Atmospheric PSF Interpolation for Weak Lensing in Short Exposure Imaging
  Data
  A main science goal for the Large Synoptic Survey Telescope (LSST) is to
measure the cosmic shear signal from weak lensing to extreme accuracy. One
difficulty, however, is that with the short exposure time ($\simeq$15 seconds)
proposed, the spatial variation of the Point Spread Function (PSF) shapes may
be dominated by the atmosphere, in addition to optics errors. While optics
errors mainly cause the PSF to vary on angular scales similar or larger than a
single CCD sensor, the atmosphere generates stochastic structures on a wide
range of angular scales. It thus becomes a challenge to infer the multi-scale,
complex atmospheric PSF patterns by interpolating the sparsely sampled stars in
the field. In this paper we present a new method, PSFent, for interpolating the
PSF shape parameters, based on reconstructing underlying shape parameter maps
with a multi-scale maximum entropy algorithm. We demonstrate, using images from
the LSST Photon Simulator, the performance of our approach relative to a
5th-order polynomial fit (representing the current standard) and a simple
boxcar smoothing technique. Quantitatively, PSFent predicts more accurate PSF
models in all scenarios and the residual PSF errors are spatially less
correlated. This improvement in PSF interpolation leads to a factor of 3.5
lower systematic errors in the shear power spectrum on scales smaller than
$\sim13'$, compared to polynomial fitting. We estimate that with PSFent and for
stellar densities greater than $\simeq1/{\rm arcmin}^{2}$, the spurious shear
correlation from PSF interpolation, after combining a complete 10-year dataset
from LSST, is lower than the corresponding statistical uncertainties on the
cosmic shear power spectrum, even under a conservative scenario.
