Contents
<camera>/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 flux-based 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 |
---|---|---|---|
release | int16 | Unique integer denoting the camera and filter set used (as 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 release,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 | |
maskbits | int16 | Bitwise mask indicating that an object touches a pixel in the coadd/*/*/*maskbits* maps, as cataloged on the DR9 bitmasks page | |
fitbits | int16 | Bitwise mask detailing pecularities of how an object was fit, as cataloged on the DR9 bitmasks page | |
type | char[3] | Morphological model: "PSF"=stellar, "REX"="round exponential galaxy", "DEV"=deVauc, "EXP"=exponential, "SER"=Sersic, "DUP"=Gaia source fit by different model. | |
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 the brick image stack (i.e. in the e.g. legacysurvey-<brick>-image-g.fits.fz coadd file) |
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, SER. The difference is versus no source. | |
ebv | float32 | mag | Galactic extinction E(B-V) reddening from SFD98, used to compute the mw_transmission_ columns |
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, "L3" for the SGA, empty otherwise | |
ref_id | int64 | Reference catalog identifier for this star; Tyc1*1,000,000+Tyc2*10+Tyc3 for Tycho2; "sourceid" for Gaia DR2 and SGA | |
pmra | float32 | mas/yr | Reference catalog proper motion in the RA direction |
pmdec | float32 | mas/yr | Reference catalog proper motion in the Dec direction |
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/(mas)² | Reference catalog inverse-variance on parallax |
ref_epoch | float32 | yr | Reference catalog reference epoch (eg, 2015.5 for Gaia DR2) |
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 | |
gaia_a_g_val | float32 | magnitudes | Gaia line-of-sight extinction in the G band |
gaia_e_bp_min_rp_val | float32 | magnitudes | Gaia line-of-sight reddening E(BP-RP) |
gaia_phot_bp_rp_excess_factor | float32 | Gaia BP/RP excess factor | |
gaia_astrometric_sigma5d_max | float32 | mas | Gaia longest semi-major axis of the 5-d error ellipsoid |
gaia_astrometric_params_solved | uint8 | which astrometric parameters were estimated for a Gaia source | |
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 \(W1\) (AB system) |
flux_w2 | float32 | nanomaggies | WISE model flux in \(W2\) (AB) |
flux_w3 | float32 | nanomaggies | WISE model flux in \(W3\) (AB) |
flux_w4 | float32 | nanomaggies | WISE model flux in \(W4\) (AB) |
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 (AB system) |
flux_ivar_w2 | float32 | 1/nanomaggies² | Inverse variance of flux_w2 (AB) |
flux_ivar_w3 | float32 | 1/nanomaggies² | Inverse variance of flux_w3 (AB) |
flux_ivar_w4 | float32 | 1/nanomaggies² | Inverse variance of flux_w4 (AB) |
fiberflux_g | float32 | nanomaggies | Predicted \(g\)-band flux within a fiber of diameter 1.5 arcsec from this object in 1 arcsec Gaussian seeing |
fiberflux_r | float32 | nanomaggies | Predicted \(r\)-band flux within a fiber of diameter 1.5 arcsec from this object in 1 arcsec Gaussian seeing |
fiberflux_z | float32 | nanomaggies | Predicted \(z\)-band flux within a fiber of diameter 1.5 arcsec from this object in 1 arcsec Gaussian seeing |
fibertotflux_g | float32 | nanomaggies | 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 | nanomaggies | 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 | nanomaggies | 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] | 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 \(g\), masked by \(invvar=0\) (inverse variance of zero [1]) |
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 \(r\), masked by \(invvar=0\) |
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 \(z\), masked by \(invvar=0\) |
apflux_resid_g | float32[8] | nanomaggies | Aperture fluxes on the co-added residual images in \(g\), masked by \(invvar=0\) |
apflux_resid_r | float32[8] | nanomaggies | Aperture fluxes on the co-added residual images in \(r\), masked by \(invvar=0\) |
apflux_resid_z | float32[8] | nanomaggies | Aperture fluxes on the co-added residual images in \(z\), masked by \(invvar=0\) |
apflux_blobresid_g | float32[8] | nanomaggies | Aperture fluxes on \(image-blobmodel\) residual maps in \(g\) [2], masked by \(invvar=0\) |
apflux_blobresid_r | float32[8] | nanomaggies | Aperture fluxes on \(image-blobmodel\) residual maps in \(r\), masked by \(invvar=0\) |
apflux_blobresid_z | float32[8] | nanomaggies | Aperture fluxes on \(image-blobmodel\) residual maps in \(z\), masked by \(invvar=0\) |
