53 research outputs found
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 -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
Opinions: Business History and Anthropology
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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..
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