We are taking a leading role in the LSST wavefront sensing algorithm development. We are leading
authors of the fast Fourier Transform (FFT) based wavefront sensing algorithm, and give frequent reports
at the LSST wavefront and alignment system weekly meetings. As one of the two key building blocks of
this FFT algorithm, the wavefront compensation code written by us was quoted as “a milestone” in the
algorithm development by LSST management at the NSF PDR in August, 2011. We independently
proposed and implemented a set of reversed-raytracing and annular-Zernike based methods to overcome
the three challenges imposed on the Active Optics System by the unique optical design of LSST, i.e., the
large central obscuration, the fast f−number, and the fact that the wavefront sensors are off-axis. This
work itself is a breakthrough development in the field of adaptive and active optics. We have also
developed stand alone Matlab libraries to perform image pre-processing tasks including the Instrument
Signature Removal (ISR), source finding, source selection, background subtraction, image centering,
source deblending, stacking, and intensity normalization of the intra and extra focal images. Meanwhile
we are validating the LSST wavefront sensing algorithm in the laboratory. To develop confidence in the
LSST wavefront sensing method, and to explore alternative simpler designs, a wavefront testbed has
been built at Purdue. The testbed utilizes a commercial deformable mirror (DM) and a Shack-Hartmann
(SH) wavefront sensor, which together form a closed adaptive optics loop to provide a precisely controlled
wavefront. In September 2012, the LSST Wavefront Sensing Pipeline developed by us, in collaboration
with NOAO, was first applied on the WIYN telescope One Degree Imager (ODI). Parts of the work
described above have led to two SPIE papers [1,2] and the rest will be presented in a series of three
more journal publications. We expect to continue this leading role in the LSST construction,
commissioning, and operation phases.
LSST provide an unprecedented opportunity for, and challenge to, the gravitational weak lensing
community. A midterm goal of the group is to build a weak lensing pipeline that will be used to analyze
the images taken by LSST. We have developed a set of efficient algorithms specifically for the task of
parameter estimations for observed galaxies, including center position, ellipticity, and shape parameters
such as size and steepness [3, 4]. The position, ellipticity, and shape parameters can then be used in
constructing orthonomal basis functions such as sérsiclets for better galaxy image reconstruction. We
have also exercised the characterization of the point spread function (PSF) as a function of position on
the focal plane with the LSST Data Challenge 3a (DC3a) simulations. We were the first to explore the use
of Open Science Grid on LSST. As part of the coordinated effort in the LSST Dark Energy Science
Collaboration (DESC), we are working on determining the dependence of the PSF on star and galaxy
spectral energy distributions (SEDs), and how well we need to know the small-scale stochastic PSF
variation and whether additional information such as those from the wavefront sensors can be useful to
improve the PSF interpolation between stars.
REFERENCES
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C. F. Claver, S. Chandrasekharana, M. Liang, B. Xin, E. Alagoz, K. Arndt, and I. Shipsey, “Prototype
pipeline for lsst wavefront sensing and reconstruction,” Proc. SPIE 8444, p. 84444P, 2012.
M. Liang, V. Krabbendama, C. F. Claver, S. Chandrasekharana, and B. Xin, “Active optics in large
synoptic survey telescope,” Proc. SPIE 8444, p. 84444Q, 2012.
G. Li, B. Xin, and W. Cui, “Fast shape estimation for galaxies and stars,” arXiv: 1203.0571 , 2012.
T. D. Kitching, B. Rowe, M. Gill, C. Heymans, R. Massey, D. Witherick, F. Courbin, K. Georgatzis, M.
Gen- tile, D. Gruen, M. Kilbinger, G. L. Li, A. P. Mariglis, G. Meylan, A. Storkey, and B. Xin, “Image
Analysis for Cosmology: Results from the GREAT10 Star Challenge,” ArXiv e-prints , Oct. 2012.
R. Noll, “Zernike polynomials and atmospheric turbulence,” J. Opt. Soc. Am. 66, pp. 207–211, 1976.
Figure 1. The optical wavefront test bench at Purdue.
Figure 2. An example of the tests of the wavefront estimation algorithm using a pair of ZEMAXsimulated images. The input images have 2 wavelengths of astigmatism-x (z6 as in Ref. [5]), which
agrees with the output of the algorithm very well.