A blinding solution for inference from astronomical data
Abstract
This paper presents a joint blinding and deblinding strategy for inference of physical laws from astronomical data. The strategy allows for up to three blinding stages, where the data may be blinded, the computations of theoretical physics may be blinded, and assuming Gaussianly distributed data  the covariance matrix may be blinded. We found covariance blinding to be particularly effective, as it enables the blinder to determine close to exactly where the blinded posterior will peak. Accordingly, we present an algorithm which induces posterior shifts in predetermined directions by hiding untraceable biases in a covariance matrix. The associated deblinding takes the form of a numerically lightweight postprocessing step, where the blinded posterior is multiplied with deblinding weights. We illustrate the blinding strategy for cosmic shear from KiDS450, and show that even though there is no direct evidence of the KiDS450 covariance matrix being biased, the famous cosmic shear tension with Planck could easily be induced by a mischaracterization of correlations between ξ_{} at the highest redshift and all lower redshifts. The blinding algorithm illustrates the increasing importance of accurate uncertainty assessment in astronomical inferences, as otherwise involuntary blinding through biases occurs.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 March 2020
 DOI:
 10.1093/mnras/staa043
 arXiv:
 arXiv:1910.08533
 Bibcode:
 2020MNRAS.492.3396S
 Keywords:

 methods: data analysis;
 methods: statistical;
 cosmology: observations;
 Astrophysics  Cosmology and Nongalactic Astrophysics;
 Astrophysics  Astrophysics of Galaxies;
 General Relativity and Quantum Cosmology;
 Physics  Data Analysis;
 Statistics and Probability
 EPrint:
 doi:10.1093/mnras/staa043