Searching for cluster substructure using APM and ROSAT data

We present a detailed study of the morphological features of 22 rich galaxy clusters. Our sample is constructed from a cross-correlation of optical (Abell+APM) data with X-ray (0.1 - 2.4) keV ROSAT pointed observations. We systematically compare cluster images and morphological parameters in an attempt to reliably identify possible substructure in both optical and the X-ray images. To this end, we compute various moments of the optical and X-ray surface-brightness distribution such as the ellipticities, center-of-mass shifts and ellipsoidal orientations. We assess the significance of our results using Monte Carlo simulations. We find significant correlations between the optical and X-ray morphological parameters, indicating that in both parts of the spectrum it is possible to identify correctly the dynamical state of a cluster. Most of our clusters (17/22) have a good 1-to-1 correspondence between the optical and the X-ray images and about 10 appear to have strong indications of substructure. This corresponds to a minimum percentage of order 45 per cent which is in very good accordance with other similar analyses. Finally, 5 out of 22 systems seem to have distinct subclumps in the optical which are not verified in the X-ray images, and thus are suspect of being due to optical projection effects. These results will serve as a useful guide in interpreting subsequent analyses of large optical cluster catalogues.Comment: 15 pages, including 9 figures, MNRAS in press, revised versio

Open Source Adoption In Large US Companies

Various organizations increasingly adopt open source software, both on desktop PCs and servers. Since the first movements in open source in the 1960’s its growth has lead to new approaches in software development, licensing, and distribution, as well as in software vendors’ business models. The literature includes very interesting studies regarding prospective benefits, business models and case studies. However, the adoption of open source in large, global companies and its relationship with factors such as profitability, revenues and industry sector has not yet been researched. This study aims to answer these questions based on data we collected from Fortune 1000 companies and provides a method that can be applied in similar contexts