SHELS: Testing Weak Lensing Maps with Redshift Surveys

Weak lensing surveys are emerging as an important tool for the construction of "mass selected" clusters of galaxies. We evaluate both the efficiency and completeness of a weak lensing selection by combining a dense, complete redshift survey, the Smithsonian Hectospec Lensing Survey (SHELS), with a weak lensing map from the Deep Lens Survey (DLS). SHELS includes 11,692 redshifts for galaxies with R < 20.6 in the four square degree DLS field; the survey is a solid basis for identifying massive clusters of galaxies with redshift z < 0.55. The range of sensitivity of the redshift survey is similar to the range for the DLS convergence map. Only four the twelve convergence peaks with signal-to-noise > 3.5 correspond to clusters of galaxies with M > 1.7 x 10^14 solar masses. Four of the eight massive clusters in SHELS are detected in the weak lensing map yielding a completeness of roughly 50%. We examine the seven known extended cluster x-ray sources in the DLS field: three can be detected in the weak lensing map, three should not be detected without boosting from superposed large-scale structure, and one is mysteriously undetected even though its optical properties suggest that it should produce a detectable lensing signal. Taken together, these results underscore the need for more extensive comparisons among different methods of massive cluster identification.Comment: 34 pages, 16 figures, ApJ accepte

Systematic and quantitative approach for the identification of high energy gamma-ray source populations

A large fraction of the detections to be made by the Gamma-ray Large Area Space Telescope (GLAST) will initially be unidentified. We argue that traditional methodological approaches to identify individuals and/or populations of $\gamma$-ray sources will encounter procedural limitations. These limitations will hamper our ability to classify source populations lying in the anticipated dataset with the required degree of confidence, particularly those for which no member has yet been convincingly detected in the predecessor experiment EGRET. Here we suggest a new paradigm for achieving the classification of $\gamma$-ray source populations based on the implementation of an a priori protocol to search for theoretically-motivated candidate sources. In order to protect the discovery potential of the sample, it is essential that such paradigm will be defined before the data is unblinded. Key to the new procedure is a statistical assessment by which the discovery of a new population can be claimed. Although we explicitly refer here to the case of GLAST, the scheme we present may be adapted to other experiments confronted with a similar problematic.Comment: In press in The Astrophysical Journal Letters. Accepted on July 12, 200

How to Host a Data Competition: Statistical Advice for Design and Analysis of a Data Competition

Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised learning competition is vulnerable to overfitting, where the winning solutions are so closely tuned to the particular set of provided data that they cannot generalize to the underlying problem of interest to the host. This paper outlines some important considerations for strategically designing relevant and informative data sets to maximize the learning outcome from hosting a competition based on our experience. It also describes a post-competition analysis that enables robust and efficient assessment of the strengths and weaknesses of solutions from different competitors, as well as greater understanding of the regions of the input space that are well-solved. The post-competition analysis, which complements the leaderboard, uses exploratory data analysis and generalized linear models (GLMs). The GLMs not only expand the range of results we can explore, they also provide more detailed analysis of individual sub-questions including similarities and differences between algorithms across different types of scenarios, universally easy or hard regions of the input space, and different learning objectives. When coupled with a strategically planned data generation approach, the methods provide richer and more informative summaries to enhance the interpretation of results beyond just the rankings on the leaderboard. The methods are illustrated with a recently completed competition to evaluate algorithms capable of detecting, identifying, and locating radioactive materials in an urban environment.Comment: 36 page

An Independent Review of USGS Circular 1370: An Evaluation of the Science Needs to Inform Decisions on Outer Continental Shelf Energy Development in the Chukchi and Beaufort Seas, Alaska

Reviews the U.S. Geological Survey's findings and recommendations on Alaska's Arctic Ocean, including geology, ecology and subsistence, effect of climate change on, and impact of oil spills. Makes recommendations for data management and other issues