Fossil evidence for spin alignment of SDSS galaxies in filaments

We search for and find fossil evidence that the distribution of the spin axes of galaxies in cosmic web filaments relative to their host filaments are not randomly distributed. This would indicate that the action of large scale tidal torques effected the alignments of galaxies located in cosmic filaments. To this end, we constructed a catalogue of clean filaments containing edge-on galaxies. We started by applying the Multiscale Morphology Filter (MMF) technique to the galaxies in a redshift-distortion corrected version of the Sloan Digital Sky Survey DR5. From that sample we extracted those 426 filaments that contained edge-on galaxies (b/a < 0.2). These filaments were then visually classified relative to a variety of quality criteria. Statistical analysis using "feature measures" indicates that the distribution of orientations of these edge-on galaxies relative to their parent filament deviate significantly from what would be expected on the basis of a random distribution of orientations. The interpretation of this result may not be immediately apparent, but it is easy to identify a population of 14 objects whose spin axes are aligned perpendicular to the spine of the parent filament (\cos \theta < 0.2). The candidate objects are found in relatively less dense filaments. This might be expected since galaxies in such locations suffer less interaction with surrounding galaxies, and consequently better preserve their tidally induced orientations relative to the parent filament. The technique of searching for fossil evidence of alignment yields relatively few candidate objects, but it does not suffer from the dilution effects inherent in correlation analysis of large samples.Comment: 20 pages, 19 figures, slightly revised and upgraded version, accepted for publication by MNRAS. For high-res version see http://www.astro.rug.nl/~weygaert/SpinAlignJones.rev.pd

The late merging phase of a galaxy cluster : XMM EPIC Observations of A3266

We present a mosaic of five XMM-Newton observations of the nearby ($z=0.0594$) merging galaxy cluster Abell 3266. We use the spectro-imaging capabilities of \xmm to build precise (projected) temperature, entropy, pressure and Fe abundance maps. The temperature map exhibits a curved, large-scale hot region, associated with elevated entropy levels, very similar to that foreseen in numerical simulations. The pressure distribution is disturbed in the central region but is remarkably regular on large scales. The Fe abundance map indicates that metals are inhomogeneously distributed across the cluster. Using simple physical calculations and comparison with numerical simulations, we discuss in detail merging scenarios that can reconcile the observed gas density, temperature and entropy structure, and the galaxy density distribution

Unveiling hidden structures in the Coma cluster

We have assembled a large data-set of 613 galaxy redshifts in the Coma cluster, the largest presently available for a cluster of galaxies. We have defined a sample of cluster members complete to b$_{26.5}=20.0$, using a membership criterion based on the galaxy velocity, when available, or on the galaxy magnitude and colour, otherwise. Such a data set allows us to define nearly complete samples within a region of 1~\Mpc\ radius, with a sufficient number of galaxies per sample to make statistical analyses possible. Using this sample and the {\em ROSAT} PSPC X--ray image of the cluster, we have re-analyzed the structure and kinematics of Coma, by applying the wavelet and adaptive kernel techniques. A striking coincidence of features is found in the distributions of galaxies and hot intracluster gas. The two central dominant galaxies, NGC4874 and NGC4889, are surrounded by two galaxy groups, mostly populated with galaxies brighter than b$_{26.5}=17$ and well separated in velocity space. On the contrary, the fainter galaxies tend to form a single smooth structure with a central peak coinciding in position with a secondary peak detected in X--rays, and located between the two dominant galaxies; we suggest to identify this structure with the main body of the Coma cluster. A continuous velocity gradient is found in the central distribution of these faint galaxies, a probable signature of tidal interactions rather than rotation. There is evidence for a bound population of bright galaxies around other brightest cluster members. Altogether, the Coma cluster structure seems to be better traced by the faint galaxy population, the bright galaxies being located in subclusters. We discuss this evidence in terms of an ongoing accretion of groups onto the cluster.Comment: to appear in A&A, 19 pages, uuencoded gzipped postscript fil

Grid Analysis of Radiological Data

IGI-Global Medical Information Science Discoveries Research Award 2009International audienceGrid technologies and infrastructures can contribute to harnessing the full power of computer-aided image analysis into clinical research and practice. Given the volume of data, the sensitivity of medical information, and the joint complexity of medical datasets and computations expected in clinical practice, the challenge is to fill the gap between the grid middleware and the requirements of clinical applications. This chapter reports on the goals, achievements and lessons learned from the AGIR (Grid Analysis of Radiological Data) project. AGIR addresses this challenge through a combined approach. On one hand, leveraging the grid middleware through core grid medical services (data management, responsiveness, compression, and workflows) targets the requirements of medical data processing applications. On the other hand, grid-enabling a panel of applications ranging from algorithmic research to clinical use cases both exploits and drives the development of the services

Wide Field Imaging. I. Applications of Neural Networks to object detection and star/galaxy classification

[Abriged] Astronomical Wide Field Imaging performed with new large format CCD detectors poses data reduction problems of unprecedented scale which are difficult to deal with traditional interactive tools. We present here NExt (Neural Extractor): a new Neural Network (NN) based package capable to detect objects and to perform both deblending and star/galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first discriminated from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold and then they are classified as stars or as galaxies through diagnostic diagrams having variables choosen accordingly to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NExt requires only the simplest a priori definition of "what an object is" (id est, it keeps all structures composed by more than one pixels) and performs the detection via an unsupervised NN approaching detection as a clustering problem which has been thoroughly studied in the artificial intelligence literature. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features, we use a NN to select the most significant features among the large number of measured ones, and then we use their selected features to perform the classification task. In order to optimise the performances of the system we implemented and tested several different models of NN. The comparison of the NExt performances with those of the best detection and classification package known to the authors (SExtractor) shows that NExt is at least as effective as the best traditional packages.Comment: MNRAS, in press. Paper with higher resolution images is available at http://www.na.astro.it/~andreon/listapub.htm