Tractor Catalog Format

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
`release`	int16		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, e.g. "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 brick
`brick_primary`	boolean		True if the object is within the brick boundary
`brightstarinblob`	boolean		True if the object shares a blob with a "bright" (Tycho-2) star
`type`	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 4-character string
`ra`	float64	deg	Right ascension at equinox J2000
`dec`	float64	deg	Declination at equinox J2000
`ra_ivar`	float32	1/deg²	Inverse variance of RA (no cosine term!), excluding astrometric calibration errors
`dec_ivar`	float32	1/deg²	Inverse variance of DEC, 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
`ref_cat`	char[2]		Reference catalog source for this star: "T2" for Tycho-2, "G2" for Gaia DR2, empty otherwise
`ref_id`	int64		Reference catalog identifier for this star; \(\mathrm{Tyc1} \times 1,000,000 + \mathrm{Tyc2} \times 10 + \mathrm{Tyc3}\) for Tycho2; "sourceid" for Gaia-DR2
`pmra`	float32	mas/yr	Reference catalog proper motion in RA direction (\(\mu_\alpha^*\equiv\mu_\alpha\cos\delta\)) in the ICRS at `ref_epoch`
`pmdec`	float32	mas/yr	Reference catalog proper motion in Dec direction (\(\mu_\delta\)) in the ICRS at `ref_epoch`
`parallax`	float32	mas	Reference catalog parallax
`pmra_ivar`	float32	1/(mas/yr)²	Reference catalog inverse-variance on `pmra`
`pmdec_ivar`	float32	1/(mas/yr)²	Reference catalog inverse-variance on `pmdec`
`parallax_ivar`	float32	1/mas²	Reference catalog inverse-variance on `parallax`
`ref_epoch`	float32	yr	Reference catalog reference epoch (e.g., 2015.5 for Gaia-DR2)
`gaia_pointsource`	bool		This Gaia-DR2 source is believed to be a star, not a galaxy
`gaia_phot_g_mean_mag`	float32	mag	Gaia G band mag
`gaia_phot_g_mean_flux_over_error`	float32		Gaia G band signal-to-noise
`gaia_phot_g_n_obs`	int16		Gaia G band number of observations
`gaia_phot_bp_mean_mag`	float32	mag	Gaia BP mag
`gaia_phot_bp_mean_flux_over_error`	float32		Gaia BP signal-to-noise
`gaia_phot_bp_n_obs`	int16		Gaia BP number of observations
`gaia_phot_rp_mean_mag`	float32	mag	Gaia RP mag
`gaia_phot_rp_mean_flux_over_error`	float32		Gaia RP signal-to-noise
`gaia_phot_rp_n_obs`	int16		Gaia RP number of observations
`gaia_phot_variable_flag`	bool		Gaia photometric variable flag
`gaia_astrometric_excess_noise`	float32		Gaia astrometric excess noise
`gaia_astrometric_excess_noise_sig`	float32		Gaia astrometric excess noise uncertainty
`gaia_astrometric_n_obs_al`	int16		Gaia number of astrometric observations along scan direction
`gaia_astrometric_n_good_obs_al`	int16		Gaia number of good astrometric observations along scan direction
`gaia_astrometric_weight_al`	float32		Gaia astrometric weight along scan direction
`gaia_duplicated_source`	bool		Gaia duplicated source flag
`flux_g`	float32	nanomaggy	model flux in \(g\)
`flux_r`	float32	nanomaggy	model flux in \(r\)
`flux_z`	float32	nanomaggy	model flux in \(z\)
`flux_w1`	float32	nanomaggy	WISE model flux in \(W1\)
`flux_w2`	float32	nanomaggy	WISE model flux in \(W2\)
`flux_w3`	float32	nanomaggy	WISE model flux in \(W3\)
`flux_w4`	float32	nanomaggy	WISE model flux in \(W4\)
`flux_ivar_g`	float32	1/nanomaggy²	Inverse variance of FLUX_G
`flux_ivar_r`	float32	1/nanomaggy²	Inverse variance of FLUX_R
`flux_ivar_z`	float32	1/nanomaggy²	Inverse variance of FLUX_Z
