Galaxy Tagging: photometric redshift refinement and group richness enhancement

TL;DR

The paper introduces 'galtag', a novel probabilistic method that refines photometric redshift estimates and enhances galaxy group richness by leveraging spectroscopic group data, significantly improving accuracy for faint galaxies.

Contribution

This work presents a new scheme, galtag, that improves photometric redshift accuracy and group richness estimation by probabilistically tagging galaxies to observed groups, validated on simulations and real survey data.

Findings

Achieves an order of magnitude improvement in photometric redshift accuracy.

Demonstrates effective group richness enhancement.

Validates method on DESI simulations and KiDS-GAMA data.

Abstract

We present a new scheme, $\it{galtag}$, for refining the photometric redshift measurements of faint galaxies by probabilistically tagging them to observed galaxy groups constructed from a brighter, magnitude-limited spectroscopy survey. First, this method is tested on the DESI light-cone data constructed on the GALFORM galaxy formation model to tests its validity. We then apply it to the photometric observations of galaxies in the Kilo-Degree Imaging Survey (KiDS) over a 1 deg$^2$ region centred at 15$^\mathrm{h}$. This region contains Galaxy and Mass Assembly (GAMA) deep spectroscopic observations (i-band<22) and an accompanying group catalogue to r-band<19.8. We demonstrate that even with some trade-off in sample size, an order of magnitude improvement on the accuracy of photometric redshifts is achievable when using $\it{galtag}$. This approach provides both refined photometric redshift measurements and group richness enhancement. In combination these products will hugely improve the scientific potential of both photometric and spectroscopic datasets. The $\it{galtag}$ software will be made publicly available at https://github.com/pkaf/galtag.git.