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Register a new userAutomated differential photometry of TAOS data: preliminary analysis
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A preliminary data analysis of the stellar light curves obtained by the robotic telescopes of the TAOS project is presented. We selected a data run relative to one of the stellar fields observed by three of the four TAOS telescopes, and we investigate the common trend and the correlation between the light curves. We propose two ways to remove these trends and show the preliminary results. A project aimed at flagging interesting behaviors, such as stellar variability, and to set up an automated follow-up with the San Pedro Martir Facilities is on the way.
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We report here on our search for excess power in photometry of Neptune collected by the K2 mission that may be due to intrinsic global oscillations of the planet Neptune. To conduct this search, we developed new methods to correct for instrumental effects such as intrapixel variability and gain variations. We then extracted and analyzed the time-series photometry of Neptune from 49 days of nearly continuous broadband photometry of the planet. We find no evidence of global oscillations and place an upper limit of $sim$5 ppm at 1000 uhz for the detection of a coherent signal. With an observed cadence of 1-minute and point-to-point scatter less than 0.01%, the photometric signal is dominated by reflected light from the Sun, which is in turn modulated by atmospheric variability of Neptune at the 2% level. A change in flux is also observed due to the increasing distance between Neptune and the K2 spacecraft, and solar variability with convection-driven solar p modes present.
The Taiwanese-American Occultation Survey (TAOS) aims to detect serendipitous occultations of stars by small (about 1 km diameter) objects in the Kuiper Belt and beyond. Such events are very rare (<0.001 events per star per year) and short in duration (about 200 ms), so many stars must be monitored at a high readout cadence. TAOS monitors typically around 500 stars simultaneously at a 5 Hz readout cadence with four telescopes located at Lulin Observatory in central Taiwan. In this paper, we report the results of the search for small Kuiper Belt Objects (KBOs) in seven years of data. No occultation events were found, resulting in a 95% c.l. upper limit on the slope of the faint end of the KBO size distribution of q = 3.34 to 3.82, depending on the surface density at the break in the size distribution at a diameter of about 90 km.
We suggest a new web-based approach for browsing and visualizing data produced by a network of telescopes, such as those of the ongoing TAOS and the forthcoming TAOS II projects. We propose a modern client-side technology and we present two examples based on two software packages developed for different kinds of server- side database approaches. In spite our examples are specific for the browsing of TAOS light curves, the software is coded in a way to be suitable for the use in several types of astronomical projects.
We present a new method to assess the properties of transiting planet candidates by multicolor photometry. By analyzing multicolor transit/eclipse light curves and apparent magnitudes of the target in parallel, this method attempts to identify the nature of the system and provide a quantitative constraint on the properties of unresolved companion(s). We demonstrate our method by observing the six systems hosting candidate transiting planets, identified by the K2 mission (EPIC 206036749, EPIC 206500801, EPIC 210513446, EPIC 211800191, EPIC 220621087, and EPIC 220696233). Applying our analysis code to the six targets, we find that EPIC 206036749, EPIC 210513446, and EPIC 211800191 are likely to be triple-star systems including eclipsing binaries, and EPIC 220696233 is likely a planetary system, albeit further observations are required to confirm the nature. Additionally, we confirm that the systematic errors in the derived system parameters arising from adopting specific isochrone models and observing instruments (passbands) are relatively small. While this approach alone is not powerful enough to validate or refute planet candidates, the technique allows us to constrain the properties of resolved/unresolved companions, and prioritize the planet candidates for further follow-up observations (e.g., radial-velocity measurements).
A novel artificial intelligence (AI) technique that uses machine learning (ML) methodologies combines several algorithms, which were developed by ThetaRay, Inc., is applied to NASAs Transiting Exoplanets Survey Satellite (TESS) dataset to identify exoplanetary candidates. The AI/ML ThetaRay system is trained initially with Kepler exoplanetary data and validated with confirmed exoplanets before its application to TESS data. Existing and new features of the data, based on various observational parameters, are constructed and used in the AI/ML analysis by employing semi-supervised and unsupervised machine learning techniques. By the application of ThetaRay system to 10,803 light curves of threshold crossing events (TCEs) produced by the TESS mission, obtained from the Mikulski Archive for Space Telescopes, the algorithm yields about 50 targets for further analysis, and we uncover three new exoplanetary candidates by further manual vetting. This study demonstrates for the first time the successful application of the particular combined multiple AI/ML-based methodologies to a large astrophysical dataset for rapid automated classification of TCEs.
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