Photo-z for weak lensing tomography from space: the role of optical and near-IR photometry

TL;DR

This study evaluates how combining optical and near-infrared photometry from space and ground-based surveys improves photometric redshift accuracy for weak lensing cosmology, enhancing Dark Energy parameter constraints.

Contribution

It demonstrates that adding IR filters on space telescopes significantly improves photometric redshift quality and Dark Energy Figure of Merit, with implications for survey design and calibration.

Findings

IR photometry increases Dark Energy Figure of Merit by 1.3 to 1.7 times

IR data effectively reduces catastrophic photo-z outliers

Deep u-band data can be as effective as IR in some scenarios

Abstract

We study in detail the photometric redshift requirements needed for tomographic weak gravitational lensing in order to measure accurately the Dark Energy equation of state. In particular, we examine how ground-based photometry (u,g,r,i,z,y) can be complemented by space-based near-infrared (IR) photometry (J,H), e.g. on board the planned DUNE satellite. Using realistic photometric redshift simulations and an artificial neural network photo-z method we evaluate the Figure of Merit for the Dark Energy parameters $(w_0, w_a)$. We consider a DUNE-like broad optical filter supplemented with ground-based multi-band optical data from surveys like the Dark Energy Survey, Pan-STARRS and LSST. We show that the Dark Energy Figure of Merit would improved by a factor of 1.3 to 1.7 if IR filters are added on board DUNE. Furthermore we show that with IR data catastrophic photo-z outliers can be removed effectively. There is an interplay between the choice of filters, the magnitude limits and the removal of outliers. We draw attention to the dependence of the results on the galaxy formation scenarios encoded into the mock galaxies, e.g the galaxy reddening. For example, deep u band data could be as effective as the IR. We also find that about $10^5-10^6$ spectroscopic redshifts are needed for calibration of the full survey.