Supernova Data Compressed Losslessly for Faster Cosmological Analysis

This paper presents a method for lossless compression of cosmological information from Type Ia supernova (SNe Ia) distance measurements. The authors perform model-independent distance measurements on four SNe Ia compilations (Pantheon, Pantheon+, DES-Dovekie, Union3) and compress each dataset into the values of $\log r_p(z)$ at eleven redshift knots. These Gaussian distributed compressed values, together with their full covariance, completely capture the distance-redshift relation information from each dataset. The authors demonstrate this by using these to perform a Markov Chain Monte Carlo (MCMC) likelihood analysis to infer cosmological parameters in flat $\Lambda$CDM, flat $w_0 w_a$CDM, and a non-parametric reconstruction of the dark-energy density $X(z)$. The resulting parameter contours and figures of merit reproduce the corresponding full distance-modulus analyses using the original SNe Ia data sets within the statistical sampling noise of the chains. The SN Ia data compression enables an analytic analysis that completes in $O(10^{-2})$ s per dataset and reduces the downstream cosmological MCMC to the fast evaluation of an $11$-dimensional Gaussian likelihood. This methodology will benefit the data analysis of future surveys from Euclid, Roman, and LSST.

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