Tractor Catalog Format

tractor/<AAA>/tractor-<brick>.fits

FITS binary table containing Tractor photometry. Before using these catalogs, note that there are known issues regarding their content and derivation. In DR5, 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
release int32   Unique integer denoting the camera and filter set used (RELEASE is documented here)
brickid int32   Brick ID [1,662174]
brickname char[8]   Name of brick, encoding the brick sky position, eg "1126p222" near RA=112.6, Dec=+22.2
objid int32   Catalog object number within this brick; a unique identifier hash is BRICKID,OBJID; OBJID spans [0,N-1] and is contiguously enumerated within each blob
brick_primary boolean   True if the object is within the brick boundary
type char[4]   Morphological model: "PSF"=stellar, "REX"="round exponential galaxy" = 0.45" round EXP 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 4-character string
ra float64 deg Right ascension at epoch J2000
dec float64 deg Declination at epoch J2000
ra_ivar float32 1/deg² Inverse variance of RA, excluding astrometric calibration errors
dec_ivar float32 1/deg² Inverse variance of DEC (no cos term!), excluding astrometric calibration errors
bx float32 pix X position (0-indexed) of coordinates in brick image stack
by float32 pix Y position (0-indexed) of coordinates in brick image stack
dchisq float32[5]   Difference in χ² between successively more-complex model fits: PSF, REX, DEV, EXP, COMP. The difference is versus no source.
ebv float32 mag Galactic extinction E(B-V) reddening from SFD98, used to compute DECAM_MW_TRANSMISSION and WISE_MW_TRANSMISSION
mjd_min float64 days Minimum Modified Julian Date of observations used to construct the model of this object
mjd_max float64 days Maximum Modified Julian Date of observations used to construct the model of this object
flux_g float32 nanomaggies model flux in \(g\)
flux_r float32 nanomaggies model flux in \(r\)
flux_z float32 nanomaggies model flux in \(z\)
flux_w1 float32 nanomaggies WISE model flux in \(W1\)
flux_w2 float32 nanomaggies WISE model flux in \(W2\)
flux_w3 float32 nanomaggies WISE model flux in \(W3\)
flux_w4 float32 nanomaggies WISE model flux in \(W4\)
flux_ivar_g float32 1/nanomaggies² Inverse variance of FLUX_G
flux_ivar_r float32 1/nanomaggies² Inverse variance of FLUX_R
flux_ivar_z float32 1/nanomaggies² Inverse variance of FLUX_Z
flux_ivar_w1 float32 1/nanomaggies² Inverse variance of FLUX_W1
flux_ivar_w2 float32 1/nanomaggies² Inverse variance of FLUX_W2
flux_ivar_w3 float32 1/nanomaggies² Inverse variance of FLUX_W3
flux_ivar_w4 float32 1/nanomaggies² Inverse variance of FLUX_W4
apflux_g float32[8] nanomaggies aperture fluxes on the co-added images in apertures of radius [0.5,0.75,1.0,1.5,2.0,3.5,5.0,7.0] arcsec in \(g\)
apflux_r float32[8] nanomaggies aperture fluxes on the co-added images in apertures of radius [0.5,0.75,1.0,1.5,2.0,3.5,5.0,7.0] arcsec in \(r\)
apflux_z float32[8] nanomaggies aperture fluxes on the co-added images in apertures of radius [0.5,0.75,1.0,1.5,2.0,3.5,5.0,7.0] arcsec in \(z\)
apflux_resid_g float32[8] nanomaggies aperture fluxes on the co-added residual images in \(g\)
apflux_resid_r float32[8] nanomaggies aperture fluxes on the co-added residual images in \(r\)
apflux_resid_z float32[8] nanomaggies aperture fluxes on the co-added residual images in \(z\)
apflux_ivar_g float32[8] 1/nanomaggies² Inverse variance of APFLUX_RESID_G
apflux_ivar_r float32[8] 1/nanomaggies² Inverse variance of APFLUX_RESID_R
apflux_ivar_z float32[8] 1/nanomaggies² Inverse variance of APFLUX_RESID_Z
mw_transmission_g float32   Galactic transmission in \(g\) filter in linear units [0,1]
mw_transmission_r float32   Galactic transmission in \(r\) filter in linear units [0,1]
mw_transmission_z float32   Galactic transmission in \(z\) filter in linear units [0,1]
mw_transmission_w1 float32   Galactic transmission in \(W1\) filter in linear units [0,1]
mw_transmission_w2 float32   Galactic transmission in \(W2\) filter in linear units [0,1]
mw_transmission_w3 float32   Galactic transmission in \(W3\) filter in linear units [0,1]
mw_transmission_w4 float32   Galactic transmission in \(W4\) filter in linear units [0,1]
nobs_g int16   Number of images that contribute to the central pixel in \(g\): filter for this object (not profile-weighted)
nobs_r int16   Number of images that contribute to the central pixel in \(r\): filter for this object (not profile-weighted)
nobs_z int16   Number of images that contribute to the central pixel in \(z\): filter for this object (not profile-weighted)
nobs_w1 int16   Number of images that contribute to the central pixel in \(W1\): filter for this object (not profile-weighted)
nobs_w2 int16   Number of images that contribute to the central pixel in \(W2\): filter for this object (not profile-weighted)
nobs_w3 int16   Number of images that contribute to the central pixel in \(W3\): filter for this object (not profile-weighted)
nobs_w4 int16   Number of images that contribute to the central pixel in \(W4\): filter for this object (not profile-weighted)
rchisq_g float32   Profile-weighted χ² of model fit normalized by the number of pixels in \(g\)
rchisq_r float32   Profile-weighted χ² of model fit normalized by the number of pixels in \(r\)
rchisq_z float32   Profile-weighted χ² of model fit normalized by the number of pixels in \(z\)
rchisq_w1 float32   Profile-weighted χ² of model fit normalized by the number of pixels in \(W1\)
rchisq_w2 float32   Profile-weighted χ² of model fit normalized by the number of pixels in \(W2\)
