Speaker
Description
Stage IV surveys such as Euclid and LSST will have unprecedented constraining power in cosmological parameters. To achieve this scientific goal, one of the major sources of uncertainties, photometric redshifts of the galaxy sample, needs to be under control. Clustering redshift has been a promising method for photometric redshift calibration, as have been adopted in many stage III surveys, but the lack of high redshift spectroscopic samples means that its application is limited for deeper surveys such as Euclid and LSST. In this talk, we explore the novel idea of using Lyman-alpha (Lya) forests from distant quasar spectra as the reference sample for redshift calibration. Lya forests have the advantage of high `sample density’ covering redshift 2 ~ 3, overlapping with the high redshift tail of typical source galaxy samples for the weak lensing survey. We demonstrate the feasibility of this method using (Lya)CoLoRe simulations, and we assess the signal-to-noise (SNR) of the clustering redshift measurement with increasingly realistic noise and various continuum subtraction procedures, such as Picca, a continuum fitting method, and LyCAN, a novel machine-learning based method. This is a promising avenue to combine Stage IV spectroscopic surveys such as DESI with Euclid and LSST to improve the robustness of its scientific results.
