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تسجيل مستخدم جديدInvestigation of transit-selected exoplanet candidates from the MACHO survey
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S. D. Huegelmeyer
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Context: Planets outside our solar system transiting their host star, i. e. those with an orbital inclination near 90 degree, are of special interest to derive physical properties of extrasolar planets. With the knowledge of the host stars physical parameters, the planetary radius can be determined. Combined with spectroscopic observations the mass and therefore the density can be derived from Doppler-measurements. Depending on the brightness of the host star, additional information, e. g. about the spin-orbit alignment between the host star and planetary orbit, can be obtained. Aims: The last few years have witnessed a growing success of transit surveys. Among other surveys, the MACHO project provided nine potential transiting planets, several of them with relatively bright parent stars. The photometric signature of a transit event is, however, insufficient to confirm the planetary nature of the faint companion. The aim of this paper therefore is a determination of the spectroscopic parameters of the host stars as well as a dynamical mass determination through Doppler-measurements. Methods: We have obtained follow-up high-resolution spectra for five stars selected from the MACHO sample, which are consistent with transits of low-luminosity objects. Radial velocities have been determined by means of cross-correlation with model spectra. The MACHO light curves have been compared to simulations based on the physical parameters of the system derived from the radial velocities and spectral analyses. Aims: We show that all transit light curves of the exoplanet candidates analysed in this work can be explained by eclipses of stellar objects, hence none of the five transiting objects is a planet.
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The NASA Star and Exoplanet Database (NStED) is a general purpose stellar archive with the aim of providing support for NASAs planet finding and characterization goals, stellar astrophysics, and the planning of NASA and other space missions. There ar
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Vetting of exoplanet candidates in transit surveys is a manual process, which suffers from a large number of false positives and a lack of consistency. Previous work has shown that Convolutional Neural Networks (CNN) provide an efficient solution to
these problems. Here, we apply a CNN to classify planet candidates from the Next Generation Transit Survey (NGTS). For training datasets we compare both real data with injected planetary transits and fully-simulated data, as well as how their different compositions affect network performance. We show that fewer hand labelled lightcurves can be utilised, while still achieving competitive results. With our best model, we achieve an AUC (area under the curve) score of $(95.6pm{0.2})%$ and an accuracy of $(88.5pm{0.3})%$ on our unseen test data, as well as $(76.5pm{0.4})%$ and $(74.6pm{1.1})%$ in comparison to our existing manual classifications. The neural network recovers 13 out of 14 confirmed planets observed by NGTS, with high probability. We use simulated data to show that the overall network performance is resilient to mislabelling of the training dataset, a problem that might arise due to unidentified, low signal-to-noise transits. Using a CNN, the time required for vetting can be reduced by half, while still recovering the vast majority of manually flagged candidates. In addition, we identify many new candidates with high probabilities which were not flagged by human vetters.
We provide the catalogue of all transit-like features, including false alarms, detected by the CoRoT exoplanet teams in the 177 454 light curves of the mission. All these detections have been re-analysed with the same softwares so that to ensure thei
r homogeneous analysis. Although the vetting process involves some human evaluation, it also involves a simple binary flag system over basic tests: detection significance, presence of a secondary, difference between odd and even depths, colour dependence, V-shape transit, and duration of the transit. We also gathered the information from the large accompanying ground-based programme carried out on the planet candidates and checked how useful the flag system could have been at the vetting stage of the candidates. In total, we identified and separated 824 false alarms of various kind, 2269 eclipsing binaries among which 616 are contact binaries and 1653 are detached ones, 37 planets and brown dwarfs, and 557 planet candidates. For the planet candidates, the catalogue gives not only their transit parameters but also the products of their light curve modelling, together with a summary of the outcome of follow-up observations when carried out and their current status. Among the planet candidates whose nature remains unresolved, we estimate that 8 +/- 3 planets are still to be identified. We derived planet and brown dwarf occurrences and confirm disagreements with Kepler estimates: small-size planets with orbital period less than ten days are underabundant by a factor of three in the CoRoT fields whereas giant planets are overabundant by a factor of two. These preliminary results would however deserve further investigations using the recently released CoRoT light curves that are corrected of the various instrumental effects and a homogeneous analysis of the stellar populations observed by the two missions.
Context. The launch of the exoplanet space missions obtaining exquisite photometry from space has resulted in the discovery of thousands of planetary systems with very different physical properties and architectures. Among them, the exoplanet CoRoT-2
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