Finding strong lenses in CFHTLS using convolutional neural networks

We train and apply convolutional neural networks, a machine learning technique developed to learn from and classify image data, to Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the identification of potential strong lensing systems. An ensemble of four convolutional neural networks was trained on images of simulated galaxy-galaxy lenses. The training sets consisted of a total of 62,406 simulated lenses and 64,673 non-lens negative examples generated with two different methodologies. The networks were able to learn the features of simulated lenses with accuracy of up to 99.8% and a purity and completeness of 94-100% on a test set of 2000 simulations. An ensemble of trained networks was applied to all of the 171 square degrees of the CFHTLS wide field image data, identifying 18,861 candidates including 63 known and 139 other potential lens candidates. A second search of 1.4 million early type galaxies selected from the survey catalog as potential deflectors, identified 2,465 candidates including 117 previously known lens candidates, 29 confirmed lenses/high-quality lens candidates, 266 novel probable or potential lenses and 2097 candidates we classify as false positives. For the catalog-based search we estimate a completeness of 21-28% with respect to detectable lenses and a purity of 15%, with a false-positive rate of 1 in 671 images tested. We predict a human astronomer reviewing candidates produced by the system would identify ~20 probable lenses and 100 possible lenses per hour in a sample selected by the robot. Convolutional neural networks are therefore a promising tool for use in the search for lenses in current and forthcoming surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope.Comment: 16 pages, 8 figures. Accepted by MNRA

Senior Honors Presentations Abstracts 2012

Foreword: The Thomas More Honors Program is extremely proud to showcase our graduating students’ talents through the abstracts on the following pages. The projects represented therein are the fruits of many hours of dedicated work on a capstone project or senior thesis required for the Honors minor. Some of our students have already presented their work at regional conferences, but most of them will be presenting their work in various forums here at Sacred Heart. We invite you to celebrate our students’ achievements personally by attending their presentations in the coming days. The abstracts are presented in alphabetical format, and the time and place of their presentations are indicated below the presentation title. Enjoy! - Suzanne M. Deschênes, Ph.D. Director, Thomas More Honors Progra

Be Still My Heart: Determinants of Support for Capital Punishment Attitudes

The following research attempts to determine the factors used by an individual to develop an attitude on the political issue of capital punishment. Using data from the 2000 National Election Study and ordered probit analysis, this research produces a multivariate, multi-stage model of death penalty attitudes. Demographic factors such as race, age, gender, and education level are included in the initial stage of the model. Attitudinal variables such as party identification, ideology, and religiosity are added, one-by-one, in the second stage of the model to determine their own individual effect on death penalty attitudes, and their effect on the preceding demographic variables. The result is a comprehensive model of death penalty attitudes

St. Thomas More Catholic Parish Cookbook

Beverages & appetizers -- Cakes & desserts -- Breads & rolls -- Main dishes, soups & salads -- Bars, cookies & candies -- Miscellaneoushttps://openprairie.sdstate.edu/sd_cookbooks/1033/thumbnail.jp

St. Thomas More High Tea Recipe Book

Savories -- Sweets.https://openprairie.sdstate.edu/sd_cookbooks/1032/thumbnail.jp