tractor/<AAA>/tractor<brick>.fits
FITS binary table containing Tractor photometry. Before using these catalogs, note that there may be
known issues regarding their content and derivation. The columns pertaining to optical data
also have \(u\), \(i\) and \(Y\)band entries (e.g. flux_u
, flux_i
, flux_Y
), but these contain only
zeros.
Name 
Type 
Units 
Description 


int16 
Unique integer denoting the camera and filter set used (RELEASE is documented here) 


int32 
Brick ID [1,662174] 


char[8] 
Name of brick, encoding the brick sky position, e.g. "1126p222" near RA=112.6, Dec=+22.2 


int32 
Catalog object number within this brick; a unique identifier hash is BRICKID,OBJID; OBJID spans [0,N1] and is contiguously enumerated within each brick 


boolean 
True if the object is within the brick boundary 


boolean 
True if the object shares a blob with a "bright" (Tycho2) star 


char[4] 
Morphological model: "PSF"=stellar, "REX"="round exponential galaxy", "DEV"=deVauc, "EXP"=exponential, "COMP"=composite. Note that in some FITS readers, a trailing space may be appended for "PSF ", "DEV " and "EXP " since the column data type is a 4character string 


float64 
deg 
Right ascension at equinox J2000 

float64 
deg 
Declination at equinox J2000 

float32 
1/deg² 
Inverse variance of RA (no cosine term!), excluding astrometric calibration errors 

float32 
1/deg² 
Inverse variance of DEC, excluding astrometric calibration errors 

float32 
pix 
X position (0indexed) of coordinates in brick image stack 

float32 
pix 
Y position (0indexed) of coordinates in brick image stack 

float32[5] 
Difference in χ² between successively morecomplex model fits: PSF, REX, DEV, EXP, COMP. The difference is versus no source. 


float32 
mag 
Galactic extinction \(E(BV)\) reddening from SFD98, used to compute DECAM_MW_TRANSMISSION and WISE_MW_TRANSMISSION 

float64 
days 
Minimum Modified Julian Date of observations used to construct the model of this object 

float64 
days 
Maximum Modified Julian Date of observations used to construct the model of this object 

char[2] 
Reference catalog source for this star: "T2" for Tycho2, "G2" for Gaia DR2, empty otherwise 


int64 
Reference catalog identifier for this star; \(\mathrm{Tyc1} \times 1,000,000 + \mathrm{Tyc2} \times 10 + \mathrm{Tyc3}\) for Tycho2; "sourceid" for GaiaDR2 


float32 
mas/yr 
Reference catalog proper motion in RA direction (\(\mu_\alpha^*\equiv\mu_\alpha\cos\delta\)) in the ICRS at 

float32 
mas/yr 
Reference catalog proper motion in Dec direction (\(\mu_\delta\)) in the ICRS at 

float32 
mas 
Reference catalog parallax 

float32 
1/(mas/yr)² 
Reference catalog inversevariance on 

float32 
1/(mas/yr)² 
Reference catalog inversevariance on 

float32 
1/mas² 
Reference catalog inversevariance on 

float32 
yr 
Reference catalog reference epoch (e.g., 2015.5 for GaiaDR2) 

bool 
This GaiaDR2 source is believed to be a star, not a galaxy 


float32 
mag 
Gaia G band mag 

float32 
Gaia G band signaltonoise 


int16 
Gaia G band number of observations 


float32 
mag 
Gaia BP mag 

float32 
Gaia BP signaltonoise 


int16 
Gaia BP number of observations 


float32 
mag 
Gaia RP mag 

float32 
Gaia RP signaltonoise 


int16 
Gaia RP number of observations 


bool 
Gaia photometric variable flag 


float32 
Gaia astrometric excess noise 


float32 
Gaia astrometric excess noise uncertainty 


int16 
Gaia number of astrometric observations along scan direction 


int16 
Gaia number of good astrometric observations along scan direction 


float32 
Gaia astrometric weight along scan direction 


bool 
Gaia duplicated source flag 


float32 
nanomaggy 
model flux in \(g\) 

float32 
nanomaggy 
model flux in \(r\) 

float32 
nanomaggy 
model flux in \(z\) 

float32 
nanomaggy 
WISE model flux in \(W1\) 

float32 
nanomaggy 
WISE model flux in \(W2\) 

float32 
nanomaggy 
WISE model flux in \(W3\) 

float32 
nanomaggy 
WISE model flux in \(W4\) 

