On point spread function modelling: towards optimal interpolation

Authors

  • Joel Bergé,

    Corresponding author
    1. Department of Physics, ETH Zurich, Wolfgang-Pauli-Strasse 27, CH-8093 Zurich, Switzerland
    2. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, MS 169-327, Pasadena, CA 91109, USA
    3. California Institute of Technology, 1200 East California Blvd, Pasadena, CA 91125, USA
      E-mail: jberge@phys.ethz.ch
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  • Sedona Price,

    Corresponding author
    1. California Institute of Technology, 1200 East California Blvd, Pasadena, CA 91125, USA
      Present address: Department of Astronomy, UC Berkeley, Berkeley, CA 94720, USA.
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  • Adam Amara,

    1. Department of Physics, ETH Zurich, Wolfgang-Pauli-Strasse 27, CH-8093 Zurich, Switzerland
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  • Jason Rhodes

    1. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, MS 169-327, Pasadena, CA 91109, USA
    2. California Institute of Technology, 1200 East California Blvd, Pasadena, CA 91125, USA
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E-mail: jberge@phys.ethz.ch

Present address: Department of Astronomy, UC Berkeley, Berkeley, CA 94720, USA.

ABSTRACT

Point spread function (PSF) modelling is a central part of any astronomy data analysis relying on measuring the shapes of objects. It is especially crucial for weak gravitational lensing, in order to beat down systematics and allow one to reach the full potential of weak lensing in measuring dark energy. A PSF modelling pipeline is made of two main steps: the first one is to assess its shape on stars, and the second is to interpolate it at any desired position (usually galaxies). We focus on the second part, and compare different interpolation schemes, including polynomial interpolation, radial basis functions, Delaunay triangulation and Kriging. For that purpose, we develop simulations of PSF fields, in which stars are built from a set of basis functions defined from a principal components analysis of a real ground-based image. We find that Kriging gives the most reliable interpolation, significantly better than the traditionally used polynomial interpolation. We also note that although a Kriging interpolation on individual images is enough to control systematics at the level necessary for current weak lensing surveys, more elaborate techniques will have to be developed to reach future ambitious surveys’ requirements.

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