MASCARA: data handling, processing, and calibration

Stuik, Lesage, Jakobs, Spronck, Snellen

SPIE Proceedings, July 1, 2014

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MASCARA, the Multi-site All-Sky CAmeRA, consists of several fully-automated stations distributed across the globe. Its goal is to find exoplanets transiting the brightest stars, in the V = 4 to 8 magnitude range, currently probed neither by space- nor by ground-based surveys. The nearby transiting planet systems that MASCARA is expected to discover will be key targets for future detailed planet atmosphere observations. Each station contains five wide-angle cameras monitoring the near-entire sky at each location. Once fully deployed, MASCARA will provide a nearly continuous coverage of the dark sky, down to magnitude 8, at sub-minute cadence. Effectively taking an image of the full sky every 6.4 seconds, MASCARA will produce approximately 500 GB of raw data per night, per station. This data needs to be processed in order to produce calibrated light curves, for up to ~40,000 stars down to magnitude 8 and with a signal-to-noise-ratio of better than 100. The aim of the data reduction pipeline is to process the data locally and in real time, both to immediately have quality control, as well as to prevent a data back-log. Although the cameras are fixed and the stars are therefore drifting over the CCDs, MASCARA is a targeted mission. Data processing consists of three main steps: 1. Compute a complete astrometric solution to sub-pixel level for each exposure and extracting postage stamps for each of the stars in the field of view. 2. Perform accurate photometry on each of the postage stamps, including back-ground subtraction and identification of errors in the photometry due to bad pixels, satellites, air planes or Laser Guide Stars. 3. Remove fluctuations on time scales typical for transits, i.e., several hours, caused by for example the camera and atmospheric transmission, color variations in stars and pixel-to-pixel gain fluctuations. Photometry on short time scales already shows noise levels close to the photon noise limit, and using a combination of calibration and relative photometry the red-noise component can be reduced to close to this photon noise limit, allowing for semi-automated identification of exo-planet transits. This paper discusses the data handling, processing and calibration and shows the first results of the pipeline.