We carry out two searches for periodic gravitational waves using the most sensitive few hours of data from the second LIGO science run. Both searches exploit fully coherent matched filtering and cover wide areas of parameter space, an innovation over previous analyses which requires considerable algorithm development and computational power. The first search is targeted at isolated, previously unknown neutron stars, covers the entire sky in the frequency band 160 -728.8 Hz, and assumes a frequency derivative of less than 4 10 ÿ10 Hz=s. The second search targets the accreting neutron star in the lowmass x-ray binary Scorpius X-1 and covers the frequency bands 464-484 Hz and 604-624 Hz as well as the two relevant binary orbit parameters. Because of the high computational cost of these searches we limit the analyses to the most sensitive 10 hours and 6 hours of data, respectively. Given the limited sensitivity and duration of the analyzed data set, we do not attempt deep follow-up studies. Rather we concentrate on demonstrating the data analysis method on a real data set and present our results as upper limits over large volumes of the parameter space. In order to achieve this, we look for coincidences in parameter space between the Livingston and Hanford 4-km interferometers. For isolated neutron stars our 95% confidence level upper limits on the gravitational wave strain amplitude range from 6:6 10 ÿ23 to 1 10 ÿ21 across the frequency band; for Scorpius X-1 they range from 1:7 10 ÿ22 to 1:3 10 ÿ21 across the two 20-Hz frequency bands. The upper limits presented in this paper are the first broadband wide parameter space upper limits on periodic gravitational waves from coherent search techniques. The methods developed here lay the foundations for upcoming hierarchical searches of more sensitive data which may detect astrophysical signals.

We present spectroscopy of binary quasar candidates selected from Data Release 4 of the Sloan Digital Sky Survey (SDSS DR4) using Kernel Density Estimation (KDE). We present 27 new sets of observations, 10 of which are binary quasars, roughly doubling the number of known g < 21 binaries with component separations of 3 ′′ ≤ ∆θ < 6 ′′ . Only 3 of 49 spectroscopically identified objects are non-quasars, confirming that the quasar selection efficiency of the KDE technique is ∼ 95%. Several of our observed binaries are wide-separation lens candidates that merit additional higher-resolution observations. One interesting pair may be an M star binary, or an M star-binary quasar superposition. Our candidates are initially selected by UV-excess (u − g < 1), but are otherwise selected irrespective of the relative colors of the quasar pair, and we thus use them to suggest optimal color similarity and photometric redshift approaches for targeting binary quasars, or projected quasar pairs. From a sample that is complete on proper scales of 23.7 < R prop < 29.7 h −1 kpc, we determine the projected quasar correlation function to be W p = 24.0± 16.9 10.8 , which is 2σ lower than recent estimates. We argue that our low W p estimates may indicate redshift evolution in the quasar correlation function from z ∼ 1.9 to z ∼ 1.4 on scales of R prop ∼ 25 h −1 kpc. The size of this evolution broadly tracks quasar clustering on larger scales, consistent with merger-driven models of quasar origin. Although our sample alone is insufficient to detect evolution in quasar clustering on small scales, an i-selected DR6 KDE quasar catalog, which will contain several hundred z ∼ < 5 binary quasars, could easily constrain any clustering evolution at R prop ∼ 25 h −1 kpc.

Problem solving is a critical element of learning physics. However, traditional instruction often emphasizes the quantitative aspects of problem solving such as equations and mathematical procedures rather than qualitative analysis for selecting appropriate concepts and principles. This study describes the development and evaluation of an instructional approach called Conceptual Problem Solving (CPS) which guides students to identify principles, justify their use, and plan their solution in writing before solving a problem. The CPS approach was implemented by high school physics teachers at three schools for major theorems and conservation laws in mechanics and CPS-taught classes were compared to control classes taught using traditional problem solving methods. Information about the teachers' implementation of the approach was gathered from classroom observations and interviews, and the effectiveness of the approach was evaluated from a series of written assessments. Results indicated that teachers found CPS easy to integrate into their curricula, students engaged in classroom discussions and produced problem solutions of a higher quality than before, and students scored higher on conceptual and problem solving measures.

We apply machine learning in the form of a nearest neighbor instance-based algorithm (NN ) to generate full photometric redshift probability density functions (PDFs) for objects in the Fifth Data Release of the Sloan Digital Sky Survey (SDSS DR5). We use a conceptually simple but novel application of NN to generate the PDFs, perturbing the object colors by their measurement error and using the resulting instances of nearest neighbor distributions to generate numerous individual redshifts. When the redshifts are compared to existing SDSS spectroscopic data, we find that the mean value of each PDF has a dispersion between the photometric and spectroscopic redshift consistent with other machine learning techniques, being ¼ 0:0207 AE 0:0001 for main sample galaxies to r < 17:77 mag, ¼ 0:0243 AE 0:0002 for luminous red galaxies to r P19:2 mag, and ¼ 0:343 AE 0:005 for quasars to i < 20:3 mag. The PDFs allow the selection of subsets with improved statistics. For quasars, the improvement is dramatic: for those with a single peak in their probability distribution, the dispersion is reduced from 0.343 to ¼ 0:117 AE 0:010, and the photometric redshift is within 0.3 of the spectroscopic redshift for 99:3% AE 0:1% of the objects. Thus, for this optical quasar sample, we can virtually eliminate ''catastrophic'' photometric redshift estimates. In addition to the SDSS sample, we incorporate ultraviolet photometry from the Third Data Release of the Galaxy Evolution Explorer All-Sky Imaging Survey (GALEX AIS GR3) to create PDFs for objects seen in both surveys. For quasars, the increased coverage of the observed-frame UV of the SED results in significant improvement over the full SDSS sample, with ¼ 0:234 AE 0:010. We demonstrate that this improvement is genuine and not an artifact of the SDSS-GALEX matching process.

We apply instance-based machine learning in the form of a k-nearest neighbor algorithm to the task of estimating photometric redshifts for 55,746 objects spectroscopically classified as quasars in the Fifth Data Release of the Sloan Digital Sky Survey. We compare the results obtained to those from an empirical color-redshift relation (CZR). In contrast to previously published results using CZRs, we find that the instance-based photometric redshifts are assigned with no regions of catastrophic failure. Remaining outliers are simply scattered about the ideal relation, in a similar manner to the pattern seen in the optical for normal galaxies at redshifts z 1. The instance-based algorithm is trained on a representative sample of the data and pseudo-blind-tested on the remaining unseen data. The variance between the photometric and spectroscopic redshifts is σ 2 = 0.123 ± 0.002 (compared to σ 2 = 0.265 ± 0.006 for the CZR), and 54.9 ± 0.7%, 73.3 ± 0.6%, and 80.7 ± 0.3% of the objects are within ∆z < 0.1, 0.2, and 0.3 respectively. We also match our sample to the Second Data Release of the Galaxy Evolution Explorer legacy data and the resulting 7,642 objects show a further improvement, giving a variance of σ 2 = 0.054 ± 0.005, and 70.8 ± 1.2%, 85.8 ± 1.0%, and 90.8 ± 0.7% of objects within ∆z < 0.1, 0.2, and 0.3. We show that the improvement is indeed due to the extra information provided by GALEX, by training on the same dataset using purely SDSS photometry, which has a variance of σ 2 = 0.090 ± 0.007. Each set of results represents a realistic standard for application to further datasets for which the spectra are representative.