Galaxy triplets in Sloan Digital Sky Survey Data Release 7-I. Catalogue

We present a new catalogue of galaxy triplets derived from the Sloan Digital Sky Survey (SDSS) Data Release 7. The identification of systems was performed considering galaxies brighter than Mr=-20.5 and imposing constraints over the projected distances, radial velocity differences of neighbouring galaxies and isolation. To improve the identification of triplets, we employed a data pixelization scheme, which allows us to handle large amounts of data as in the SDSS photometric survey. Using spectroscopic and photometric data in the redshift range 0.01 =z= 0.40, we obtain 5901 triplet candidates. We have used a mock catalogue to analyse the completeness and contamination of our methods. The results show a high level of completeness ( 80 per cent) and low contamination ( 5 per cent). By using photometric and spectroscopic data, we have also addressed the effects of fibre collisions in the spectroscopic sample. We have defined an isolation criterion considering the distance of the triplet brightest galaxy to the closest neighbour cluster, to describe a global environment, as well as the galaxies within a fixed aperture, around the triplet brightest galaxy, to measure the local environment. The final catalogue comprises 1092 isolated triplets of galaxies in the redshift range 0.01 =z= 0.40. Our results show that photometric redshifts provide very useful information, allowing us to complete the sample of nearby systems whose detection is affected by fibre collisions, as well as extending the detection of triplets to large distances, where spectroscopic redshifts are not available.

THE SLOAN DIGITAL SKY SURVEY CO-ADD: A GALAXY PHOTOMETRIC REDSHIFT CATALOG

We present and describe a catalog of galaxy photometric redshifts (photo-z) for the Sloan Digital Sky Survey (SDSS) Co-add Data. We use the artificial neural network (ANN) technique to calculate the photo-z and the nearest neighbor error method to estimate photo-z errors for similar to 13 million objects classified as galaxies in the co-add with r < 24.5. The photo-z and photo-z error estimators are trained and validated on a sample of similar to 83,000 galaxies that have SDSS photometry and spectroscopic redshifts measured by the SDSS Data Release 7 (DR7), the Canadian Network for Observational Cosmology Field Galaxy Survey, the Deep Extragalactic Evolutionary Probe Data Release 3, the VIsible imaging Multi-Object Spectrograph-Very Large Telescope Deep Survey, and the WiggleZ Dark Energy Survey. For the best ANN methods we have tried, we find that 68% of the galaxies in the validation set have a photo-z error smaller than sigma(68) = 0.031. After presenting our results and quality tests, we provide a short guide for users accessing the public data.

Simcluster: clustering enumeration gene expression data on the simplex space

Abstract Background Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space. Results Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster. Conclusion Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data.