Smokescreen: A Python package for data vector blinding and encryption in cosmological analyses

TL;DR

Smokescreen is an open-source Python library that applies cosmology-dependent data vector shifts for blinding in cosmological analyses, ensuring unbiased results while maintaining data security.

Contribution

It introduces a flexible, secure data-vector blinding method compatible with Firecrown likelihoods and the SACC format, applicable to various cosmological experiments.

Findings

Provides a secure, reversible blinding technique for cosmological data analysis.

Ensures the theoretical model used for blinding matches the inference model.

Developed for LSST but adaptable to other experiments using Firecrown and SACC.

Abstract

Smokescreen is an open-source Python library for data-vector concealment (blinding) in cosmological analyses. Data-vector blinding works by applying cosmology-dependent shifts to the observed data vector, moving it away from the true cosmological signal without affecting its statistical properties, so that analysts cannot infer the true result until the analysis is frozen and the blinding is lifted. The package computes these shifts using Firecrown likelihoods applied to data vectors stored in the SACC format, ensuring that the theoretical model used for blinding is identical to that used for inference whilst remaining agnostic to the specific observable being blinded. To prevent accidental unblinding, the original SACC file, containing the true cosmology, is encrypted. Although developed for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), Smokescreen is applicable to any experiment using Firecrown likelihoods and the SACC data format.