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Register a new userImproved Variable Star Search in Large Photometric Data Sets -- New Variables in CoRoT Field LRa02 Detected by BEST II
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The CoRoT field LRa02 has been observed with the Berlin Exoplanet Search Telescope II (BEST II) during the southern summer 2007/2008. A first analysis of stellar variability led to the publication of 345 newly discovered variable stars. Now, a deeper analysis of this data set was used to optimize the variability search procedure. Several methods and parameters have been tested in order to improve the selection process compared to the widely used J index for variability ranking. This paper describes an empirical approach to treat systematic trends in photometric data based upon the analysis of variance statistics that can significantly decrease the rate of false detections. Finally, the process of reanalysis and method improvement has virtually doubled the number of variable stars compared to the first analysis by Kabath et al. A supplementary catalog of 272 previously unknown periodic variables plus 52 stars with suspected variability is presented. Improved ephemerides are given for 19 known variables in the field. In addition, the BEST II results are compared with CoRoT data and its automatic variability classification.
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The Berlin Exoplanet Search Telescope II (BEST II) is a small wide field-of-view photometric survey telescope system located at the Observatorio Cerro Armazones, Chile. The high duty cycle combined with excellent observing conditions and millimagnitude photometric precision makes this instrument suitable for ground based support observations for the CoRoT space mission. Photometric data of the CoRoT LRa02 target field collected between November 2008 and March 2009 were analysed for stellar variability. The presented results will help in the future analysis of the CoRoT data, particularly in additional science programs related to variable stars. BEST II observes selected CoRoT target fields ahead of the space mission. The photometric data acquired are searched for stellar variability, periodic variable stars are identified with time series analysis of the obtained stellar light curves. We obtained the light curves of 104335 stars in the CoRoT LRa02 field over 41 nights. Variability was detected in light curves of 3726 stars of which 350 showed a regular period. These stars are, with the exception of 5 previously known variable stars, new discoveries.
We describe a methodology to classify periodic variable stars identified using photometric time-series measurements constructed from the Wide-field Infrared Survey Explorer (WISE) full-mission single-exposure Source Databases. This will assist in the future construction of a WISE Variable Source Database that assigns variables to specific science classes as constrained by the WISE observing cadence with statistically meaningful classification probabilities. We have analyzed the WISE light curves of 8273 variable stars identified in previous optical variability surveys (MACHO, GCVS, and ASAS) and show that Fourier decomposition techniques can be extended into the mid-IR to assist with their classification. Combined with other periodic light-curve features, this sample is then used to train a machine-learned classifier based on the random forest (RF) method. Consistent with previous classification studies of variable stars in general, the RF machine-learned classifier is superior to other methods in terms of accuracy, robustness against outliers, and relative immunity to features that carry little or redundant class information. For the three most common classes identified by WISE: Algols, RR Lyrae, and W Ursae Majoris type variables, we obtain classification efficiencies of 80.7%, 82.7%, and 84.5% respectively using cross-validation analyses, with 95% confidence intervals of approximately +/-2%. These accuracies are achieved at purity (or reliability) levels of 88.5%, 96.2%, and 87.8% respectively, similar to that achieved in previous automated classification studies of periodic variable stars.
Up to now, planet search programs have concentrated on main sequence stars later than spectral type F5. However, identifying planets of early type stars would be interesting. For example, the mass loss of planets orbiting early and late type stars is different because of the differences of the EUV and X-ray radiation of the host stars. As an initial step, we carried out a program to identify suitable A-stars in the CoRoT fields using spectra taken with the AAOmega spectrograph. In total we identified 562 A-stars in IRa01, LRa01, and LRa02.
Up to now, planet search programs have concentrated on main sequence stars later than spectral type F5. However, identifying planets of early type stars would be interesting. For example, the mass loss of planets orbiting early and late type stars is different because of the differences of the EUV and X-ray radiation of the host stars. As an initial step, we carried out a program to identify suitable A-stars in the CoRoT fields using spectra taken with the AAOmega spectrograph. In total we identified 562 A-stars in IRa01, LRa01, and LRa02.
With now more than 20 exoplanets discovered by CoRoT, it has often been considered strange that so many of them are orbiting F-stars, and so few of them K or M-stars. Although transit search programs are mostly sensitive to short-period planets, they are ideal for verifying these results. To determine the frequency of planets as a function of stellar mass, we also have to characterize the sample of stars that was observed. We study the stellar content of the CoRoT-fields IRa01, LRa01 (=LRa06), and LRa02 by determining the spectral types of 11466 stars. We used spectra obtained with the multi-object spectrograph AAOmega and derived the spectral types by using template spectra with well-known parameters. We find that 34.8+/-0.7% of the stars observed by CoRoT in these fields are F-dwarfs, 15.1+/-0.5% G-dwarfs, and 5.0+/-0.3% K-dwarfs. We conclude that the apparent lack of exoplanets of K- and M-stars is explained by the relatively small number of these stars in the observed sample. We also show that the apparently large number of planets orbiting F-stars is similarly explained by the large number of such stars in these fields. Our study also shows that the difference between the sample of stars that CoRoT observes and a sample of randomly selected stars is relatively small, and that the yield of CoRoT specifically is the detection one hot Jupiter amongst 2100+/-700 stars. We conclude that transit search programs can be used to study the relation between the frequency of planets and the mass of the host stars, and that the results obtained so far generally agree with those of radial velocity programs.
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