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

Context. Sky surveys produce enormous quantities of data on extensive regions of the sky. The easiest way to access this information is through catalogues of standardised data products. XMM-Newton has been surveying the sky in the X-ray, ultra-violet, and optical bands for 20 years. Aims. The XMM-Newton Survey Science Centre has been producing standardised data products and catalogues to facilitate access to the serendipitous X-ray sky. Methods. Using improved calibration and enhanced software, we re-reduced all of the 14 041 XMM-Newton X-ray observations, of which 11 204 observations contained data with at least one detection and with these we created a new, high quality version of the XMM-Newton serendipitous source catalogue, 4XMM-DR9. Results. 4XMM-DR9 contains 810 795 detections down to a detection significance of 3σ, of which 550 124 are unique sources, which cover 1152 degrees2 (2.85%) of the sky. Filtering 4XMM-DR9 to retain only the cleanest sources with at least a 5σ detection significance leaves 433 612 detections. Of these detections, 99.6% have no pileup. Furthermore, 336 columns of information on each detection are provided, along with images. The quality of the source detection is shown to have improved significantly with respect to previous versions of the catalogues. Spectra and lightcurves are also made available for more than 288 000 of the brightest sources (36% of all detections).

The Crab Nebula has been observed by the HEGRA (High-Energy Gamma-Ray Astronomy) stereoscopic system of imaging air Cerenkov telescopes (IACTs) for a total of D200 hr during two observational campaigns : from 1997 September to 1998 March and from 1998 August to 1999 April. The recent detailed studies of system performance give an energy threshold and an energy resolution for c-rays of 500 GeV and D18%, respectively. The Crab energy spectrum was measured with the HEGRA IACT system in a very broad energy range up to 20 TeV, using observations at zenith angles up to 65¡. The Crab data can be Ðtted in the energy range from 1 to 20 TeV by a simple power law, which yields TeV)~2.59B0.03B0.05 photons m~2 s~1 TeV~1. The Crab dJ c /dE \ (2.79^0.02^0.5) ] 10~7(E/1 Nebula energy spectrum, as measured with the HEGRA IACT system, agrees within 15% in the absolute scale and within 0.1 units in the power-law index with the latest measurements by the Whipple, CANGAROO, and CAT groups, consistent within the statistical and systematic errors quoted by the experiments. The pure power-law spectrum of TeV c-rays from the Crab Nebula constrains the physics parameters of the nebula environment as well as the models of photon emission.

Context. There are a number of methods that identify stellar sub-structure in star forming regions, but these do not quantify the degree of association of individual stars – something which is required if we are to better understand the mechanisms and physical processes that dictate structure. Aims. We present the new novel statistical clustering tool “INDICATE” which assesses and quantifies the degree of spatial clustering of each object in a dataset, discuss its applications as a tracer of morphological stellar features in star forming regions, and to look for these features in the Carina Nebula (NGC 3372). Methods. We employ a nearest neighbour approach to quantitatively compare the spatial distribution in the local neighbourhood of an object with that expected in an evenly spaced uniform (i.e. definitively non-clustered) field. Each object is assigned a clustering index (“I”) value, which is a quantitative measure of its clustering tendency. We have calibrated our tool against random distributions to aid interpretation and identification of significant I values. Results. Using INDICATE we successfully recover known stellar structure of the Carina Nebula, including the young Trumpler 14-16, Treasure Chest and Bochum 11 clusters. Four sub-clusters contain no, or very few, stars with a degree of association above random which suggests these sub-clusters may be fluctuations in the field rather than real clusters. In addition we find: (1) Stars in the NW and SE regions have significantly different clustering tendencies, which is reflective of differences in the apparent star formation activity in these regions. Further study is required to ascertain the physical origin of the difference; (2) The different clustering properties between the NW and SE regions are also seen for OB stars and are even more pronounced; (3) There are no signatures of classical mass segregation present in the SE region – massive stars here are not spatially concentrated together above random; (4) Stellar concentrations are more frequent around massive stars than typical for the general population, particularly in the Tr14 cluster; (5) There is a relation between the concentration of OB stars and the concentration of (lower mass) stars around OB stars in the centrally concentrated Tr14 and Tr15, but no such relation exists in Tr16. We conclude this is due to the highly sub-structured nature of Tr16. Conclusions. INDICATE is a powerful new tool employing a novel approach to quantify the clustering tendencies of individual objects in a dataset within a user-defined parameter space. As such it can be used in a wide array of data analysis applications. In this paper we have discussed and demonstrated its application to trace morphological features of young massive clusters.