Cosmology Requirements on Supernova Photometric Redshift Systematics for Rubin LSST and Roman Space Telescope

Abstract

Some million Type Ia supernovae (SN) will be discovered and monitored during upcoming wide area time domain surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). For cosmological use, accurate redshifts are needed among other characteristics; however the vast majority of the SN will not have spectroscopic redshifts, even for their host galaxies, only photometric redshifts. We assess the redshift systematic control necessary for robust cosmology. Based on the photometric vs true redshift relation generated by machine learning applied to a simulation of 500,000 galaxies as observed with LSST quality, we quantify requirements on systematics in the mean relation and in the outlier fraction and deviance so as not to bias dark energy cosmological inference. Certain redshift ranges are particularly sensitive, motivating spectroscopic followup of SN at $z\lesssim 0.2$ and around $z\approx 0.5$-0.6. Including Nancy Grace Roman Space Telescope near infrared bands in the simulation, we reanalyze the constraints, finding improvements at high redshift but little at the low redshifts where systematics lead to strong cosmology bias. We identify a complete spectroscopic survey of SN host galaxies for $z\lesssim 0.2$ as a highly favored element for robust SN cosmology.

arXiv commentAccepted in PRD. 9 pages, 11 figures

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• The Pantheon+ Analysis: Improving the Redshifts and Peculiar Velocities of Type Ia Supernovae Used in Cosmological Analyses
• Measuring Cosmological Parameters with Type Ia Supernovae in redMaGiC galaxies
• Using Host Galaxy Photometric Redshifts to Improve Cosmological Constraints with Type Ia Supernova in the LSST Era
• Swiss-cheese cosmologies with variable $G$ and $\Lambda$ from the renormalization group
• Optimising a magnitude-limited spectroscopic training sample for photometric classification of supernovae
• Dark energy reconstruction analysis with artificial neural networks: Application on simulated Supernova Ia data from Rubin Observatory
• Exploring Alternative Cosmologies with the LSST: Simulated Forecasts and Current Observational Constraints
• Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates
• The sensitivity of GPz estimates of photo-z posterior PDFs to realistically complex training set imperfections
• Constraining the swiss-cheese IR-fixed point cosmology with cosmic expansion
• Type Ia supernova Hubble diagrams with host galaxy photometric redshifts