34 research outputs found
Wide Area X-ray Surveys for AGN and Starburst Galaxies
While often the point sources in X-ray surveys are dominated by AGN, with the
high sensitivity of modern X-ray telescopes such as Chandra and XMM-Newton
normal/starburst galaxies are also being detected in large numbers. We have
made use of Bayesian statistics for both the selection of galaxies from deep
X-ray surveys and in the analysis of the luminosity functions for galaxies.
These techniques can be used to similarly select galaxies from wide-area X-ray
surveys and to analyze their luminosity function. The prospects for detecting
galaxies and AGN from a proposed ``wide-deep'' XMM-Newton survey and from
future wide-area X-ray survey missions (such as WFXT and eRosita) are also
discussed.Comment: 7 pages, 5 figures. Conference proceedings in "Classification and
Discovery in Large Astronomical Surveys", 2008, C.A.L. Bailer-Jones (ed.
Self-Organizing Maps. An application to the OGLE data and the Gaia Science Alerts
Self-Organizing Map (SOM) is a promising tool for exploring large
multi-dimensional data sets. It is quick and convenient to train in an
unsupervised fashion and, as an outcome, it produces natural clusters of data
patterns. An example of application of SOM to the new OGLE-III data set is
presented along with some preliminary results.
Once tested on OGLE data, the SOM technique will also be implemented within
the Gaia mission's photometry and spectrometry analysis, in particular, in
so-called classification-based Science Alerts. SOM will be used as a basis of
this system as the changes in brightness and spectral behaviour of a star can
be easily and quickly traced on a map trained in advance with simulated and/or
real data from other surveys.Comment: Presented as a poster at the "Classification and Discovery in Large
Astronomical Surveys" meeting, Ringberg Castle, 14-17 October, 200
A Review of Automated Stellar Spectral Classification and Surveys
Modern spectroscopic surveys and automated classifiers are becoming so inextricably linked that it is difficult even to summarize one without discussing the other. Some of the automated classifiers are being built because of current analysis needs, though with a clear anticipation of future, larger surveys. Other automated classifiers are being designed specifically for future surveys. Automated classifiers may be applied to databases already in hand, to real-time analysis at the telescope, or one day to on-board satellite analysis where the raw data are too bulky to save and transmit. In addition, many current spectroscopic surveys target galaxies. These surveys may contain stars either by accident or by a purposeful, but minority, assignment of input slits or fibers to stars. Nonetheless, these surveys still represent vast sources of stellar spectral data. Our review embarks by discussing current work, both on automated stellar classification and surveys, and then finishes with plans and portents for the future
Learning about Galactic structure with Gaia astrometry
The Gaia mission is reviewed together with the expected contents of the final
catalogue. It is then argued that the ultimate goal of Galactic structure
studies with Gaia astrometry should be to build a dynamical model of our galaxy
which is capable of explaining the contents of the Gaia catalogue. This will be
possible only by comparing predicted catalogue data to Gaia's actual
measurements. To complement this approach the Gaia catalogue should be used to
recalibrate photometric distance and abundance indicators across the HR-diagram
in order to overcome the lack of precise parallax data at the faint end of the
astrometric survey. Using complementary photometric and spectroscopic data from
other surveys will be essential in this respect.Comment: Presented at the "Classification and Discovery in Large Astronomical
Surveys" meeting, Ringberg Castle, 14-17 October, 200
Parameter Estimation from an Optimal Projection in a Local Environment
The parameter fit from a model grid is limited by our capability to reduce
the number of models, taking into account the number of parameters and the non
linear variation of the models with the parameters. The Local MultiLinear
Regression (LMLR) algorithms allow one to fit linearly the data in a local
environment. The MATISSE algorithm, developed in the context of the estimation
of stellar parameters from the Gaia RVS spectra, is connected to this class of
estimators. A two-steps procedure was introduced. A raw parameter estimation is
first done in order to localize the parameter environment. The parameters are
then estimated by projection on specific vectors computed for an optimal
estimation. The MATISSE method is compared to the estimation using the
objective analysis. In this framework, the kernel choice plays an important
role. The environment needed for the parameter estimation can result from it.
