Modeling Redshift Uncertainties in Roman Weak Lensing Cosmology

TL;DR

This paper develops and tests an optimized PCA method to model redshift uncertainties in Roman Space Telescope weak lensing data, improving cosmological parameter estimation accuracy.

Contribution

It implements an enhanced PCA approach within the Roman HLIS pipeline, demonstrating its effectiveness in reducing biases in cosmological parameters under various redshift miscalibrations.

Findings

PCA-based method produces consistent constraints with mean-shift approach when miscalibration is mild.

Including more PCs reduces biases in $S_8$ and $\Omega_m$ for stronger miscalibrations.

Fewer PCs can achieve similar bias mitigation as the nine-bin mean-shift model.

Abstract

Cosmological constraints using weak gravitational lensing measurements from the Roman Space Telescope will require a powerful method for modelling uncertainties in the galaxy redshift distribution. In this work, we use an optimized version of the principal component analysis (PCA) to model uncertainties in the full shape of the redshift distributions, a method proposed by \cite{pca_method} and recently used in the Dark Energy Survey Y6 analysis. Here, we implement this new approach within the Roman High Latitude Imaging Survey (HLIS) Cosmology Project Infrastructure Team (PIT) pipeline, namely Cobaya-Cosmolike Joint Architecture (\texttt{CoCoA}). To validate the PCA in mitigating biases on cosmological parameters, $S_8$ and $\Omega_m$, we use a set of redshift distributions from \texttt{Cardinal} generated for a variety of Roman configurations. Overall, when the simulated cosmic shear data vector is not strongly miscalibrated relative to the fiducial one, both the mean-shift and the PCA-based approaches produce consistent cosmological constraints when marginalizing over nuisance parameters. For mild to strong miscalibration, including additional PCs progressively mitigates biases in $S_8$ and $\Omega_m$, and can achieve comparable performance with fewer parameters than the nine tomographic-bin mean-shift model.