float32 
1/nanomaggy² 
Inverse variance of FLUX_G 

float32 
1/nanomaggy² 
Inverse variance of FLUX_R 

float32 
1/nanomaggy² 
Inverse variance of FLUX_Z 

float32 
1/nanomaggy² 
Inverse variance of FLUX_W1 

float32 
1/nanomaggy² 
Inverse variance of FLUX_W2 

float32 
1/nanomaggy² 
Inverse variance of FLUX_W3 

float32 
1/nanomaggy² 
Inverse variance of FLUX_W4 

float32 
nanomaggy 
Predicted \(g\)band flux within a fiber of diameter 1.5 arcsec from this object in 1 arcsec Gaussian seeing 

float32 
nanomaggy 
Predicted \(r\)band flux within a fiber of diameter 1.5 arcsec from this object in 1 arcsec Gaussian seeing 

float32 
nanomaggy 
Predicted \(z\)band flux within a fiber of diameter 1.5 arcsec from this object in 1 arcsec Gaussian seeing 

float32 
nanomaggy 
Predicted \(g\)band flux within a fiber of diameter 1.5 arcsec from all sources at this location in 1 arcsec Gaussian seeing 

float32 
nanomaggy 
Predicted \(r\)band flux within a fiber of diameter 1.5 arcsec from all sources at this location in 1 arcsec Gaussian seeing 

float32 
nanomaggy 
Predicted \(z\)band flux within a fiber of diameter 1.5 arcsec from all sources at this location in 1 arcsec Gaussian seeing 

float32[8] 
nanomaggy 
Aperture fluxes on the coadded images in apertures of radius [0.5,0.75,1.0,1.5,2.0,3.5,5.0,7.0] arcsec in \(g\) 

float32[8] 
nanomaggy 
Aperture fluxes on the coadded images in apertures of radius [0.5,0.75,1.0,1.5,2.0,3.5,5.0,7.0] arcsec in \(r\) 

float32[8] 
nanomaggy 
Aperture fluxes on the coadded images in apertures of radius [0.5,0.75,1.0,1.5,2.0,3.5,5.0,7.0] arcsec in \(z\) 

float32[8] 
nanomaggy 
Aperture fluxes on the coadded residual images in \(g\) 

float32[8] 
nanomaggy 
Aperture fluxes on the coadded residual images in \(r\) 

float32[8] 
nanomaggy 
Aperture fluxes on the coadded residual images in \(z\) 

float32[8] 
1/nanomaggy² 
Inverse variance of 

float32[8] 
1/nanomaggy² 
Inverse variance of 

float32[8] 
1/nanomaggy² 
Inverse variance of 

float32 
Galactic transmission in \(g\) filter in linear units [0,1] 


float32 
Galactic transmission in \(r\) filter in linear units [0,1] 


float32 
Galactic transmission in \(z\) filter in linear units [0,1] 


float32 
Galactic transmission in \(W1\) filter in linear units [0,1] 


float32 
Galactic transmission in \(W2\) filter in linear units [0,1] 


float32 
Galactic transmission in \(W3\) filter in linear units [0,1] 


float32 
Galactic transmission in \(W4\) filter in linear units [0,1] 


int16 
Number of images that contribute to the central pixel in \(g\): filter for this object (not profileweighted) 


int16 
Number of images that contribute to the central pixel in \(r\): filter for this object (not profileweighted) 


int16 
Number of images that contribute to the central pixel in \(z\): filter for this object (not profileweighted) 


int16 
Number of images that contribute to the central pixel in \(W1\): filter for this object (not profileweighted) 


int16 
Number of images that contribute to the central pixel in \(W2\): filter for this object (not profileweighted) 


int16 
Number of images that contribute to the central pixel in \(W3\): filter for this object (not profileweighted) 


int16 
Number of images that contribute to the central pixel in \(W4\): filter for this object (not profileweighted) 


float32 
Profileweighted χ² of model fit normalized by the number of pixels in \(g\) 


float32 
Profileweighted χ² of model fit normalized by the number of pixels in \(r\) 


float32 
Profileweighted χ² of model fit normalized by the number of pixels in \(z\) 


float32 
Profileweighted χ² of model fit normalized by the number of pixels in \(W1\) 


float32 
Profileweighted χ² of model fit normalized by the number of pixels in \(W2\) 


float32 
Profileweighted χ² of model fit normalized by the number of pixels in \(W3\) 


float32 
Profileweighted χ² of model fit normalized by the number of pixels in \(W4\) 


float32 
Profileweighted fraction of the flux from other sources divided by the total flux in \(g\) (typically [0,1]) 


float32 
Profileweighted fraction of the flux from other sources divided by the total flux in \(r\) (typically [0,1]) 


float32 
Profileweighted fraction of the flux from other sources divided by the total flux in \(z\) (typically [0,1]) 


float32 
Profileweighted fraction of the flux from other sources divided by the total flux in \(W1\) (typically [0,1]) 


