<region>/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. Note that all fluxbased quantities in the catalogs are on the AB system (we specify that WISE fluxes are AB in the table for clarity, as such quantities are often quoted on the Vega system).
Name 
Type 
Units 
Description 


int16 
Integer denoting the camera and filter set used, which will be unique for a given processing run of the data (as documented here) 


int32 
Brick ID [1,662174] 


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


int32 
Catalog object number within this brick; a unique identifier hash is 


boolean 



int16 
Bitwise mask indicating that an object touches a pixel in the 


int16 
Bitwise mask detailing pecularities of how an object was fit, as cataloged on the DR9 bitmasks page 


char[3] 
Morphological model: "PSF"=stellar, "REX"="round exponential galaxy", "DEV"=deVauc, "EXP"=exponential, "SER"=Sersic, "DUP"=Gaia source fit by different model. 


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 the brick image stack (i.e. in the e.g. legacysurvey<brick>imageg.fits.fz coadd file) 

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, SER. The difference is versus no source. 


float32 
mag 
Galactic extinction E(BV) reddening from SFD98, used to compute the 

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, "L3" for the SGA, empty otherwise 


int64 
Reference catalog identifier for this star; Tyc1*1,000,000+Tyc2*10+Tyc3 for Tycho2; "sourceid" for Gaia DR2 and SGA 


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 (eg, 2015.5 for Gaia DR2) 

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 
magnitudes 
Gaia lineofsight extinction in the G band 

float32 
magnitudes 
Gaia lineofsight reddening E(BPRP) 

float32 
Gaia BP/RP excess factor 


float32 
mas 
Gaia longest semimajor axis of the 5d error ellipsoid 

uint8 
which astrometric parameters were estimated for a Gaia source 


float32 
nanomaggy 
model flux in \(g\) 

float32 
nanomaggy 
model flux in \(r\) 

float32 
nanomaggy 
model flux in \(z\) 

float32 
nanomaggy 
WISE model flux in \(W1\) (AB system) 

float32 
nanomaggy 
WISE model flux in \(W2\) (AB) 

float32 
nanomaggy 
WISE model flux in \(W3\) (AB) 

float32 
nanomaggy 
WISE model flux in \(W4\) (AB) 

float32 
1/nanomaggy² 
Inverse variance of 

float32 
1/nanomaggy² 
Inverse variance of 

float32 
1/nanomaggy² 
Inverse variance of 

float32 
1/nanomaggy² 
Inverse variance of 

float32 
1/nanomaggy² 
Inverse variance of 

float32 
1/nanomaggy² 
Inverse variance of 

float32 
1/nanomaggy² 
Inverse variance of 

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 \(g\), masked by \(invvar=0\) (inverse variance of zero [1]) 

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 \(r\), masked by \(invvar=0\) 

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 \(z\), masked by \(invvar=0\) 

float32[8] 
nanomaggy 
Aperture fluxes on the coadded residual images in \(g\), masked by \(invvar=0\) 

float32[8] 
nanomaggy 
Aperture fluxes on the coadded residual images in \(r\), masked by \(invvar=0\) 

float32[8] 
nanomaggy 
Aperture fluxes on the coadded residual images in \(z\), masked by \(invvar=0\) 

float32[8] 
nanomaggy 
Aperture fluxes on \(imageblobmodel\) residual maps in \(g\) [2], masked by \(invvar=0\) 

float32[8] 
nanomaggy 
Aperture fluxes on \(imageblobmodel\) residual maps in \(r\), masked by \(invvar=0\) 

float32[8] 
nanomaggy 
Aperture fluxes on \(imageblobmodel\) residual maps in \(z\), masked by \(invvar=0\) 

float32[8] 
1/nanomaggy² 
Inverse variance of 

float32[8] 
1/nanomaggy² 
Inverse variance of 

float32[8] 
1/nanomaggy² 
Inverse variance of 

float32[8] 
Fraction of pixels masked in \(g\)band aperture flux measurements; 1 means fully masked (ie, fully ignored; contributing zero to the measurement) 


float32[8] 
Fraction of pixels masked in \(r\)band aperture flux measurements; 1 means fully masked (ie, fully ignored; contributing zero to the measurement) 


float32[8] 
Fraction of pixels masked in \(z\)band aperture flux measurements; 1 means fully masked (ie, fully ignored; contributing zero to the measurement) 


float32[5] 
nanomaggy 
Aperture fluxes on the coadded images in apertures of radius [3, 5, 7, 9, 11] [3] arcsec in \(W1\), masked by \(invvar=0\) 

float32[5] 
nanomaggy 
Aperture fluxes on the coadded images in apertures of radius [3, 5, 7, 9, 11] arcsec in \(W2\), masked by \(invvar=0\) 

float32[5] 
nanomaggy 
Aperture fluxes on the coadded images in apertures of radius [3, 5, 7, 9, 11] arcsec in \(W3\), masked by \(invvar=0\) 

float32[5] 
nanomaggy 
Aperture fluxes on the coadded images in apertures of radius [3, 5, 7, 9, 11] arcsec in \(W4\), masked by \(invvar=0\) 

