Old Photometric Redshift Sweeps

The DR9 photometric redshift sweeps were updated in July of 2023, and we recommend that users adopt the newer versions.

Below, for posterity, we retain the information for the earlier versions of the photometric redshift sweeps, which are stored at 9.0-photo-z/sweep-<brickmin>-<brickmax>-pz.fits.

The Photometric Redshifts for the Legacy Surveys (PRLS, e.g., see Zhou et al. 2021) catalog is row-by-row-matched to the DR9 sweep catalogs as described previously for the other types of sweeps files.

The photometric redshifts are computed using the random forest algorithm. Details of the photo-z training and performance can be found in Zhou et al. (2021). For computing the photo-z's, we require at least one exposure in \(g\), \(r\) and \(z\) bands (NOBS_G,R,Z>1). For objects that do not meet the NOBS cut, the photo-z values are filled with -99. Although we provide photo-z's for all objects that meet the NOBS cut, only relatively bright objects have reliable photo-z's. As a rule of thumb, objects brighter than \(z\)-band magnitude of 21 are mostly reliable, whereas fainter objects are increasingly unreliable with large systematic offsets.

The photo-z catalogs do not provide information on star-galaxy separation. Stars are excluded from the photo-z training data, and we do not attempt to identify stars. To perform star-galaxy separation, one can use the morphological "TYPE" and/or the photometry (e.g., the optical-WISE color cut, as applied in Zhou et al. 2021, can be very effective for selecting redshift ≳ 0.3 galaxies) in the sweep catalogs.

Name

Type

Description

RELEASE

int16

Integer denoting the camera and filter set used, which will be unique for a given processing run of the data (RELEASE is documented here)

BRICKID

int32

A unique Brick ID (in the range [1, 662174])

OBJID

int32

Catalog object number within this brick; a unique identifier hash is RELEASE,BRICKID,OBJID; OBJID spans [0,N-1] and is contiguously enumerated within each blob

Z_PHOT_MEAN

float32

photo-z derived from the mean of the photo-z PDF

Z_PHOT_MEDIAN

float32

photo-z derived from the median of the photo-z PDF

Z_PHOT_STD

float32

standard deviation of the photo-z's derived from the photo-z PDF

Z_PHOT_L68

float32

lower bound of the 68% confidence region, derived from the photo-z PDF

Z_PHOT_U68

float32

upper bound of the 68% confidence region, derived from the photo-z PDF

Z_PHOT_L95

float32

lower bound of the 95% confidence region, derived from the photo-z PDF

Z_PHOT_U95

float32

upper bound of the 68% confidence region, derived from the photo-z PDF

Z_SPEC

float32

spectroscopic redshift, if available

SURVEY

char[10]

source of the spectroscopic redshift

TRAINING

boolean

whether or not the spectroscopic redshift is used in photometric redshift training

Work which uses this photometric redshift catalog should cite Zhou et al. (2021) and include the additional acknowledgment for photometric redshifts.