# zBEAMS: A unified solution for supernova cosmology with redshift uncertainties

## TL;DR

zBEAMS is a hierarchical Bayesian method that accurately estimates cosmological parameters from supernova data with uncertain redshifts, photometric redshift errors, and host galaxy misidentification, without spectra.

## Contribution

It introduces a unified Bayesian framework that marginalizes over redshift uncertainties and contamination, improving cosmological estimates from photometric supernova surveys.

## Key findings

- Unbiased cosmological parameter estimation with photometric data.
- Effective correction for host galaxy misidentification.
- Demonstrates robustness through supernova simulations.

## Abstract

Supernova cosmology without spectra will be an important component of future surveys such as LSST. This lack of supernova spectra results in uncertainty in the redshifts which, if ignored, leads to significantly biased estimates of cosmological parameters. Here we present a hierarchical Bayesian formalism -- zBEAMS -- that addresses this problem by marginalising over the unknown or uncertain supernova redshifts to produce unbiased cosmological estimates that are competitive with supernova data with spectroscopically confirmed redshifts. zBEAMS provides a unified treatment of both photometric redshifts and host galaxy misidentification (occurring due to chance galaxy alignments or faint hosts), effectively correcting the inevitable contamination in the Hubble diagram. Like its predecessor BEAMS, our formalism also takes care of non-Ia supernova contamination by marginalising over the unknown supernova type. We illustrate this technique with simulations of supernovae with photometric redshifts and host galaxy misidentification. A novel feature of the photometric redshift case is the important role played by the redshift distribution of the supernovae.