The Cherenkov Telescope Array (CTA) is a new observatory for very high-energy (VHE) gamma rays. CTA has ambitions science goals, for which it is necessary to achieve full-sky coverage, to improve the sensitivity by about an order of magnitude, to span about four decades of energy, from a few tens of GeV to above 100 TeV with enhanced angular and energy resolutions over existing VHE gamma-ray observatories. An international collaboration has formed with more than 1000 members from 27 countries in Europe, Asia, Africa and North and South America. In 2010 the CTA Consortium completed a Design Study and started a three-year Preparatory Phase which leads to production readiness of CTA in 2014. In this paper we introduce the science goals and the concept of CTA, and provide an overview of the project. ?? 2013 Elsevier B.V. All rights reserved

The Central Calibration Facilities workpackage of the Cherenkov Telescope Array (CTA) observatory for very high energy gamma ray astronomy defines the overall calibration strategy of the array, develops dedicated hardware and software for the overall array calibration and coordinates the calibration efforts of the different telescopes. The latter include LED-based light pulsers, and various methods and instruments to achieve a calibration of the overall optical throughput. On the array level, methods for the inter-telescope calibration and the absolute calibration of the entire observatory are being developed. Additionally, the atmosphere above the telescopes, used as a calorimeter, will be monitored constantly with state-of-the-art instruments to obtain a full molecular and aerosol profile up to the stratosphere. The aim is to provide a maximal uncertainty of 10% on the reconstructed energy-scale, obtained through various independent methods. Different types of LIDAR in combination with all-sky-cameras will provide the observatory with an online, intelligent scheduling system, which, if the the sky is partially covered by clouds, gives preference to sources observable under good atmospheric conditions. Wide-field optical telescopes and Raman Lidars will provide online information about the heightresolved atmospheric extinction, throughout the field-of-view of the cameras, allowing for the correction of the reconstructed energy of each gamma-ray event. The aim is to maximize the duty cycle of the observatory, in terms of usable data, while reducing the dead time introduced by calibration activities to an absolute minimum.

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The masking of cloud shadows in optical satellite imagery is an important step in automated processing chains. A new method (the TIP method) for cloud shadow detection in multi-spectral satellite images is presented and compared to current methods. The TIP method is based on the evaluation of thresholds, indices and projections. Most state-of-the-art methods solemnly rely on one of these evaluation steps or on a complex working mechanism. Instead, the new method incorporates three basic evaluation steps into one algorithm for easy and accurate cloud shadow detection. Furthermore the performance of the masking algorithms provided by the software packages ATCOR (“Atmospheric Correction”) and PACO (“Python-based Atmospheric Correction”) is compared with that of the newly implemented TIP method on a set of 20 Sentinel-2 scenes distributed over the globe, covering a wide variety of environments and climates. The algorithms incorporated in each piece of masking software use the class of cloud shadows, but they employ different rules and class-specific thresholds. Classification results are compared to the assessment of an expert human interpreter. The class assignment of the human interpreter is considered as reference or “truth”. The overall accuracies for the class cloud shadows of ATCOR and PACO (including TIP) for difference areas of the selected scenes are 70.4% and 76.6% respectively. The difference area encompasses the parts of the classification image where the classification maps disagree. User and producer accuracies for the class cloud shadow are strongly scene-dependent, typically varying between 45% and 95%. The experimental results show that the proposed TIP method based on thresholds, indices and projections can obtain improved cloud shadow detection performance.

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