`flux_ivar_w1`	float32	1/nanomaggy²	Inverse variance of FLUX_W1
`flux_ivar_w2`	float32	1/nanomaggy²	Inverse variance of FLUX_W2
`flux_ivar_w3`	float32	1/nanomaggy²	Inverse variance of FLUX_W3
`flux_ivar_w4`	float32	1/nanomaggy²	Inverse variance of FLUX_W4
`fiberflux_g`	float32	nanomaggy	Predicted \(g\)-band flux within a fiber of diameter 1.5 arcsec from this object in 1 arcsec Gaussian seeing
`fiberflux_r`	float32	nanomaggy	Predicted \(r\)-band flux within a fiber of diameter 1.5 arcsec from this object in 1 arcsec Gaussian seeing
`fiberflux_z`	float32	nanomaggy	Predicted \(z\)-band flux within a fiber of diameter 1.5 arcsec from this object in 1 arcsec Gaussian seeing
`fibertotflux_g`	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
`fibertotflux_r`	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
`fibertotflux_z`	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
`apflux_g`	float32[8]	nanomaggy	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]	nanomaggy	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]	nanomaggy	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]	nanomaggy	Aperture fluxes on the co-added residual images in \(g\)
`apflux_resid_r`	float32[8]	nanomaggy	Aperture fluxes on the co-added residual images in \(r\)
`apflux_resid_z`	float32[8]	nanomaggy	Aperture fluxes on the co-added residual images in \(z\)
`apflux_ivar_g`	float32[8]	1/nanomaggy²	Inverse variance of `apflux_resid_g`
`apflux_ivar_r`	float32[8]	1/nanomaggy²	Inverse variance of `apflux_resid_r`
`apflux_ivar_z`	float32[8]	1/nanomaggy²	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/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
`psfdepth_r`	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
`psfdepth_z`	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
`galdepth_g`	float32	1/nanomaggy²	As for PSFDEPTH_G but for a galaxy (0.45" exp, round) detection sensitivity
`galdepth_r`	float32	1/nanomaggy²	As for PSFDEPTH_R but for a galaxy (0.45" exp, round) detection sensitivity
`galdepth_z`	float32	1/nanomaggy²	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[11]	nanomaggy	FLUX_W1 in each of up to eleven unWISE coadd epochs
`lc_flux_w2`	float32[11]	nanomaggy	FLUX_W2 in each of up to eleven unWISE coadd epochs
`lc_flux_ivar_w1`	float32[11]	1/nanomaggy²	Inverse variance of LC_FLUX_W1
`lc_flux_ivar_w2`	float32[11]	1/nanomaggy²	Inverse variance of LC_FLUX_W2
`lc_nobs_w1`	int16[11]		NOBS_W1 in each of up to eleven unWISE coadd epochs
`lc_nobs_w2`	int16[11]		NOBS_W2 in each of up to eleven unWISE coadd epochs
`lc_fracflux_w1`	float32[11]		FRACFLUX_W1 in each of up to eleven unWISE coadd epochs
`lc_fracflux_w2`	float32[11]		FRACFLUX_W2 in each of up to eleven unWISE coadd epochs
`lc_rchisq_w1`	float32[11]		RCHISQ_W1 in each of up to eleven unWISE coadd epochs
`lc_rchisq_w2`	float32[11]		RCHISQ_W2 in each of up to eleven unWISE coadd epochs
`lc_mjd_w1`	float64[11]		MJD_W1 in each of up to eleven unWISE coadd epochs
`lc_mjd_w2`	float64[11]		MJD_W2 in each of up to eleven 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 (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	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 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 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 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 \(A / E(B-V)\) = 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*}