The Chandra XBootes Survey - III: Optical and Near-IR Counterparts

The XBootes Survey is a 5-ks Chandra survey of the Bootes Field of the NOAO Deep Wide-Field Survey (NDWFS). This survey is unique in that it is the largest (9.3 deg^2), contiguous region imaged in X-ray with complementary deep optical and near-IR observations. We present a catalog of the optical counterparts to the 3,213 X-ray point sources detected in the XBootes survey. Using a Bayesian identification scheme, we successfully identified optical counterparts for 98% of the X-ray point sources. The optical colors suggest that the optically detected galaxies are a combination of z<1 massive early-type galaxies and bluer star-forming galaxies whose optical AGN emission is faint or obscured, whereas the majority of the optically detected point sources are likely quasars over a large redshift range. Our large area, X-ray bright, optically deep survey enables us to select a large sub-sample of sources (773) with high X-ray to optical flux ratios (f_x/f_o>10). These objects are likely high redshift and/or dust obscured AGN. These sources have generally harder X-ray spectra than sources with 0.1<f_x/f_o<10. Of the 73 X-ray sources with no optical counterpart in the NDWFS catalog, 47 are truly optically blank down to R~25.5 (the average 50% completeness limit of the NDWFS R-band catalogs). These sources are also likely to be high redshift and/or dust obscured AGN.Comment: 19 pages, 13 figures, ApJ accepted. Catalog can be found at: http://www.noao.edu/noao/noaodeep or ftp://archive.noao.edu/pub/catalogs/xbootes

Optimization of star research algorithm for esmo star tracker

This paper explains in detail the design and the development of a software research star algorithm, embedded on a star tracker, by the ISAE/SUPAERO team. This research algorithm is inspired by musical techniques. This work will be carried out as part of the ESMO (European Student Moon Orbiter) project by different teams of students and professors from ISAE/SUPAERO (Institut Supe ́rieur de l’Ae ́ronautique et de l’Espace). Till today, the system engineering studies have been completed and the work that will be presented will concern the algorithmic and the embedded software development. The physical architecture of the sensor relies on APS 750 developed by the CIMI laboratory of ISAE/SUPAERO. First, a star research algorithm based on the image acquired in lost-in-space mode (one of the star tracker opera- tional modes) will be presented; it is inspired by techniques of musical recognition with the help of the correlation of digital signature (hash) with those stored in databases. The musical recognition principle is based on finger- printing, i.e. the extraction of points of interest in the studied signal. In the musical context, the signal spectrogram is used to identify these points. Applying this technique in image processing domain requires an equivalent tool to spectrogram. Those points of interest create a hash and are used to efficiently search within the database pre- viously sorted in order to be compared. The main goals of this research algorithm are to minimise the number of steps in the computations in order to deliver information at a higher frequency and to increase the computation robustness against the different possible disturbances

Efficient Photometric Selection of Quasars from the Sloan Digital Sky Survey: 100,000 z<3 Quasars from Data Release One

We present a catalog of 100,563 unresolved, UV-excess (UVX) quasar candidates to g=21 from 2099 deg^2 of the Sloan Digital Sky Survey (SDSS) Data Release One (DR1) imaging data. Existing spectra of 22,737 sources reveals that 22,191 (97.6%) are quasars; accounting for the magnitude dependence of this efficiency, we estimate that 95,502 (95.0%) of the objects in the catalog are quasars. Such a high efficiency is unprecedented in broad-band surveys of quasars. This ``proof-of-concept'' sample is designed to be maximally efficient, but still has 94.7% completeness to unresolved, g<~19.5, UVX quasars from the DR1 quasar catalog. This efficient and complete selection is the result of our application of a probability density type analysis to training sets that describe the 4-D color distribution of stars and spectroscopically confirmed quasars in the SDSS. Specifically, we use a non-parametric Bayesian classification, based on kernel density estimation, to parameterize the color distribution of astronomical sources -- allowing for fast and robust classification. We further supplement the catalog by providing photometric redshifts and matches to FIRST/VLA, ROSAT, and USNO-B sources. Future work needed to extend the this selection algorithm to larger redshifts, fainter magnitudes, and resolved sources is discussed. Finally, we examine some science applications of the catalog, particularly a tentative quasar number counts distribution covering the largest range in magnitude (14.2<g<21.0) ever made within the framework of a single quasar survey.Comment: 35 pages, 11 figures (3 color), 2 tables, accepted by ApJS; higher resolution paper and ASCII version of catalog available at http://sdss.ncsa.uiuc.edu/qso/nbckde

PROTEOFORMER: deep proteome coverage through ribosome profiling and MS integration

An increasing amount of studies integrate mRNA sequencing data into MS-based proteomics to complement the translation product search space. However, several factors, including extensive regulation of mRNA translation and the need for three- or six-frame-translation, impede the use of mRNA-seq data for the construction of a protein sequence search database. With that in mind, we developed the PROTEOFORMER tool that automatically processes data of the recently developed ribosome profiling method (sequencing of ribosome-protected mRNA fragments), resulting in genome-wide visualization of ribosome occupancy. Our tool also includes a translation initiation site calling algorithm allowing the delineation of the open reading frames (ORFs) of all translation products. A complete protein synthesis-based sequence database can thus be compiled for mass spectrometry-based identification. This approach increases the overall protein identification rates with 3% and 11% (improved and new identifications) for human and mouse, respectively, and enables proteome-wide detection of 5'-extended proteoforms, upstream ORF translation and near-cognate translation start sites. The PROTEOFORMER tool is available as a stand-alone pipeline and has been implemented in the galaxy framework for ease of use

