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.

Evaluating the Impact of Charter Schools on Student Achievement: A Longitudinal Look at the Great Lakes States

This study looks at student achievement in math and reading in charter and traditional public schools over a five-year period in Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin. The primary finding is that student achievement in charter schools in these six states is lower than in traditional public schools. The study also finds, however, that student achievement in charter schools is improving over time

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

Examining Rate Priming on Information Processing

The current study investigated the effect of a musical prime on reading rate, reading comprehension, and processing speed. This research also examined if there is a relation between reading speed and reading comprehension. Music and language primes have been shown to affect processing speed similarly, such that when participants were exposed to a slow prime, language production would slow down, and vice versa for fast primes (Jungers, Hupp, & Dickerson, 2016). This result has also been found in other cognitive capacities when participants are exposed to a prime, such as decision-making (Buelow, Hupp, Porter & Coleman, 2016), suggesting that the rate of prime could change processing speed across domains. The current study was looking to further support this theory by testing for processing speed in motor movements and reading rate. Participants completed the Purdue Pegboard Task and The Nelson Denny Reading Test after being exposed to 3 minutes of a classical music prime. The musical prime was manipulated to have both slow and fast tempos. The current study shows that there is a positive correlation between reading rate and reading comprehension, but the rate of prime did not affect processing speed, reading rate, or reading comprehension.No embargoAcademic Major: Psycholog

A Package for the Automated Classification of Periodic Variable Stars

We present a machine learning package for the classification of periodic variable stars. Our package is intended to be general: it can classify any single band optical light curve comprising at least a few tens of observations covering durations from weeks to years, with arbitrary time sampling. We use light curves of periodic variable stars taken from OGLE and EROS-2 to train the model. To make our classifier relatively survey-independent, it is trained on 16 features extracted from the light curves (e.g. period, skewness, Fourier amplitude ratio). The model classifies light curves into one of seven superclasses - Delta Scuti, RR Lyrae, Cepheid, Type II Cepheid, eclipsing binary, long-period variable, non-variable - as well as subclasses of these, such as ab, c, d, and e types for RR Lyraes. When trained to give only superclasses, our model achieves 0.98 for both recall and precision as measured on an independent validation dataset (on a scale of 0 to 1). When trained to give subclasses, it achieves 0.81 for both recall and precision. In order to assess classification performance of the subclass model, we applied it to the MACHO, LINEAR, and ASAS periodic variables, which gave recall/precision of 0.92/0.98, 0.89/0.96, and 0.84/0.88, respectively. We also applied the subclass model to Hipparcos periodic variable stars of many other variability types that do not exist in our training set, in order to examine how much those types degrade the classification performance of our target classes. In addition, we investigate how the performance varies with the number of data points and duration of observations. We find that recall and precision do not vary significantly if the number of data points is larger than 80 and the duration is more than a few weeks. The classifier software of the subclass model is available from the GitHub repository (https://goo.gl/xmFO6Q).Comment: 16 pages, 11 figures, accepted for publication in A&

Achieving a wide field near infrared camera for the Calar Alto 3.5m telescope

The ongoing development of large infrared array detectors has enabled wide field, deep surveys to be undertaken. There are, however, a number of challenges in building an infrared instrument which has both excellent optical quality and high sensitivity over a wide field. We discuss these problems in the context of building a wide field imaging camera for the 3.5m telescope at Calar Alto with the new 2K*2K HgCdTe HAWAII-2 focal plane array. Our final design is a prime focus camera with a 15' field-of-view, called Omega 2000. To achieve excellent optical quality over the whole field, we have had to dispense with the reimaging optics and cold Lyot stop. We show that creative baffling schemes, including the use of undersized baffles, can compensate for the lost K band sensitivity. A moving baffle will be employed in Omega 2000 to allow full transmission in the non-thermal J and H bands.Comment: To appear in the SPIE proceedings of ``Optical and IR Telescope Instrumentation and Detectors'', Munich, March 200

Assessment of stochastic and deterministic models of 6304 quasar lightcurves from SDSS Stripe 82

The optical light curves of many quasars show variations of tenths of a magnitude or more on time scales of months to years. This variation often cannot be described well by a simple deterministic model. We perform a Bayesian comparison of over 20 deterministic and stochastic models on 6304 QSO light curves in SDSS Stripe 82. We include the damped random walk (or Ornstein-Uhlenbeck [OU] process), a particular type of stochastic model which recent studies have focused on. Further models we consider are single and double sinusoids, multiple OU processes, higher order continuous autoregressive processes, and composite models. We find that only 29 out of 6304 QSO lightcurves are described significantly better by a deterministic model than a stochastic one. The OU process is an adequate description of the vast majority of cases (6023). Indeed, the OU process is the best single model for 3462 light curves, with the composite OU process/sinusoid model being the best in 1706 cases. The latter model is the dominant one for brighter/bluer QSOs. Furthermore, a non-negligible fraction of QSO lightcurves show evidence that not only the mean is stochastic but the variance is stochastic, too. Our results confirm earlier work that QSO light curves can be described with a stochastic model, but place this on a firmer footing, and further show that the OU process is preferred over several other stochastic and deterministic models. Of course, there may well exist yet better (deterministic or stochastic) models which have not been considered here.Comment: accepted by AA, 12 pages, 11 figures, 4 table

Automated Classification of Stellar Spectra. II: Two-Dimensional Classification with Neural Networks and Principal Components Analysis

We investigate the application of neural networks to the automation of MK spectral classification. The data set for this project consists of a set of over 5000 optical (3800-5200 AA) spectra obtained from objective prism plates from the Michigan Spectral Survey. These spectra, along with their two-dimensional MK classifications listed in the Michigan Henry Draper Catalogue, were used to develop supervised neural network classifiers. We show that neural networks can give accurate spectral type classifications (sig_68 = 0.82 subtypes, sig_rms = 1.09 subtypes) across the full range of spectral types present in the data set (B2-M7). We show also that the networks yield correct luminosity classes for over 95% of both dwarfs and giants with a high degree of confidence. Stellar spectra generally contain a large amount of redundant information. We investigate the application of Principal Components Analysis (PCA) to the optimal compression of spectra. We show that PCA can compress the spectra by a factor of over 30 while retaining essentially all of the useful information in the data set. Furthermore, it is shown that this compression optimally removes noise and can be used to identify unusual spectra.Comment: To appear in MNRAS. 15 pages, 17 figures, 7 tables. 2 large figures (nos. 4 and 15) are supplied as separate GIF files. The complete paper can be obtained as a single gziped PS file from http://wol.ra.phy.cam.ac.uk/calj/p1.htm