Bulgarian Academy of Sciences

About: Bulgarian Academy of Sciences is a government organization based out in Sofia, Bulgaria. It is known for research contribution in the topics: Catalysis & Coupling constant. The organization has 17989 authors who have published 36276 publications receiving 642820 citations. The organization is also known as: Bulgarian Academy of Sciences，簡稱：BAS & Balgarska Akademiya na Naukite.

The VLT-FLAMES Tarantula Survey: I. Introduction and observational overview⋆

Abstract: The VLT-FLAMES Tarantula Survey (VFTS) is an ESO Large Programme that has obtained multi-epoch optical spectroscopy of over 800 massive stars in the 30 Doradus region of the Large Magellanic Cloud (LMC). Here we introduce our scientific motivations and give an overview of the survey targets, including optical and near-infrared photometry and comprehensive details of the data reduction. One of the principal objectives was to detect massive binary systems via variations in their radial velocities, thus shaping the multi-epoch observing strategy. Spectral classifications are given for the massive emission-line stars observed by the survey, including the discovery of a new Wolf-Rayet star (VFTS 682, classified as WN5h), 2' to the northeast of R136. To illustrate the diversity of objects encompassed by the survey, we investigate the spectral properties of sixteen targets identified by Gruendl & Chu from Spitzer photometry as candidate young stellar objects or stars with notable mid-infrared excesses. Detailed spectral classification and quantitative analysis of the O- and B-type stars in the VFTS sample, paying particular attention to the effects of rotational mixing and binarity, will be presented in a series of future articles to address fundamental questions in both stellar and cluster evolution.

Using diversity in cluster ensembles

Abstract: The pairwise approach to cluster ensembles uses multiple partitions, each of which constructs a coincidence matrix between all pairs of objects. The matrices for the partitions are then combined and a final clustering is derived thereof. Here we study the diversity within such cluster ensembles. Based on this, we propose a variant of the generic ensemble method where the number of overproduced clusters is chosen randomly for every ensemble member (partition). Using three artificial sets we show that this approach increases the spread of the diversity within the ensemble thereby leading to a better match with the known cluster labels. Experimental results with three real data sets are also reported.