float32 
Profileweighted fraction of the flux from other sources divided by the total flux in \(W2\) (typically [0,1]) 


float32 
Profileweighted fraction of the flux from other sources divided by the total flux in \(W3\) (typically [0,1]) 


float32 
Profileweighted fraction of the flux from other sources divided by the total flux in \(W4\) (typically [0,1]) 


float32 
Profileweighted fraction of pixels masked from all observations of this object in \(g\), strictly between [0,1] 


float32 
Profileweighted fraction of pixels masked from all observations of this object in \(r\), strictly between [0,1] 


float32 
Profileweighted fraction of pixels masked from all observations of this object in \(z\), strictly between [0,1] 


float32 
Fraction of a source's flux within the blob in \(g\), near unity for real sources 


float32 
Fraction of a source's flux within the blob in \(r\), near unity for real sources 


float32 
Fraction of a source's flux within the blob in \(z\), near unity for real sources 


int16 
Bitwise mask set if the central pixel from any image satisfies each condition in \(g\) 


int16 
Bitwise mask set if the central pixel from any image satisfies each condition in \(r\) 


int16 
Bitwise mask set if the central pixel from any image satisfies each condition in \(z\) 


int16 
Bitwise mask set if the central pixel from all images satisfy each condition in \(g\) 


int16 
Bitwise mask set if the central pixel from all images satisfy each condition in \(r\) 


int16 
Bitwise mask set if the central pixel from all images satisfy each condition in \(z\) 


uint8 
W1 bright star bitmask, \(2^0\) \((2^1)\) for southward (northward) scans 


uint8 
W2 bright star bitmask, \(2^0\) \((2^1)\) for southward (northward) scans 


float32 
arcsec 
Weighted average PSF FWHM in the \(g\) band 

float32 
arcsec 
Weighted average PSF FWHM in the \(r\) band 

float32 
arcsec 
Weighted average PSF FWHM in the \(z\) band 

float32 
1/nanomaggy² 
For a \(5\sigma\) point source detection limit in \(g\), \(5/\sqrt(\mathrm{PSFDEPTH\_G})\) gives flux in nanomaggies and \(2.5[\log_{10}(5 / \sqrt(\mathrm{PSFDEPTH\_G}))  9]\) gives corresponding magnitude 

float32 
1/nanomaggy² 
For a \(5\sigma\) point source detection limit in \(r\), \(5/\sqrt(\mathrm{PSFDEPTH\_R})\) gives flux in nanomaggies and \(2.5[\log_{10}(5 / \sqrt(\mathrm{PSFDEPTH\_R}))  9]\) gives corresponding magnitude 

float32 
1/nanomaggy² 
For a \(5\sigma\) point source detection limit in \(z\), \(5/\sqrt(\mathrm{PSFDEPTH\_Z})\) gives flux in nanomaggies and \(2.5[\log_{10}(5 / \sqrt(\mathrm{PSFDEPTH\_Z}))  9]\) gives corresponding magnitude 

float32 
1/nanomaggy² 
As for PSFDEPTH_G but for a galaxy (0.45" exp, round) detection sensitivity 

float32 
1/nanomaggy² 
As for PSFDEPTH_R but for a galaxy (0.45" exp, round) detection sensitivity 

float32 
1/nanomaggy² 
As for PSFDEPTH_Z but for a galaxy (0.45" exp, round) detection sensitivity 

char[8] 
unWISE coadd file name for the center of each object 


float32[11] 
nanomaggy 
FLUX_W1 in each of up to eleven unWISE coadd epochs 

float32[11] 
nanomaggy 
FLUX_W2 in each of up to eleven unWISE coadd epochs 

float32[11] 
1/nanomaggy² 
Inverse variance of LC_FLUX_W1 

float32[11] 
1/nanomaggy² 
Inverse variance of LC_FLUX_W2 

int16[11] 
NOBS_W1 in each of up to eleven unWISE coadd epochs 


int16[11] 
NOBS_W2 in each of up to eleven unWISE coadd epochs 


float32[11] 
FRACFLUX_W1 in each of up to eleven unWISE coadd epochs 


float32[11] 
FRACFLUX_W2 in each of up to eleven unWISE coadd epochs 


float32[11] 
RCHISQ_W1 in each of up to eleven unWISE coadd epochs 


float32[11] 
RCHISQ_W2 in each of up to eleven unWISE coadd epochs 


float64[11] 
MJD_W1 in each of up to eleven unWISE coadd epochs 


float64[11] 
MJD_W2 in each of up to eleven unWISE coadd epochs 


float32 
Fraction of model in deVauc [0,1] 


float32 
Inverse variance of FRACDEV 


float32 
arcsec 
Halflight radius of exponential model (>0) 

