Aims. Various galaxy classification schemes have been developed so far to constrain the main physical processes regulating evolution of different galaxy types. In the era of a deluge of astrophysical information and recent progress in machine learning, a new approach to galaxy classification has become imperative. Methods. In this paper, we employ a Fisher Expectation-Maximization (FEM) unsupervised algorithm working in a parameter space of 12 restframe magnitudes and spectroscopic redshift. The model (DBk) and the number of classes (12) were established based on the joint analysis of standard statistical criteria and confirmed by the analysis of the galaxy distribution with respect to a number of classes and their properties. This new approach allows us to classify galaxies based on only their redshifts and ultraviolet to near-infrared (UV-NIR) spectral energy distributions. Results. The FEM unsupervised algorithm has automatically distinguished 12 classes: 11 classes of VIPERS galaxies and an additional class of broad-line active galactic nuclei (AGNs). After a first broad division into blue, green, and red categories, we obtained a further sub-division into: three red, three green, and five blue galaxy classes. The FEM classes follow the galaxy sequence from the earliest to the latest types, which is reflected in their colours (which are constructed from rest-frame magnitudes used in the classification procedure) but also their morphological, physical, and spectroscopic properties (not included in the classification scheme). We demonstrate that the members of each class share similar physical and spectral properties. In particular, we are able to find three different classes of red passive galaxy populations. Thus, we demonstrate the potential of an unsupervised approach to galaxy classification and we retrieve the complexity of galaxy populations at z ∼ 0.7, a task that usual, simpler, colour-based approaches cannot fulfil.

Context. The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS, have been recently cross-matched to construct a novel photometric redshift catalogue on 70% of the sky. Galaxies were separated from stars and quasars through colour cuts, which may leave imperfections because different source types may overlap in colour space. Aims. The aim of the present work is to identify galaxies in the WISE × SuperCOSMOS catalogue through an alternative approach of machine learning. This allows us to define more complex separations in the multi-colour space than is possible with simple colour cuts, and should provide a more reliable source classification. Methods. For the automatised classification we used the support vector machines (SVM) learning algorithm and employed SDSS spectroscopic sources that we cross-matched with WISE × SuperCOSMOS to construct the training and verification set. We performed a number of tests to examine the behaviour of the classifier (completeness, purity, and accuracy) as a function of source apparent magnitude and Galactic latitude. We then applied the classifier to the full-sky data and analysed the resulting catalogue of candidate galaxies. We also compared the resulting dataset with the one obtained through colour cuts. Results. The tests indicate very high accuracy, completeness, and purity (>95%) of the classifier at the bright end; this deteriorates for the faintest sources, but still retains acceptable levels of ∼85%. No significant variation in the classification quality with Galactic latitude is observed. When we applied the classifier to all-sky WISE × SuperCOSMOS data, we found 15 million galaxies after masking problematic areas. The resulting sample is purer than the one produced by applying colour cuts, at the price of a lower completeness across the sky. Conclusions. The automatic classification is a successful alternative approach to colour cuts for defining a reliable galaxy sample. The identifications we obtained are included in the public release of the WISE × SuperCOSMOS galaxy catalogue.

Post-Human Genome Project progress has enabled a new wave of population genetic research, and intensified controversy over the use of race/ethnicity in this work. At the same time, the development of methods for inferring genetic ancestry offers more empirical means of assigning group labels. Here, we provide a systematic analysis of the use of race/ethnicity and ancestry in current genetic research. We base our analysis on key published recommendations for the use and reporting of race/ethnicity which advise that researchers: explain why the terms/categories were used and how they were measured, carefully define them, and apply them consistently. We studied 170 population genetic research articles from high impact journals, published 2008–2009. A comparative perspective was obtained by aligning study metrics with similar research from articles published 2001–2004. Our analysis indicates a marked improvement in compliance with some of the recommendations/guidelines for the use of race/ethnicity over time, while showing that important shortfalls still remain: no article using ‘race’, ‘ethnicity’ or ‘ancestry’ defined or discussed the meaning of these concepts in context; a third of articles still do not provide a rationale for their use, with those using ‘ancestry’ being the least likely to do so. Further, no article discussed potential socio-ethical implications of the reported research. As such, there remains a clear imperative for highlighting the importance of consistent and comprehensive reporting on human populations to the genetics/genomics community globally, to generate explicit guidelines for the uses of ancestry and genetic ancestry, and importantly, to ensure that guidelines are followed.

POLAR, a joint European-Chinese experiment, is a novel compact space-borne Compton polarimeter conceived and optimized for detection of the prompt emission of Gamma-Ray Bursts (GRB) and precise measurements of polarization in the hard X-ray energy range 50-500 keV. The complete instrument consists of two parts: internal one, placed inside spacelab and the detector itself, placed outside spacelab, called respectively IBOX and OBOX. The OBOX constitutes of 25 frontend electronic modules (FEE), high voltage and low voltage power supplies and the Central Task Processing Unit. The main functions of Central Task Processing Unit system are defined as follows: communication and transfer of data to IBOX, communication with all frontends, analysis of trigger signals and generation of global trigger signals, data acquisition, synchronizing of all frontends and control of power supplies. The functional requirements are fulfilled by three individual FPGA chips named respectively to their functions: Concentrator, Trigger and CPU. This article presents description of the Central Task Processing Unit hardware design and brief introduction to main components of the firmware developed for this device. Ongoing integration activities of the device with the complete POLAR instrument proved that all basic functions are working correctly. The qualification model of the instrument has been constructed and currently undergoes verification and validation tests in view of planned flight onboard the Chinese spacelab TG-2 scheduled for 2015