars project.
Recent and on-going large ground-based multi-object spectroscopic surveys allow to significantly increase the sample of spectroscopic binaries to get insight into their statistical properties. We investigate the repeated spectral observations of the Gaia-ESO Survey (GES) internal data release 5 to identify and characterize spectroscopic binaries with one visible component (SB1) in fields covering the discs, the bulge, the CoRot fields, and stellar clusters and associations. A statistical chi2-test is performed on spectra of the iDR5 sub-sample of approximately 43500 stars characterized by at least 2 observations and a S/N > 3. Our sample of RV variables is cleaned from contamination by pulsation/convection-induced variables using Gaia DR2 parallaxes and photometry. Monte-Carlo simulations using the SB9 catalogue of spectroscopic orbits allow to estimate our detection efficiency and to correct the SB1 rate to evaluate the GES SB1 binary fraction and its dependence with effective temperature and metallicity. We find 641 (resp., 803) FGK SB1 candidates at the 5 sigma (resp., 3 sigma) level. The orbital-period distribution is estimated from the RV standard-deviation distribution and reveals that the detected SB1 probe binaries with log(P[d]) < 4. We estimate the global GES SB1 fraction to be in the range 7-14% with a typical uncertainty of 4%. The GES SB1 frequency decreases with metallicity at a rate of -9+/-3%/dex in the metallicity range -2.7<FeH<+0.6. This anticorrelation is obtained with a confidence level higher than 93% on a homogeneous sample covering spectral types FGK and a large range of metallicities. When the present-day mass function is accounted for, this rate turns to 4+/-2%/dex with a confidence level higher than 88%. In addition we provide the variation of the SB1 fraction with metallicity separately for F, G, and K spectral types, as well as for dwarf and giant primaries.
Current ongoing stellar spectroscopic surveys (RAVE, GALAH, Gaia-ESO, LAMOST, APOGEE, Gaia) are mostly devoted to studying Galactic archaeology and structure of the Galaxy. But they allow for important auxiliary science: (i) Galactic interstellar medium can be studied in four dimensions (position in space + radial velocity) through weak but numerous diffuse insterstellar bands and atomic absorptions seen in spectra of background stars, (ii) emission spectra which are quite frequent even in field stars can serve as a good indicator of their youth, pointing e.g. to stars recently ejected from young stellar environments, (iii) astrometric solution of the photocenter of a binary to be obtained by Gaia can yield accurate masses when joined by spectroscopic information obtained serendipitously during a survey. These points are illustrated by first results from the first three surveys mentioned above. These hint at the near future: spectroscopic studies of the dynamics of the interstellar medium can identify and quantify Galactic fountains which may sustain star formation in the disk by entraining fresh gas from the halo; RAVE already provided a list of ~14,000 field stars with chromosperic emission in Ca II lines, to be supplemented by many more observations by Gaia in the same band, and by GALAH and Gaia-ESO observations of Balmer lines; several millions of astrometric binaries with periods up to a few years which are being observed by Gaia can yield accurate masses when supplemented with measurements from only a few high-quality ground based spectra.
The unparalleled photometric data obtained by NASAs Kepler Space Telescope has led to an improved understanding of stellar structure and evolution - in particular for solar-like oscillators in this context. Binary stars are fascinating objects. Because they were formed together, binary systems provide a set of two stars with very well constrained parameters. Those can be used to study properties and physical processes, such as the stellar rotation, dynamics and rotational mixing of elements and allows us to learn from the differences we find between the two components. In this work, we discussed a detailed study of the binary system KIC9163796, discovered through Kepler photometry. The ground-based follow-up spectroscopy showed that this system is a double-lined spectroscopic binary, with a mass ratio close to unity. However, the fundamental parameters of the components of this system as well as their lithium abundances differ substantially. Kepler photometry of this system allows to perform a detailed seismic analysis as well as to derive the orbital period and the surface rotation rate of the primary component of the system. Indications of the seismic signature of the secondary are found. The differing parameters are best explained with both components located in the early and the late phase of the first dredge up at the bottom of the red-giant branch. Observed lithium abundances in both components are in good agreement with prediction of stellar models including rotational mixing. By combining observations and theory, a comprehensive picture of the system can be drawn.
The recently launched NASA Transiting Exoplanet Survey Satellite (TESS) mission is going to collect lightcurves for a few hundred million of stars and we expect to increase the number of pulsating stars to analyze compared to the few thousand stars observed by the CoRoT, $textit{Kepler}$ and K2 missions. However, most of the TESS targets have not yet been properly classified and characterized. In order to improve the analysis of the TESS data, it is crucial to determine the type of stellar pulsations in a timely manner. We propose an automatic method to classify stars attending to their pulsation properties, in particular, to identify solar-like pulsators among all TESS targets. It relies on the use of the global amount of power contained in the power spectrum (already known as the FliPer method) as a key parameter, along with the effective temperature, to feed into a machine learning classifier. Our study, based on TESS simulated datasets, shows that we are able to classify pulsators with a $98%$ accuracy.
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