float32[5] 
nanomaggy 
Aperture fluxes on the coadded residual images in \(W1\), masked by \(invvar=0\) 

float32[5] 
nanomaggy 
Aperture fluxes on the coadded residual images in \(W2\), masked by \(invvar=0\) 

float32[5] 
nanomaggy 
Aperture fluxes on the coadded residual images in \(W3\), masked by \(invvar=0\) 

float32[5] 
nanomaggy 
Aperture fluxes on the coadded residual images in \(W4\), masked by \(invvar=0\) 

float32[5] 
1/nanomaggy² 
Inverse variance of 

float32[5] 
1/nanomaggy² 
Inverse variance of 

float32[5] 
1/nanomaggy² 
Inverse variance of 

float32[5] 
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 \(g\) as cataloged on the DR9 bitmasks page 


int16 
Bitwise mask set if the central pixel from any image satisfies each condition in \(r\) as cataloged on the DR9 bitmasks page 


int16 
Bitwise mask set if the central pixel from any image satisfies each condition in \(z\) as cataloged on the DR9 bitmasks page 


int16 
Bitwise mask set if the central pixel from all images satisfy each condition in \(g\) as cataloged on the DR9 bitmasks page 


int16 
Bitwise mask set if the central pixel from all images satisfy each condition in \(r\) as cataloged on the DR9 bitmasks page 


int16 
Bitwise mask set if the central pixel from all images satisfy each condition in \(z\) as cataloged on the DR9 bitmasks page 


uint8 
W1 bitmask as cataloged on the DR9 bitmasks page 


uint8 
W2 bitmask as cataloged on the DR9 bitmasks page 


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 AB 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 AB 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 AB magnitude 

float32 
1/nanomaggy² 
As for 

float32 
1/nanomaggy² 
As for 

float32 
1/nanomaggy² 
As for 

float32 
arcsec² 
Noise equivalent area in \(g\). 

float32 
arcsec² 
Noise equivalent area in \(r\). 

float32 
arcsec² 
Noise equivalent area in \(z\). 

float32 
arcsec² 
Blobmasked noise equivalent area in \(g\). 

float32 
arcsec² 
Blobmasked noise equivalent area in \(r\). 

float32 
arcsec² 
Blobmasked noise equivalent area in \(z\). 

float32 
1/nanomaggy² 
As for 

float32 
1/nanomaggy² 
As for 

float32 
1/nanomaggy² 
As for 

float32 
1/nanomaggy² 
As for 

char[8] 
unWISE coadd brick name (corresponding to the, e.g., legacysurvey<brick>imageW1.fits.fz coadd file) for the center of each object 


float32 
pix 
X position of coordinates in the brick image stack that corresponds to 

float32 
pix 
Y position of coordinates in the brick image stack that corresponds to 

float32[15] 
nanomaggy 


float32[15] 
nanomaggy 


float32[15] 
1/nanomaggy² 
Inverse variance of 

float32[15] 
1/nanomaggy² 
Inverse variance of 

int16[15] 



int16[15] 



float32[15] 



float32[15] 



float32[15] 



float32[15] 



float64[15] 



float64[15] 



int16[15] 
Index number of unWISE epoch for W1 (defaults to 1 for unused entries) 


int16[15] 
Index number of unWISE epoch for W2 (defaults to 1 for unused entries) 


float32 
Powerlaw index for the Sersic profile model ( 


float32 
Inverse variance of 


float32 
arcsec 
Halflight radius of galaxy model for galaxy type 

float32 
1/arcsec² 
Inverse variance of 

float32 
Ellipticity component 1 of galaxy model for galaxy type 


float32 
Inverse variance of 


float32 
Ellipticity component 2 of galaxy model for galaxy type 


float32 
Inverse variance of 
GoodnessofFits and Morphological type
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 Sersic model ("SER"), in that order. Note that the Sersic model replaces the composite ("COMP") model used in DR8 (and before).
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.
The final, additional moropholigical type is "DUP." This type is set for Gaia sources that are coincident with, and so have been fit by, an extended source.
No optical flux is assigned to DUP
sources, but they are retained to ensure that all Gaia sources appear in the catalogs even if Tractor prefers an alternate fit.
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.
Eddie Schlafly has computed the extinction coefficients for the DECam filters through airmass=1.3, computed for a 7000K source spectrum as was
done in the Appendix of Schlafly & Finkbeiner (2011).
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 are multiplied by the SFD98 E(BV) values at the coordinates
of each object to derive the \(g\), \(r\) and \(z\) mw_transmission
values in the Legacy Surveys catalogs. The coefficients at different airmasses
only change by a small amount, with the largest effect in \(g\)band where the coefficient would be 3.219 at airmass=1 and 3.202 at airmass=2.
We calculate Galactic extinction for BASS and MzLS as if they are on the DECam filter system.
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 ellipticities for each galaxy type
(i.e. shape_e1
, shape_e2
) are 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 \(r, b/a, \phi\):
Footnotes