float32 
1/arcsec² 
Inverse variance of R_EXP 

float32 
Ellipticity component 1 


float32 
Inverse variance of SHAPEEXP_E1 


float32 
Ellipticity component 2 


float32 
Inverse variance of SHAPEEXP_E2 


float32 
arcsec 
Halflight radius of deVaucouleurs model (>0) 

float32 
1/arcsec² 
Inverse variance of R_DEV 

float32 
Ellipticity component 1 


float32 
Inverse variance of SHAPEDEV_E1 


float32 
Ellipticity component 2 


float32 
Inverse variance of SHAPEDEV_E2 
Mask Values
The ANYMASK and ALLMASK bit masks are defined as follows from the CP (NOIRLab Community Pipeline) Data Quality bits.
Bit 
Value 
Name 
Description 

0 
1 
detector bad pixel/no data 
See the CP Data Quality bit description. 
1 
2 
saturated 
See the CP Data Quality bit description. 
2 
4 
interpolated 
See the CP Data Quality bit description. 
4 
16 
single exposure cosmic ray 
See the CP Data Quality bit description. 
6 
64 
bleed trail 
See the CP Data Quality bit description. 
7 
128 
multiexposure transient 
See the CP Data Quality bit description. 
8 
256 
edge 
See the CP Data Quality bit description. 
9 
512 
edge2 
See the CP Data Quality bit description. 
10 
1024 
longthin 
\(\gt 5\sigma\) connected components with major axis \(\gt 200\) pixels and major/minor axis \(\gt 0.1\). To mask, e.g., satellite trails. 
GoodnessofFits
The dchisq
values represent the χ² sum of all pixels in the source's blob
for various models. This 5element vector contains the χ² difference between
the bestfit point source (type="PSF"), round exponential galaxy model ("REX"),
de Vaucouleurs model ("DEV"), exponential model ("EXP"), and a composite model ("COMP"), in that order.
The "REX" model is a round exponential galaxy profile with a variable radius
and is meant to capture slightlyextended but low signaltonoise objects.
The dchisq
values are the χ² difference versus no source in this locationthat is, it is the improvement from adding the given source to our model of the sky. The first element (for PSF) corresponds to a traditional notion of detection significance.
Note that the dchisq
values are negated so that positive values indicate better fits.
We penalize models with negative flux in a band by subtracting rather than adding its χ² improvement in that band.
The rchisq
values are interpreted as the reduced χ² pixelweighted by the model fit,
computed as the following sum over pixels in the blob for each object:
The above sum is over all images contributing to a particular filter, and can be negativevalued for sources that have a flux measured as negative in some bands where they are not detected.
Galactic Extinction Coefficients
The Galactic extinction values are derived from the SFD98 maps, but with updated coefficients to convert \(E(BV)\) to the extinction in each filter. These are reported in linear units of transmission, with 1 representing a fully transparent region of the Milky Way and 0 representing a fully opaque region. The value can slightly exceed unity owing to noise in the SFD98 maps, although it is never below 0.
Extinction coefficients for the SDSS filters have been changed to the values recommended by Schlafly & Finkbeiner (2011) using the Fitzpatrick (1999) extinction curve at \(R_V\) = 3.1 and their improved overall calibration of the SFD98 maps. These coefficients are \(A / E(BV)\) = 4.239, 3.303, 2.285, 1.698, 1.263 in \(ugriz\), which are different from those used in SDSSI,II,III, but are the values used for SDSSIV/eBOSS target selection.
Extinction coefficients for the DECam filters use the Schlafly & Finkbeiner (2011) values, with \(u\)band computed using the same formulae and code at airmass 1.3 (Schlafly, priv. comm. decamdata list on 11/13/14). These coefficients are \(A / E(BV)\) = 3.995, 3.214, 2.165, 1.592, 1.211, 1.064 for the DECam \(u\), \(g\), \(r\), \(i\), \(z\), \(Y\) filters, respectively. Note that these are slightly different from the coefficients in Schlafly & Finkbeiner (2011).
The coefficients for the four WISE filters are derived from Fitzpatrick (1999), as recommended by Schlafly & Finkbeiner (2011), considered better than either the Cardelli et al. (1989) curves or the newer Fitzpatrick & Massa (2009) NIR curve (which is not vetted beyond 2 microns). These coefficients are \(A / E(BV)\) = 0.184, 0.113, 0.0241, 0.00910.
Ellipticities
The ellipticity, ε, is different from the usual eccentricity, \(e \equiv \sqrt{1  (b/a)^2}\). In gravitational lensing studies, the ellipticity is taken to be a complex number:
Where ϕ is the position angle with a range of 180°, due to the ellipse's symmetry. Going between \(r, \epsilon_1, \epsilon_2\) and \(r, b/a, \phi\):