Initial results from a laboratory emulation of weak gravitational lensing measurements
Suresh Seshadri, Charles Shapiro, Timothy Goodsall, Jason Fucik,, Christopher M. Hirata, Jason Rhodes, Barnaby Rowe, Roger Smith

TL;DR
This study emulates weak gravitational lensing measurements in the lab using WFIRST-like detectors and image reconstruction, demonstrating that detector-induced biases are below the thresholds affecting cosmological analyses.
Contribution
It presents a laboratory emulation of weak lensing measurements with WFIRST-like detectors, assessing detector-induced biases on shear correlation functions.
Findings
Bias in shear correlation functions is two orders of magnitude below the weak lensing signal.
After polynomial removal, the bias reduces to approximately 10^-6.
Bias at relevant scales for dark energy studies is around 10^-7, meeting future mission requirements.
Abstract
Weak gravitational lensing observations are a key science driver for the NASA Wide Field Infrared Survey Telescope (WFIRST). To validate the performance of the WFIRST infrared detectors, we have performed a laboratory emulation of weak gravitational lensing measurements. Our experiments used a custom precision projector system to image a target mask composed of a grid of pinholes, emulating stellar point sources, onto a 1.7 micron cut-off Teledyne HgCdTe/H2RG detector. We used a 880nm LED illumination source and f/22 pupil stop to produce undersampled point spread functions similar to those expected from WFIRST. We also emulated the WFIRST image reconstruction strategy, using the IMage COMbination (IMCOM) algorithm to derive oversampled images from dithered, undersampled input images. We created shear maps for this data and computed shear correlation functions to mimic a real weak…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