The determination of a first parameter set can be also avoided for this
analysis. These procedures based on a local projection can be fruitfully
applied to non linear parameter estimation if the number of data sets to be
fitted is greater than the number of models
Astronomical imaging: The theory of everything
We are developing automated systems to provide homogeneous calibration
meta-data for heterogeneous imaging data, using the pixel content of the image
alone where necessary. Standardized and complete calibration meta-data permit
generative modeling: A good model of the sky through wavelength and time--that
is, a model of the positions, motions, spectra, and variability of all stellar
sources, plus an intensity map of all cosmological sources--could synthesize or
generate any astronomical image ever taken at any time with any equipment in
any configuration. We argue that the best-fit or highest likelihood model of
the data is also the best possible astronomical catalog constructed from those
data. A generative model or catalog of this form is the best possible platform
for automated discovery, because it is capable of identifying informative
failures of the model in new data at the pixel level, or as statistical
anomalies in the joint distribution of residuals from many images. It is also,
in some sense, an astronomer's "theory of everything".Comment: a talk given at "Classification and Discovery in Large Astronomical
Surveys", Ringberg Castle, 2008-10-1
Time Variability of Quasars: the Structure Function Variance
Significant progress in the description of quasar variability has been
recently made by employing SDSS and POSS data. Common to most studies is a
fundamental assumption that photometric observations at two epochs for a large
number of quasars will reveal the same statistical properties as well-sampled
light curves for individual objects. We critically test this assumption using
light curves for a sample of 2,600 spectroscopically confirmed quasars
observed about 50 times on average over 8 years by the SDSS stripe 82 survey.
We find that the dependence of the mean structure function computed for
individual quasars on luminosity, rest-frame wavelength and time is
qualitatively and quantitatively similar to the behavior of the structure
function derived from two-epoch observations of a much larger sample. We also
reproduce the result that the variability properties of radio and X-ray
selected subsamples are different. However, the scatter of the variability
structure function for fixed values of luminosity, rest-frame wavelength and
time is similar to the scatter induced by the variance of these quantities in
the analyzed sample. Hence, our results suggest that, although the statistical
properties of quasar variability inferred using two-epoch data capture some
underlying physics, there is significant additional information that can be
extracted from well-sampled light curves for individual objects.Comment: Presented at the "Classification and Discovery in Large Astronomical
Surveys" meeting, Ringberg Castle, 14-17 October, 200
Broad Absorption Line Quasar catalogues with Supervised Neural Networks
We have applied a Learning Vector Quantization (LVQ) algorithm to SDSS DR5
quasar spectra in order to create a large catalogue of broad absorption line
quasars (BALQSOs). We first discuss the problems with BALQSO catalogues
constructed using the conventional balnicity and/or absorption indices (BI and
AI), and then describe the supervised LVQ network we have trained to recognise
BALQSOs. The resulting BALQSO catalogue should be substantially more robust and
complete than BI- or AI-based ones.Comment: 5 pages, 3 figures, to appear in the proceedings of "Classification
and Discovery in Large Astronomical Surveys", Ringberg Castle, 14-17 October
200
The LSST Data Mining Research Agenda
We describe features of the LSST science database that are amenable to
scientific data mining, object classification, outlier identification, anomaly
detection, image quality assurance, and survey science validation. The data
mining research agenda includes: scalability (at petabytes scales) of existing
machine learning and data mining algorithms; development of grid-enabled
parallel data mining algorithms; designing a robust system for brokering
classifications from the LSST event pipeline (which may produce 10,000 or more
event alerts per night); multi-resolution methods for exploration of petascale
databases; indexing of multi-attribute multi-dimensional astronomical databases
(beyond spatial indexing) for rapid querying of petabyte databases; and more.Comment: 5 pages, Presented at the "Classification and Discovery in Large
Astronomical Surveys" meeting, Ringberg Castle, 14-17 October, 200