A preliminary study of photometric redshifts based on the Wide Field Survey Telescope

TL;DR

This study evaluates the accuracy of photometric redshifts from the upcoming WFST survey using mock data, demonstrating promising results that can be enhanced with data from future surveys like CSST and Euclid.

Contribution

It provides a preliminary assessment of photometric redshift quality for WFST using mock observations and template fitting, highlighting potential improvements with additional survey data.

Findings

Bias less than 0.006 in shallow mode

Normalized median absolute deviation less than 0.03 in shallow mode

Outlier fraction below 5% in shallow mode

Abstract

The Wide Field Survey Telescope (WFST) is a dedicated time-domain multi-band ($u$, $g$, $r$, $i$, and $z$) photometric survey facility under construction. In this paper, we present a preliminary study that assesses the quality of photometric redshifts based on WFST by utilizing mock observations derived with the galaxy catalog in the COSMOS/UltraVISTA field. We apply the template fitting technique to estimate photometric redshifts by using the ZEBRA photometric-redshift code and adopting a modified set of adaptive templates. We evaluate the bias (median relative offset between the output photometric redshifts and input redshifts), normalized median absolute deviation ($\sigma_{\rm NMAD}$) and outlier fraction ($f_{\rm outlier}$) of photometric redshifts in two typical WFST observational cases, the single 30-second exposure observations (hereafter shallow mode) and co-added 50-minute exposure observations (hereafter deep mode). We find bias$\la0.006$, $\sigma_{\rm NMAD}\la0.03$, and $f_{\rm outlier}\la5\%$ in the shallow mode and bias$\approx 0.005$, $\sigma_{\rm NMAD}\approx 0.06$, and $f_{\rm outlier}\approx 17\%$--$27\%$ in the deep mode, respectively, under various lunar phases. Combining the WFST mock observational data with that from the upcoming CSST and Euclid surveys, we demonstrate that the $z_{\rm phot}$ results can be significantly improved, with $f_{\rm outlier}\approx 1\%$ and $\sigma_{\rm NMAD}\approx 0.02$.