rchisq_w3 float32   Profile-weighted χ² of model fit normalized by the number of pixels in \(W3\)
rchisq_w4 float32   Profile-weighted χ² of model fit normalized by the number of pixels in \(W4\)
fracflux_g float32   Profile-weighted fraction of the flux from other sources divided by the total flux in \(g\) (typically [0,1])
fracflux_r float32   Profile-weighted fraction of the flux from other sources divided by the total flux in \(r\) (typically [0,1])
fracflux_z float32   Profile-weighted fraction of the flux from other sources divided by the total flux in \(z\) (typically [0,1])
fracflux_w1 float32   Profile-weighted fraction of the flux from other sources divided by the total flux in \(W1\) (typically [0,1])
fracflux_w2 float32   Profile-weighted fraction of the flux from other sources divided by the total flux in \(W2\) (typically [0,1])
fracflux_w3 float32   Profile-weighted fraction of the flux from other sources divided by the total flux in \(W3\) (typically [0,1])
fracflux_w4 float32   Profile-weighted fraction of the flux from other sources divided by the total flux in \(W4\) (typically [0,1])
fracmasked_g float32   Profile-weighted fraction of pixels masked from all observations of this object in \(g\), strictly between [0,1]
fracmasked_r float32   Profile-weighted fraction of pixels masked from all observations of this object in \(r\), strictly between [0,1]
fracmasked_z float32   Profile-weighted fraction of pixels masked from all observations of this object in \(z\), strictly between [0,1]
fracin_g float32   Fraction of a source's flux within the blob in \(g\), near unity for real sources
fracin_r float32   Fraction of a source's flux within the blob in \(r\), near unity for real sources
fracin_z float32   Fraction of a source's flux within the blob in \(z\), near unity for real sources
anymask_g int16   Bitwise mask set if the central pixel from any image satisfies each condition in \(g\)
anymask_r int16   Bitwise mask set if the central pixel from any image satisfies each condition in \(r\)
anymask_z int16   Bitwise mask set if the central pixel from any image satisfies each condition in \(z\)
allmask_g int16   Bitwise mask set if the central pixel from all images satisfy each condition in \(g\)
allmask_r int16   Bitwise mask set if the central pixel from all images satisfy each condition in \(r\)
allmask_z int16   Bitwise mask set if the central pixel from all images satisfy each condition in \(z\)
wisemask_w1 uint8   W1 bright star bitmask, \(2^0\) \((2^1)\) for southward (northward) scans
wisemask_w2 uint8   W2 bright star bitmask, \(2^0\) \((2^1)\) for southward (northward) scans
psfsize_g float32 arcsec Weighted average PSF FWHM in the \(g\) band
psfsize_r float32 arcsec Weighted average PSF FWHM in the \(r\) band
psfsize_z float32 arcsec Weighted average PSF FWHM in the \(z\) band
psfdepth_g float32 1/nanomaggies² 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
psfdepth_r float32 1/nanomaggies² For a \(5\sigma\) point source detection limit in \(g\), \(5/\sqrt(\mathrm{PSFDEPTH\_R})\) gives flux in nanomaggies and \(-2.5(\log_{10}((5 / \sqrt(\mathrm{PSFDEPTH\_R}) - 9)\) gives corresponding magnitude
psfdepth_z float32 1/nanomaggies² For a \(5\sigma\) point source detection limit in \(g\), \(5/\sqrt(\mathrm{PSFDEPTH\_Z})\) gives flux in nanomaggies and \(-2.5(\log_{10}((5 / \sqrt(\mathrm{PSFDEPTH\_Z}) - 9)\) gives corresponding magnitude
galdepth_g float32 1/nanomaggies² As for PSFDEPTH_G but for a galaxy (0.45" exp, round) detection sensitivity
galdepth_r float32 1/nanomaggies² As for PSFDEPTH_R but for a galaxy (0.45" exp, round) detection sensitivity
galdepth_z float32 1/nanomaggies² As for PSFDEPTH_Z but for a galaxy (0.45" exp, round) detection sensitivity
wise_coadd_id char[8]   unWISE coadd file name for the center of each object
lc_flux_w1 float32[7] nanomaggies FLUX_W1 in each of up to seven unWISE coadd epochs
lc_flux_w2 float32[7] nanomaggies FLUX_W2 in each of up to seven unWISE coadd epochs
lc_flux_ivar_w1 float32[7] 1/nanomaggies² Inverse variance of LC_FLUX_W1
lc_flux_ivar_w2 float32[7] 1/nanomaggies² Inverse variance of LC_FLUX_W2
lc_nobs_w1 int16[7]   NOBS_W1 in each of up to seven unWISE coadd epochs
lc_nobs_w2 int16[7]   NOBS_W2 in each of up to seven unWISE coadd epochs
lc_fracflux_w1 float32[7]   FRACFLUX_W1 in each of up to seven unWISE coadd epochs
lc_fracflux_w2 float32[7]   FRACFLUX_W2 in each of up to seven unWISE coadd epochs
lc_rchisq_w1 float32[7]   RCHISQ_W1 in each of up to seven unWISE coadd epochs
lc_rchisq_w2 float32[7]   RCHISQ_W2 in each of up to seven unWISE coadd epochs
lc_mjd_w1 float32[7]   MJD_W1 in each of up to seven unWISE coadd epochs
lc_mjd_w2 float32[7]   MJD_W2 in each of up to seven unWISE coadd epochs
fracdev float32   Fraction of model in deVauc [0,1]
fracdev_ivar float32   Inverse variance of FRACDEV
shapeexp_r float32 arcsec Half-light radius of exponential model (>0)
shapeexp_r_ivar float32 1/arcsec² Inverse variance of R_EXP
shapeexp_e1 float32   Ellipticity component 1
shapeexp_e1_ivar float32   Inverse variance of SHAPEEXP_E1
shapeexp_e2 float32   Ellipticity component 2
shapeexp_e2_ivar float32   Inverse variance of SHAPEEXP_E2
shapedev_r float32 arcsec Half-light radius of deVaucouleurs model (>0)
shapedev_r_ivar float32 1/arcsec² Inverse variance of R_DEV
shapedev_e1 float32   Ellipticity component 1
shapedev_e1_ivar float32   Inverse variance of SHAPEDEV_E1
shapedev_e2 float32   Ellipticity component 2
shapedev_e2_ivar float32   Inverse variance of SHAPEDEV_E2

