Photometric redshifts and intrinsic alignments: degeneracies and biases in 3$\times$2pt analysis

TL;DR

This paper investigates how intrinsic alignments and photometric redshift errors can cause biases in cosmological parameters derived from 3x2pt analyses, highlighting degeneracies and the importance of marginalization.

Contribution

It systematically analyzes degeneracies between IA and photo-z models and assesses how modeling choices impact cosmological bias in upcoming surveys.

Findings

Marginalizing over IA parameters prevents cosmological bias.

Strong degeneracies exist between IA parameters and photo-z shifts.

Bias can persist even with good fit statistics if IA is not properly modeled.

Abstract

We present a systematic study of cosmological parameter bias in weak lensing and large-scale structure analyses for upcoming imaging surveys induced by the interplay of intrinsic alignments (IA) and photometric redshift (photo-z) model mis-specification error. We first examine the degeneracies between the parameters of the Tidal Alignment - Tidal Torquing (TATT) model for IA and of a photo-z model including a mean shift ($\Delta \bar{z}$) and variance ($\sigma_{z}$) for each tomographic bin of lenses and sources, under a variety of underlying true IA behaviors. We identify strong degeneracies between: (1) the redshift scaling of the tidal alignment amplitude and the mean shift and variances of source bins, (2) the redshift scaling of the tidal torquing amplitude and the variance of the lowest-$z$ source bin, and (3) the IA source density weighting and the mean shift and variance of several source bins. We then use this information to guide our exploration of the level of cosmological parameter bias which can be induced given incorrect modelling of IA, photo-z, or both. We find that marginalizing over all the parameters of TATT is generally sufficient to preclude cosmological parameter bias in the scenarios we consider. However, this does not necessarily mean that IA and photo-z parameters are themselves unbiased, nor does it mean that the best-fit model is a good fit to the data. We also find scenarios where the inferred parameters produce $\chi^2_{\rm DOF}$ values indicative of a good fit but cosmological parameter bias is significant, particularly when the IA source density weighting parameter is not marginalized over.