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Cosmological systematics beyond nuisance parameters: form-filling functions |
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| Authors: | T D Kitching A Amara F B Abdalla B Joachimi A Refregier |
| Institution: | University of Oxford, Department of Physics, Keble Road, Oxford OX1 3RH;SUPA, University of Edinburgh, Institute for Astronomy, Royal Observatory Edinburgh, Blackford Hill, Edinburgh EH9 3HJ;Department of Physics, ETH Zurich, Wolfgang-Pauli-Strasse 16, CH-8093 Zurich, Switzerland;Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT;Argelander-Institut fur Astronomie (AIfA), Universitat Bonn, Auf dem Hugel 71, 53121 Bonn, Germany;Service d'Astrophysique, CEA Saclay, Batiment 709, 91191 Gif-sur-Yvette Cedex, France |
| Abstract: | In the absence of any compelling physical model, cosmological systematics are often misrepresented as statistical effects and the approach of marginalizing over extra nuisance systematic parameters is used to gauge the effect of the systematic. In this article, we argue that such an approach is risky at best since the key choice of function can have a large effect on the resultant cosmological errors. As an alternative we present a functional form-filling technique in which an unknown, residual, systematic is treated as such. Since the underlying function is unknown, we evaluate the effect of every functional form allowed by the information available (either a hard boundary or some data). Using a simple toy model, we introduce the formalism of functional form filling. We show that parameter errors can be dramatically affected by the choice of function in the case of marginalizing over a systematic, but that in contrast the functional form-filling approach is independent of the choice of basis set. We then apply the technique to cosmic shear shape measurement systematics and show that a shear calibration bias of | m ( z )| ? 10 A module associated with the work in this paper is available through the open source icosmo code available at http://www.icosmo.org . |
| Keywords: | methods: data analysis methods: numerical methods: statistical cosmology: observations |