Photometric Redshift Requirements for Self-Calibration of Cluster Dark Energy Studies
Marcos Lima, Wayne Hu (KICP, U. Chicago)

TL;DR
Accurate photometric redshift estimates are crucial for galaxy cluster surveys to effectively constrain dark energy, with specific accuracy requirements identified for self-calibration and external validation methods.
Contribution
This paper quantifies photometric redshift accuracy requirements for self-calibration of cluster dark energy studies, highlighting the importance of spectroscopic training sets and combined calibration techniques.
Findings
Photometric redshift bias must be <0.003 for optimal dark energy constraints.
Spectroscopic training for 5-15% of clusters reduces dark energy degradation.
Self-calibration techniques can relax photo-z accuracy requirements.
Abstract
The ability to constrain dark energy from the evolution of galaxy cluster counts is limited by the imperfect knowledge of cluster redshifts. Ongoing and upcoming surveys will mostly rely on redshifts estimated from broad-band photometry (photo-z's). For a Gaussian distribution for the cluster photo-z errors and a high cluster yield cosmology defined by the WMAP 1 year results, the photo-z bias and scatter needs to be known better than 0.003 and 0.03, respectively, in order not to degrade dark energy constrains by more than 10% for a survey with specifications similar to the South Pole Telescope. Smaller surveys and cosmologies with lower cluster yields produce weaker photo-z requirements, though relative to worse baseline constraints. Comparable photo-z requirements are necessary in order to employ self-calibration techniques when solving for dark energy and observable-mass parameters…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
