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The Qatar Exoplanet Survey (QES) is discovering hot Jupiters and aims to discover hot Saturns and hot Neptunes that transit in front of relatively bright host stars. QES currently operates a robotic wide-angle camera system to identify promising transiting exoplanet candidates among which are the confirmed exoplanets Qatar 1b and 2b. This paper describes the first generation QES instrument, observing strategy, data reduction techniques, and follow-up procedures. The QES cameras in New Mexico complement the SuperWASP cameras in the Canary Islands and South Africa, and we have developed tools to enable the QES images and light curves to be archived and analysed using the same methods developed for the SuperWASP datasets. With its larger aperture, finer pixel scale, and comparable field of view, and with plans to deploy similar systems at two further sites, the QES, in collaboration with SuperWASP, should help to speed the discovery of smaller radius planets transiting bright stars in northern skies.
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In this paper we present three new extrasolar planets from the Qatar Exoplanet Survey (QES). Qatar-8b is a hot Saturn, with Mpl = 0.37 Mjup and Rpl = 1.3 Rjup, orbiting a solar-like star every Porb = 3.7 days. Qatar-9b is a hot Jupiter with a mass of Mpl = 1.2 Mjup and a radius of Rpl = 1 Rjup, in a Porb = 1.5 days orbit around a low mass, Mstar = 0.7 Msun, mid-K main-sequence star. Finally, Qatar-10b is a hot, Teq ~ 2000 K, sub-Jupiter mass planet, Mpl = 0.7 Mjup, with a radius of Rpl = 1.54 Rjup and an orbital period of Porb = 1.6 days, placing it on the edge of the sub-Jupiter desert.
WFIRST is NASAs first flagship mission with pre-defined core science programs to study dark energy and perform a statistical census of wide orbit exoplanets with a gravitational microlensing survey. Together, these programs are expected to use more than half of the prime mission observing time. Previously, only smaller, PI-led missions have had core programs that used such a large fraction of the observing time, and in many cases, the data from these PI-led missions was reserved for the PIs science team for a proprietary period that allowed the PIs team to make most of the major discoveries from the data. Such a procedure is not appropriate for a flagship mission, which should provide science opportunities to the entire astronomy community. For this reason, there will be no proprietary period for WFIRST data, but we argue that a larger effort to make WFIRST science accessible to the astronomy community is needed. We propose a plan to enhance community involvement in the WFIRST exoplanet microlensing survey in two different ways. First, we propose a set of high level data products that will enable astronomers without detailed microlensing expertise access to the statistical implications of the WFIRST exoplanet microlensing survey data. And second, we propose the formation of a WFIRST Exoplanet Microlensing Community Science Team that will open up participation in the development of the WFIRST exoplanet microlensing survey to the general astronomy community in collaboration for the NASA selected science team, which will have the responsibility to provide most of the high level data products. This community science team will be open to volunteers, but members should also have the opportunity to apply for funding.
In Spring 2013, the LEECH (LBTI Exozodi Exoplanet Common Hunt) survey began its $sim$130-night campaign from the Large Binocular Telescope (LBT) atop Mt Graham, Arizona. This survey benefits from the many technological achievements of the LBT, including two 8.4-meter mirrors on a single fixed mount, dual adaptive secondary mirrors for high Strehl performance, and a cold beam combiner to dramatically reduce the telescopes overall background emissivity. LEECH neatly complements other high-contrast planet imaging efforts by observing stars at L (3.8 $mu$m), as opposed to the shorter wavelength near-infrared bands (1-2.4 $mu$m) of other surveys. This portion of the spectrum offers deep mass sensitivity, especially around nearby adolescent ($sim$0.1-1 Gyr) stars. LEECHs contrast is competitive with other extreme adaptive optics systems, while providing an alternative survey strategy. Additionally, LEECH is characterizing known exoplanetary systems with observations from 3-5$mu$m in preparation for JWST.
We used the light curve archive of the Qatar Exoplanet Survey (QES) to investigate the RR Lyrae variable stars listed in the General Catalogue of Variable Stars (GCVS). Of 588 variables studied, we reclassify 14 as eclipsing binaries, one as an RS Canum Venaticorum-type variable, one as an irregular variable, four as classical Cepheids, and one as a type II Cepheid, while also improving their periods. We also report new RR Lyrae sub-type classifications for 65 variables and improve on the GCVS period estimates for 135 RR Lyrae variables. There are seven double-mode RR Lyrae stars in the sample for which we measured their fundamental and first overtone periods. Finally, we detect the Blazhko effect in 38 of the RR Lyrae stars for the first time and we successfully measured the Blazhko period for 26 of them.
The Gemini Planet Imager Exoplanet Survey (GPIES) is a multi-year direct imaging survey of 600 stars to discover and characterize young Jovian exoplanets and their environments. We have developed an automated data architecture to process and index all data related to the survey uniformly. An automated and flexible data processing framework, which we term the Data Cruncher, combines multiple data reduction pipelines together to process all spectroscopic, polarimetric, and calibration data taken with GPIES. With no human intervention, fully reduced and calibrated data products are available less than an hour after the data are taken to expedite follow-up on potential objects of interest. The Data Cruncher can run on a supercomputer to reprocess all GPIES data in a single day as improvements are made to our data reduction pipelines. A backend MySQL database indexes all files, which are synced to the cloud, and a front-end web server allows for easy browsing of all files associated with GPIES. To help observers, quicklook displays show reduced data as they are processed in real-time, and chatbots on Slack post observing information as well as reduced data products. Together, the GPIES automated data processing architecture reduces our workload, provides real-time data reduction, optimizes our observing strategy, and maintains a homogeneously reduced dataset to study planet occurrence and instrument performance.
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