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

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

Erosion and tourism infrastructure in the coastal zone: Problems, consequences and management

The importance of coastal zones to the tourism industry and the need to protect such resources is not only vital to the economy of nations but presents a growing dilemma for many localities and regions. Beaches have become synonymous with tourism and with current predictions of climate change and sea level rise; they are under significant threat of erosion worldwide. From an assessment of the effects of erosion, including evaluation of impacts on coastal destinations and tourism development, the consequences for global tourism business are projected. An analysis of hard and soft engineering responses showed that coastal protection measures should be linked to physical processes whilst management strategies included a case study proposal for beach nourishment, in response to the erosion of a tourist beach. Integrated Coastal Zone Management is justified as a tool for managing coastal resources and accommodating increasing pressures from tourism whilst strategies are recommended to ameliorate projected impacts

Do people's first names match their faces?

We often feel that people’s first names suit their faces in some way. Evidence has already shown that we share common stereotypes about how people with particular names should look. Here, we investigate whether there is any accuracy to these beliefs. Simply, can we match people’s names to their faces? Across two experiments, we tested whether American (Experiment 1) and British participants (Experiment 2) were able to match the first names of strangers with photographs of their faces. Although Experiment 1 provided some initial support for accuracy in female participants, we were unable to replicate this result in Experiment 2. Therefore, we find no overall evidence to suggest that particular names and faces are associated with each other

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 timing and precision of action prediction in the aging brain

Action Prediction in the Aging Brai

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

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 $\sim$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