A Bayesian approach to star-galaxy classification

Star-galaxy classification is one of the most fundamental data-processing tasks in survey astronomy, and a critical starting point for the scientific exploitation of survey data. For bright sources this classification can be done with almost complete reliability, but for the numerous sources close to a survey's detection limit each image encodes only limited morphological information. In this regime, from which many of the new scientific discoveries are likely to come, it is vital to utilise all the available information about a source, both from multiple measurements and also prior knowledge about the star and galaxy populations. It is also more useful and realistic to provide classification probabilities than decisive classifications. All these desiderata can be met by adopting a Bayesian approach to star-galaxy classification, and we develop a very general formalism for doing so. An immediate implication of applying Bayes's theorem to this problem is that it is formally impossible to combine morphological measurements in different bands without using colour information as well; however we develop several approximations that disregard colour information as much as possible. The resultant scheme is applied to data from the UKIRT Infrared Deep Sky Survey (UKIDSS), and tested by comparing the results to deep Sloan Digital Sky Survey (SDSS) Stripe 82 measurements of the same sources. The Bayesian classification probabilities obtained from the UKIDSS data agree well with the deep SDSS classifications both overall (a mismatch rate of 0.022, compared to 0.044 for the UKIDSS pipeline classifier) and close to the UKIDSS detection limit (a mismatch rate of 0.068 compared to 0.075 for the UKIDSS pipeline classifier). The Bayesian formalism developed here can be applied to improve the reliability of any star-galaxy classification schemes based on the measured values of morphology statistics alone.Comment: Accepted 22 November 2010, 19 pages, 17 figure

No Confirmed New Isolated Neutron Stars In The SDSS Data Release 4

We report on follow-up observations of candidate X-ray bright, radio-quiet isolated neutron stars (INSs) identified from correlations of the ROSAT All-Sky Survey (RASS) and the Sloan Digital Sky Survey (SDSS) Data Release 4 in Ag\"ueros et al. (2006). We obtained Chandra X-ray Telescope exposures for 13 candidates in order to pinpoint the source of X-ray emission in optically blank RASS error circles. These observations eliminated 12 targets as good INS candidates. We discuss subsequent observations of the remaining candidate with the XMM-Newton X-ray Observatory, the Gemini North Observatory, and the Apache Point Observatory. We identify this object as a likely extragalactic source with an unusually high log(fX/fopt) ~ 2.4. We also use an updated version of the population synthesis models of Popov et al. (2010) to estimate the number of RASS-detected INSs in the SDSS Data Release 7 footprint. We find that these models predict ~3-4 INSs in the 11,000 square deg imaged by SDSS, which is consistent with the number of known INSs that fall within the survey footprint. In addition, our analysis of the four new INS candidates identified by Turner et al. (2010) in the SDSS footprint implies that they are unlikely to be confirmed as INSs; together, these results suggest that new INSs are not likely to be found from further correlations of the RASS and SDSS.Comment: 11 pages, 2 figures, 3 tables; accepted for publication in A

Selecting Quasars by their Intrinsic Variability

We present a new and simple technique for selecting extensive, complete and pure quasar samples, based on their intrinsic variability. We parametrize the single-band variability by a power-law model for the light-curve structure function, with amplitude A and power-law index gamma. We show that quasars can be efficiently separated from other non-variable and variable sources by the location of the individual sources in the A-gamma plane. We use ~60 epochs of imaging data, taken over ~5 years, from the SDSS stripe 82 (S82) survey, where extensive spectroscopy provides a reference sample of quasars, to demonstrate the power of variability as a quasar classifier in multi-epoch surveys. For UV-excess selected objects, variability performs just as well as the standard SDSS color selection, identifying quasars with a completeness of 90% and a purity of 95%. In the redshift range 2.5<z<3, where color selection is known to be problematic, variability can select quasars with a completeness of 90% and a purity of 96%. This is a factor of 5-10 times more pure than existing color-selection of quasars in this redshift range. Selecting objects from a broad griz color box without u-band information, variability selection in S82 can afford completeness and purity of 92%, despite a factor of 30 more contaminants than quasars in the color-selected feeder sample. This confirms that the fraction of quasars hidden in the 'stellar locus' of color-space is small. To test variability selection in the context of Pan-STARRS 1 (PS1) we created mock PS1 data by down-sampling the S82 data to just 6 epochs over 3 years. Even with this much sparser time sampling, variability is an encouragingly efficient classifier. For instance, a 92% pure and 44% complete quasar candidate sample is attainable from the above $griz$-selected catalog.Comment: 16 pages, 9 color figures and 5 tables - v3: Equations corrected and text updated (see Erratum for details of corrections). Erratum: http://adsabs.harvard.edu/abs/2010ApJ...721.1941S Original Paper: http://adsabs.harvard.edu/abs/2010ApJ...714.1194

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LSST Science Book, Version 2.0

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at http://www.lsst.org/lsst/sciboo

Gérer l’incertitude : les pratiques française, britannique et américaine en termes de technique et d’organisation pour le développement du moteur à réaction, 1944-1955

Introduction « Si les recherches sur le moteur à réaction se poursuivent encore longtemps à ce rythme, la possibilité d’exploiter les résultats sans trop de déperdition va se poser de manière aiguë ; à l’heure actuelle, il est clair qu’aucun expert ne peut lire l’intégralité de la littérature sur le sujet, et très souvent, des résultats assez importants obtenus dans un laboratoire ne sont pas connus des autres laboratoires, même lorsqu’ils ne sont pas très éloignés… De plus, le voile du secre..