Photometric Catalogue of Quasars and Other Point Sources in the Sloan Digital Sky Survey

We present a catalogue of about 6 million unresolved photometric detections in the Sloan Digital Sky Survey Seventh Data Release classifying them into stars, galaxies and quasars. We use a machine learning classifier trained on a subset of spectroscopically confirmed objects from 14th to 22nd magnitude in the SDSS {\it i}-band. Our catalogue consists of 2,430,625 quasars, 3,544,036 stars and 63,586 unresolved galaxies from 14th to 24th magnitude in the SDSS {\it i}-band. Our algorithm recovers 99.96% of spectroscopically confirmed quasars and 99.51% of stars to i $\sim$21.3 in the colour window that we study. The level of contamination due to data artefacts for objects beyond $i=21.3$ is highly uncertain and all mention of completeness and contamination in the paper are valid only for objects brighter than this magnitude. However, a comparison of the predicted number of quasars with the theoretical number counts shows reasonable agreement.Comment: 16 pages, Ref. No. MN-10-2382-MJ.R2, accepted for publication in MNRAS Main Journal, April 201

Photometric Selection of QSO Candidates From GALEX Sources

We present a catalog of 36,120 QSO candidates from the Galaxy Evolution Explorer (GALEX) Release Two (GR2) UV catalog and the USNO-A2.0 optical catalog. The selection criteria are established using known quasars from the Sloan Digital Sky Survey (SDSS). The SDSS sample is then used to assign individual probabilities to our GALEX-USNO candidates. The mean probability is ~50%, and would rise to ~65% if better morphological information than that from USNO were available to eliminate galaxies. The sample is ~40% complete for i<=19.1. Candidates are cross-identified in 2MASS, FIRST, SDSS, and XMM-Newton Slewing Survey (XMMSL1), whenever such counterparts exist. The present catalog covers the 8000 square degrees of GR2 lying above 25 degrees Galactic latitude, but can be extended to all 24,000 square degress that satisfy this criterion as new GALEX data become available.Comment: AASTeX v5.2, 31 pages, 9 figures. Accepted for publication in ApJ. Extended tables available in the online edition of the journa

Algorithms for autonomous star identification

Algorithms for onboard autonomous star identification are presented. The algorithms are applicable to two types of spacecraft missions, those flown with nearly inertially fixed attitude (solar maximum mission type); and those flown with smoothly time varying attitude (LANDSAT-D type)

Shape-based peak identification for ChIP-Seq

We present a new algorithm for the identification of bound regions from ChIP-seq experiments. Our method for identifying statistically significant peaks from read coverage is inspired by the notion of persistence in topological data analysis and provides a non-parametric approach that is robust to noise in experiments. Specifically, our method reduces the peak calling problem to the study of tree-based statistics derived from the data. We demonstrate the accuracy of our method on existing datasets, and we show that it can discover previously missed regions and can more clearly discriminate between multiple binding events. The software T-PIC (Tree shape Peak Identification for ChIP-Seq) is available at http://math.berkeley.edu/~vhower/tpic.htmlComment: 12 pages, 6 figure

SPIDERS: Selection of spectroscopic targets using AGN candidates detected in all-sky X-ray surveys

SPIDERS (SPectroscopic IDentification of eROSITA Sources) is an SDSS-IV survey running in parallel to the eBOSS cosmology project. SPIDERS will obtain optical spectroscopy for large numbers of X-ray-selected AGN and galaxy cluster members detected in wide area eROSITA, XMM-Newton and ROSAT surveys. We describe the methods used to choose spectroscopic targets for two sub-programmes of SPIDERS: X-ray selected AGN candidates detected in the ROSAT All Sky and the XMM-Newton Slew surveys. We have exploited a Bayesian cross-matching algorithm, guided by priors based on mid-IR colour-magnitude information from the WISE survey, to select the most probable optical counterpart to each X-ray detection. We empirically demonstrate the high fidelity of our counterpart selection method using a reference sample of bright well-localised X-ray sources collated from XMM-Newton, Chandra and Swift-XRT serendipitous catalogues, and also by examining blank-sky locations. We describe the down-selection steps which resulted in the final set of SPIDERS-AGN targets put forward for spectroscopy within the eBOSS/TDSS/SPIDERS survey, and present catalogues of these targets. We also present catalogues of ~12000 ROSAT and ~1500 XMM-Newton Slew survey sources which have existing optical spectroscopy from SDSS-DR12, including the results of our visual inspections. On completion of the SPIDERS program, we expect to have collected homogeneous spectroscopic redshift information over a footprint of ~7500 deg$^2$ for >85 percent of the ROSAT and XMM-Newton Slew survey sources having optical counterparts in the magnitude range 17<r<22.5, producing a large and highly complete sample of bright X-ray-selected AGN suitable for statistical studies of AGN evolution and clustering.Comment: MNRAS, accepte