Ohio State nav bar

CCAPP Seminar - Danielle Leonard (Carnegie Mellon University) "Measuring the scale-dependence of intrinsic alignments using multiple shear estimates"

CCAPP Logo
November 14, 2017
11:30AM - 12:30PM
4138 Physics Research Building

Date Range
Add to Calendar 2017-11-14 11:30:00 2017-11-14 12:30:00 CCAPP Seminar - Danielle Leonard (Carnegie Mellon University) "Measuring the scale-dependence of intrinsic alignments using multiple shear estimates" Abstracts: The next generation of cosmological surveys promises significant advancements in the field of weak gravitational lensing. As such, it is crucial that relevant systematic effects such as the intrinsic alignment of galaxies are well-understood. I will discuss a new method for measuring the scale-dependence of the intrinsic alignment contamination to the galaxy-galaxy lensing signal, which takes advantage of multiple shear estimates applied to the same data set. For a galaxy-galaxy lensing measurement which uses LSST sources and DESI lenses, the signal-to-noise on the intrinsic alignment signal measured by our method is forecast to improve on an existing method (Blazek et al. 2012) by a factor of ~3, for optimally chosen pairs of shear estimates. 4138 Physics Research Building Department of Physics physics@osu.edu America/New_York public

Abstracts: The next generation of cosmological surveys promises significant advancements in the field of weak gravitational lensing. As such, it is crucial that relevant systematic effects such as the intrinsic alignment of galaxies are well-understood. I will discuss a new method for measuring the scale-dependence of the intrinsic alignment contamination to the galaxy-galaxy lensing signal, which takes advantage of multiple shear estimates applied to the same data set. For a galaxy-galaxy lensing measurement which uses LSST sources and DESI lenses, the signal-to-noise on the intrinsic alignment signal measured by our method is forecast to improve on an existing method (Blazek et al. 2012) by a factor of ~3, for optimally chosen pairs of shear estimates.