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A unified analysis of four cosmic shear surveys
Chang, Chihway,Wang, Michael,Dodelson, Scott,Eifler, Tim,Heymans, Catherine,Jarvis, Michael,Jee, M James,Joudaki, Shahab,Krause, Elisabeth,Malz, Alex,Mandelbaum, Rachel,Mohammed, Irshad,Schneider, Mic Oxford University Press 2019 Monthly notices of the Royal Astronomical Society Vol.482 No.3
Industry 4.0 - A challenge for variation simulation tools for mechanical assemblies
Boorla, Srinivasa M.,Bjarklev, Kristian,Eifler, Tobias,Howard, Thomas J.,McMahon, Christopher A. Techno-Press 2019 Advances in computational design Vol.4 No.1
Variation Analysis (VA) is used to simulate final product variation, taking into consideration part manufacturing and assembly variations. In VA, all the manufacturing and assembly processes are defined at the product design stage. Process Capability Data Bases (PCDB) provide information about measured variation from previous products and processes and allow the designer to apply this to the new product. A new challenge to this traditional approach is posed by the Industry 4.0 (I4.0) revolution, where Smart Manufacturing (SM) is applied. The manufacturing intelligence and adaptability characteristics of SM make present PCDBs obsolete. Current tolerance analysis methods, which are made for discrete assembly products, are also challenged. This paper discusses the differences expected in future factories relevant to VA, and the approaches required to meet this challenge. Current processes are mapped using I4.0 philosophy and gaps are analysed for potential approaches for tolerance analysis tools. Matching points of simulation capability and I4.0 intents are identified as opportunities. Applying conditional variations, incorporating levels of adjustability, and the un-suitability of present Monte Carlo simulation due to changed mass production characteristics, are considered as major challenges. Opportunities including predicting residual stresses in the final product and linking them to product deterioration, calculating non-dimensional performances and extending simulations for process manufactured products, such as drugs, food products etc. are additional winning aspects for next generation VA tools.
Cosmic shear measurements with Dark Energy Survey Science Verification data
Becker, M. R.,Troxel, M. A.,MacCrann, N.,Krause, E.,Eifler, T. F.,Friedrich, O.,Nicola, A.,Refregier, A.,Amara, A.,Bacon, D.,Bernstein, G. M.,Bonnett, C.,Bridle, S. L.,Busha, M. T.,Chang, C.,Dodelson, American Physical Society 2016 Physical Review D Vol.94 No.2
<P>We present measurements of weak gravitational lensing cosmic shear two-point statistics using Dark Energy Survey Science Verification data. We demonstrate that our results are robust to the choice of shear measurement pipeline, either NGMIX or IM3SHAPE, and robust to the choice of two-point statistic, including both real and Fourier-space statistics. Our results pass a suite of null tests including tests for B-mode contamination and direct tests for any dependence of the two-point functions on a set of 16 observing conditions and galaxy properties, such as seeing, airmass, galaxy color, galaxy magnitude, etc. We furthermore use a large suite of simulations to compute the covariance matrix of the cosmic shear measurements and assign statistical significance to our null tests. We find that our covariance matrix is consistent with the halo model prediction, indicating that it has the appropriate level of halo sample variance. We compare the same jackknife procedure applied to the data and the simulations in order to search for additional sources of noise not captured by the simulations. We find no statistically significant extra sources of noise in the data. The overall detection significance with tomography for our highest source density catalog is 9.7 sigma. Cosmological constraints from the measurements in this work are presented in a companion paper.</P>