Characterization of Atmospheric Turbulence for the Large Synoptic Survey Telescope

Claire-Alice Hebert, Stanford University

Atmospheric turbulence significantly limits the performance of wide-field ground-based telescopes such as the Large Synoptic Survey Telescope (LSST). LSST is an 8.4-meter optical survey telescope now under construction. One of its scientific goals is to detect signatures of dark matter and dark energy by measuring subtle distortions of galaxy shapes due to weak gravitational lensing.

LSST uses active optics to adjust the alignment of its mirrors and camera against external factors such as wind or mechanical stress. Kolmogorov atmospheric turbulence simulations are used to estimate the contribution of atmospheric noise to the control system. In addition, atmospheric turbulence is the dominant contribution to the LSST point spread function (PSF); simulations are used to understand how images are degraded and can be corrected. Ideally these simulations should be supported by data. Here we explore the temporal differences between atmospheric turbulence data and simulations.

The atmospheric turbulence data are recorded by the Gemini Planet Imager Adaptive Optics telemetry at Cerro Pachón, Chile, near the LSST site at El Peñón. These kHz measurements of wavefront distortion can be used to reconstruct the behavior of atmospheric turbulence. The atmospheric simulations are carried out using GalSim, an open-source software package for simulating images of astronomical objects and PSFs. Kolmogorov turbulence is implemented with a von Karman model for the outer scale.

The resulting wavefront phase maps, for both data and simulations, are analyzed with various metrics. The spatial variance of the wavefront provides insight into the Fried parameter, one of the simulation inputs. The phase maps are fit to Zernike polynomials, a complete basis set that describes aberrations over the pupil plane. We contrast the temporal variation of these Zernike coefficient time series between data and simulations and use the power spectral density of each coefficient to compare the different temporal frequency behavior.

Abstract Author(s): Claire-Alice Hebert, Adam Snyder, Aaron Roodman, Bruce Macintosh, Patricia Burchat