Breaking the Mass-sheet Degeneracy in Time-delay Cosmology with Lensed and Unlensed Type Ia Supernovae

TL;DR

This paper presents a Bayesian framework that combines lensed and unlensed Type Ia supernovae observations to effectively break the mass-sheet degeneracy in time-delay cosmography, leading to more accurate cosmological measurements.

Contribution

It introduces a novel method that uses Gaussian processes and combined observational data to resolve the mass-sheet degeneracy without relying on specific cosmological models.

Findings

Successfully reconstructs distance-redshift relations from unlensed SNe Ia.

Demonstrates the framework's ability to recover true MSD parameters in simulations.

Improves the accuracy of time-delay distance measurements in lensing studies.

Abstract

This study introduces an innovative framework aimed at overcoming the ongoing issue of mass-sheet degeneracy (MSD) in time-delay cosmography by incorporating observations of both gravitationally lensed and unlensed Type Ia supernovae (SNe Ia). By simultaneously using lensing magnification measurements $\mu^{\rm{obs}}$ and cosmic distance ratios ($D_s/D_{ds}$), we develop a Bayesian framework capable of breaking the MSD. Specifically, we reconstruct the distance-redshift and magnitude-redshift relations from unlensed Type Ia supernovae using Gaussian process to avoid dependence on specific cosmological models. Our framework shows substantial efficacy in resolving the MSD by imposing constraints on the MSD parameter $\lambda$. Furthermore, we extend this framework to analyze multiple gravitational lensing systems. The results show strong agreement with the fiducial MSD parameters used in the data simulation, confirming our method's effectiveness in mitigating the MSD. Ultimately, this technique enables the derivation of corrected time-delay distance measurements under the MSD, improving the precision of cosmological parameters inferred from strong lensing systems.