# Deep ugrizY Imaging and DEEP2/3 Spectroscopy: A Photometric Redshift Testbed for LSST and Public Release of Data from the DEEP3 Galaxy Redshift Survey

## TL;DR

This paper provides calibrated photometric and spectroscopic galaxy data in the Extended Groth Strip, demonstrating improved photometric redshift accuracy using machine learning, and offers publicly available catalogs for LSST-related research.

## Contribution

It introduces a new catalog with matched-aperture photometry and spectroscopic redshifts, and tests its effectiveness for photometric redshift estimation with machine learning.

## Key findings

- Corrected aperture photometry improves photo-z accuracy
- The catalogs are suitable for LSST photo-z algorithm testing
- Public release of data supports future galaxy redshift studies

## Abstract

We present catalogs of calibrated photometry and spectroscopic redshifts in the Extended Groth Strip, intended for studies of photometric redshifts (photo-z's). The data includes ugriz photometry from CFHTLS and Y-band photometry from the Subaru Suprime camera, as well as spectroscopic redshifts from the DEEP2, DEEP3 and 3D-HST surveys. These catalogs incorporate corrections to produce effectively matched-aperture photometry across all bands, based upon object size information available in the catalog and Moffat profile point spread function fits. We test this catalog with a simple machine learning-based photometric redshift algorithm based upon Random Forest regression, and find that the corrected aperture photometry leads to significant improvement in photo-z accuracy compared to the original SExtractor catalogs from CFHTLS and Subaru. The deep ugrizY photometry and spectroscopic redshifts are well-suited for empirical tests of photometric redshift algorithms for LSST. The resulting catalogs are publicly available. We include a basic summary of the strategy of the DEEP3 Galaxy Redshift Survey to accompany the recent public release of DEEP3 data.