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 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.

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Cited by in corpus (10)

• KiDS-1000 Cosmology: Multi-probe weak gravitational lensing and spectroscopic galaxy clustering constraints
• Euclid. I. Overview of the Euclid mission
• KiDS-1000 catalogue: Weak gravitational lensing shear measurements
• Blinding multi-probe cosmological experiments
• Cosmology Intertwined: A Review of the Particle Physics, Astrophysics, and Cosmology Associated with the Cosmological Tensions and Anomalies
• Organised Randoms: Learning and correcting for systematic galaxy clustering patterns in KiDS using self-organising maps
• Blind Observers of the Sky
• Validating the Galaxy and Quasar Catalog-Level Blinding Scheme for the DESI 2024 analysis
• Cosmological inference including massive neutrinos from the matter power spectrum: biases induced by uncertainties in the covariance matrix
• Catalog-level blinding on the bispectrum for DESI-like galaxy surveys