A blinding solution for inference from astronomical data
  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 post-processing step, where the blinded
posterior is multiplied with deblinding weights. We illustrate the blinding
strategy for cosmic shear from KiDS-450, and show that even though there is no
direct evidence of the KiDS-450 covariance matrix being biased, the famous
cosmic shear tension with Planck could easily be induced by a
mischaracterization of correlations between $\xi_-$ 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.
