No Arabic abstract
The Hungarian-made Automated Telescope Network (HATNet) has been in operation since 2003, with the key science goal being the discovery and accurate characterization of transiting extrasolar planets (TEPs) around bright stars. Using six small, 11,cm aperture, fully automated telescopes in Arizona and Hawaii, as of 2017 March, it has discovered and accurately characterized 67 such objects. The HATSouth network of telescopes has been in operation since 2009, using slightly larger, 18,cm diameter optical tubes. It was the first global network of telescopes using identical instrumentation. With three premier sites spread out in longitude (Chile, Namibia, Australia), the HATSouth network permits round-the-clock observations of a 128 square arcdegree swath of the sky at any given time, weather permitting. As of this writing, HATSouth has discovered 36 transiting exoplanets. Many of the altogether ~100 HAT and HATSouth exoplanets were the first of their kind. They have been important contributors to the rapidly developing field of exoplanets, motivating and influencing observational techniques, theoretical studies, and also actively shaping future instrumentation for the detection and characterization of such objects.
We summarize the contribution of the HATNet project to extrasolar planet science, highlighting published planets (HAT-P-1b through HAT-P-26b). We also briefly discuss the operations, data analysis, candidate selection and confirmation procedures, and we summarize what HATNet provides to the exoplanet community with each discovery.
We report the results of a ${sim}4$-year direct imaging survey of 104 stars to resolve and characterize circumstellar debris disks in scattered light as part of the Gemini Planet Imager Exoplanet Survey. We targeted nearby (${lesssim}150$ pc), young (${lesssim}500$ Myr) stars with high infrared excesses ($L_{mathrm{IR}} / L_star > 10^{-5}$), including 38 with previously resolved disks. Observations were made using the Gemini Planet Imager high-contrast integral field spectrograph in $H$-band (1.6 $mu$m) coronagraphic polarimetry mode to measure both polarized and total intensities. We resolved 26 debris disks and three protoplanetary/transitional disks. Seven debris disks were resolved in scattered light for the first time, including newly presented HD 117214 and HD 156623, and we quantified basic morphologies of five of them using radiative transfer models. All of our detected debris disks but HD 156623 have dust-poor inner holes, and their scattered-light radii are generally larger than corresponding radii measured from resolved thermal emission and those inferred from spectral energy distributions. To assess sensitivity, we report contrasts and consider causes of non-detections. Detections were strongly correlated with high IR excess and high inclination, although polarimetry outperformed total intensity angular differential imaging for detecting low inclination disks (${lesssim} 70 deg$). Based on post-survey statistics, we improved upon our pre-survey target prioritization metric predicting polarimetric disk detectability. We also examined scattered-light disks in the contexts of gas, far-IR, and millimeter detections. Comparing $H$-band and ALMA fluxes for two disks revealed tentative evidence for differing grain properties. Finally, we found no preference for debris disks to be detected in scattered light if wide-separation substellar companions were present.
Since the start of the Wide Angle Search for Planets (WASP) program, more than 160 transiting exoplanets have been discovered in the WASP data. In the past, possible transit-like events identified by the WASP pipeline have been vetted by human inspection to eliminate false alarms and obvious false positives. The goal of the present paper is to assess the effectiveness of machine learning as a fast, automated, and reliable means of performing the same functions on ground-based wide-field transit-survey data without human intervention. To this end, we have created training and test datasets made up of stellar light curves showing a variety of signal types including planetary transits, eclipsing binaries, variable stars, and non-periodic signals. We use a combination of machine learning methods including Random Forest Classifiers (RFCs) and Convolutional Neural Networks (CNNs) to distinguish between the different types of signals. The final algorithms correctly identify planets in the test data ~90% of the time, although each method on its own has a significant fraction of false positives. We find that in practice, a combination of different methods offers the best approach to identifying the most promising exoplanet transit candidates in data from WASP, and by extension similar transit surveys.
We report the discovery of HATS-5b, a transiting hot-Saturn orbiting a G type star, by the HAT-South survey. HATS-5b has a mass of Mp=0.24 Mj, radius of Rp=0.91 Rj, and transits its host star with a period of P=4.7634d. The radius of HATS-5b is consistent with both theoretical and empirical models. The host star has a V band magnitude of 12.6, mass of 0.94 Msun, and radius of 0.87 Rsun. The relatively high scale height of HATS-5b, and the bright, photometrically quiet host star, make this planet a favourable target for future transmission spectroscopy follow-up observations. We reexamine the correlations in radius, equilibrium temperature, and metallicity of the close-in gas-giants, and find hot Jupiter-mass planets to exhibit the strongest dependence between radius and equilibrium temperature. We find no significant dependence in radius and metallicity for the close-in gas-giant population.
We report the discovery of HATS-1b, a transiting extrasolar planet orbiting the moderately bright V=12.05 G dwarf star GSC 6652-00186, and the first planet discovered by HATSouth, a global network of autonomous wide-field telescopes. HATS-1b has a period P~3.4465 d, mass Mp~1.86MJ, and radius Rp~1.30RJ. The host star has a mass of 0.99Msun, and radius of 1.04Rsun. The discovery light curve of HATS-1b has near continuous coverage over several multi-day periods, demonstrating the power of using a global network of telescopes to discover transiting planets.