Mask Values

The ANYMASK and ALLMASK bit masks are defined as follows from the CP (NOAO 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 multi-exposure 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.

Goodness-of-Fits

The dchisq values represent the χ² sum of all pixels in the source's blob for various models. This 5-element vector contains the χ² difference between the best-fit 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 slightly-extended but low signal-to-noise objects. The dchisq values are the χ² difference versus no source in this location---that is, it is the improvement from adding the given source to our model of the sky. The first element (for PSF) corresponds to a tradition 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 pixel-weighted by the model fit, computed as the following sum over pixels in the blob for each object:

\begin{equation*} \chi^2 = \frac{\sum \left[ \left(\mathrm{image} - \mathrm{model}\right)^2 \times \mathrm{model} \times \mathrm{inverse\, variance}\right]}{\sum \left[ \mathrm{model} \right]} \end{equation*}

The above sum is over all images contributing to a particular filter, and can be negative-valued 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(B-V) 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(B-V) = 4.239, 3.303, 2.285, 1.698, 1.263 in \(ugriz\), which are different from those used in SDSS-I,II,III, but are the values used for SDSS-IV/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. decam-data list on 11/13/14). These coefficients are A / E(B-V) = 3.995, 3.214, 2.165, 1.592, 1.211, 1.064 (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(B-V) = 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:

\begin{equation*} \epsilon = \frac{a-b}{a+b} \exp( 2i\phi ) = \epsilon_1 + i \epsilon_2 \end{equation*}

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 \(r, b/a, \phi\):

\begin{align*} r & = & r \\ |\epsilon| & = & \sqrt{\epsilon_1^2 + \epsilon_2^2} \\ \frac{b}{a} & = & \frac{1 - |\epsilon|}{1 + |\epsilon|} \\ \phi & = & \frac{1}{2} \arctan \frac{\epsilon_2}{\epsilon_1} \\ |\epsilon| & = & \frac{1 - b/a}{1 + b/a} \\ \epsilon_1 & = & |\epsilon| \cos(2 \phi) \\ \epsilon_2 & = & |\epsilon| \sin(2 \phi) \\ \end{align*}