Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

The spatial variation of the colour of a galaxy may introduce a bias in the measurement of its shape if the point spread function (PSF) profile depends on wavelength. We study how this bias depends on the properties of the PSF and the galaxies themselves. The bias depends on the scales used to estimate the shape, which may be used to optimize methods to reduce the bias. Here, we develop a general approach to quantify the bias. Although applicable to any weak lensing survey, we focus on the implications for the ESA Euclid mission. Based on our study of synthetic galaxies, we find that the bias is a few times 10-3 for a typical galaxy observed by Euclid. Consequently, it cannot be neglected and needs to be accounted for. We demonstrate how one can do so using spatially resolved observations of galaxies in two filters. We show that Hubble Space Telescope (HST) observations in the F606W and F814W filters allow us to model and reduce the bias by an order of magnitude, sufficient to meet Euclid's scientific requirements. The precision of the correction is ultimately determined by the number of galaxies for which spatially resolved observations in at least two filters are available. We use results from the Millennium simulation to demonstrate that archival HST data will be sufficient for the tomographic cosmic shear analysis with the Euclid data set. © 2013 The Author. Published by Oxford University Press on behalf of the Royal Astronomical Society.

Abstract:

Background: Validated methods of objective assessments of surgical skills are resource intensive. We sought to test a web-based grading tool using crowdsourcing called Crowd-Sourced Assessment of Technical Skill. Materials and methods: Institutional Review Board approval was granted to test the accuracy of Amazon.com's Mechanical Turk and Facebook crowdworkers compared with experienced surgical faculty grading a recorded dry-laboratory robotic surgical suturing performance using three performance domains from a validated assessment tool. Assessor free-text comments describing their rating rationale were used to explore a relationship between the language used by the crowd and grading accuracy. Results: Of a total possible global performance score of 3-15, 10 experienced surgeons graded the suturing video at a mean score of 12.11 (95% confidence interval [CI], 11.11-13.11). Mechanical Turk and Facebook graders rated the video at mean scores of 12.21 (95% CI, 11.98-12.43) and 12.06 (95% CI, 11.57-12.55), respectively. It took 24 h to obtain responses from 501 Mechanical Turk subjects, whereas it took 24 d for 10 faculty surgeons to complete the 3-min survey. Facebook subjects (110) responded within 25 d. Language analysis indicated that crowdworkers who used negation words (i.e., "but," "although," and so forth) scored the performance more equivalently to experienced surgeons than crowdworkers who did not (P < 0.00001). Conclusions: For a robotic suturing performance, we have shown that surgery-naive crowdworkers can rapidly assess skill equivalent to experienced faculty surgeons using Crowd-Sourced Assessment of Technical Skill. It remains to be seen whether crowds can discriminate different levels of skill and can accurately assess human surgery performances. © 2013 Elsevier Inc. All rights reserved.