apflux_ivar_g | float32[8] | 1/nanomaggies² | Inverse variance of apflux_resid_g, masked by \(invvar=0\) |
apflux_ivar_r | float32[8] | 1/nanomaggies² | Inverse variance of apflux_resid_r, masked by \(invvar=0\) |
apflux_ivar_z | float32[8] | 1/nanomaggies² | Inverse variance of apflux_resid_z, masked by \(invvar=0\) |
apflux_masked_g | float32[8] | Fraction of pixels masked in \(g\)-band aperture flux measurements; 1 means fully masked (ie, fully ignored; contributing zero to the measurement) | |
apflux_masked_r | float32[8] | Fraction of pixels masked in \(r\)-band aperture flux measurements; 1 means fully masked (ie, fully ignored; contributing zero to the measurement) | |
apflux_masked_z | float32[8] | Fraction of pixels masked in \(z\)-band aperture flux measurements; 1 means fully masked (ie, fully ignored; contributing zero to the measurement) | |
apflux_w1 | float32[5] | nanomaggies | Aperture fluxes on the co-added images in apertures of radius [3, 5, 7, 9, 11] [3] arcsec in \(W1\), masked by \(invvar=0\) |
apflux_w2 | float32[5] | nanomaggies | Aperture fluxes on the co-added images in apertures of radius [3, 5, 7, 9, 11] arcsec in \(W2\), masked by \(invvar=0\) |
apflux_w3 | float32[5] | nanomaggies | Aperture fluxes on the co-added images in apertures of radius [3, 5, 7, 9, 11] arcsec in \(W3\), masked by \(invvar=0\) |
apflux_w4 | float32[5] | nanomaggies | Aperture fluxes on the co-added images in apertures of radius [3, 5, 7, 9, 11] arcsec in \(W4\), masked by \(invvar=0\) |
apflux_resid_w1 | float32[5] | nanomaggies | Aperture fluxes on the co-added residual images in \(W1\), masked by \(invvar=0\) |
apflux_resid_w2 | float32[5] | nanomaggies | Aperture fluxes on the co-added residual images in \(W2\), masked by \(invvar=0\) |
apflux_resid_w3 | float32[5] | nanomaggies | Aperture fluxes on the co-added residual images in \(W3\), masked by \(invvar=0\) |
apflux_resid_w4 | float32[5] | nanomaggies | Aperture fluxes on the co-added residual images in \(W4\), masked by \(invvar=0\) |
apflux_ivar_w1 | float32[5] | 1/nanomaggies² | Inverse variance of apflux_resid_w1, masked by \(invvar=0\) |
apflux_ivar_w2 | float32[5] | 1/nanomaggies² | Inverse variance of apflux_resid_w2, masked by \(invvar=0\) |
apflux_ivar_w3 | float32[5] | 1/nanomaggies² | Inverse variance of apflux_resid_w3, masked by \(invvar=0\) |
apflux_ivar_w4 | float32[5] | 1/nanomaggies² | Inverse variance of apflux_resid_w4, masked by \(invvar=0\) |
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 \(g\) as cataloged on the DR9 bitmasks page | |
anymask_r | int16 | Bitwise mask set if the central pixel from any image satisfies each condition in \(r\) as cataloged on the DR9 bitmasks page | |
anymask_z | int16 | Bitwise mask set if the central pixel from any image satisfies each condition in \(z\) as cataloged on the DR9 bitmasks page | |
allmask_g | int16 | Bitwise mask set if the central pixel from all images satisfy each condition in \(g\) as cataloged on the DR9 bitmasks page | |
allmask_r | int16 | Bitwise mask set if the central pixel from all images satisfy each condition in \(r\) as cataloged on the DR9 bitmasks page | |
allmask_z | int16 | Bitwise mask set if the central pixel from all images satisfy each condition in \(z\) as cataloged on the DR9 bitmasks page | |
wisemask_w1 | uint8 | W1 bitmask as cataloged on the DR9 bitmasks page | |
wisemask_w2 | uint8 | W2 bitmask as cataloged on the DR9 bitmasks page | |
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 AB 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 AB 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 AB 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 |
nea_g | float32 | arcsec² | Noise equivalent area in \(g\). |
nea_r | float32 | arcsec² | Noise equivalent area in \(r\). |
nea_z | float32 | arcsec² | Noise equivalent area in \(z\). |
blob_nea_g | float32 | arcsec² | Blob-masked noise equivalent area in \(g\). |
blob_nea_r | float32 | arcsec² | Blob-masked noise equivalent area in \(r\). |
blob_nea_z | float32 | arcsec² | Blob-masked noise equivalent area in \(z\). |
psfdepth_w1 | float32 | 1/nanomaggies² | As for psfdepth_g (and also on the AB system) but for WISE W1 |
psfdepth_w2 | float32 | 1/nanomaggies² | As for psfdepth_g (and also on the AB system) but for WISE W2 |
psfdepth_w3 | float32 | 1/nanomaggies² | As for psfdepth_g (and also on the AB system) but for WISE W3 |
psfdepth_w4 | float32 | 1/nanomaggies² | As for psfdepth_g (and also on the AB system) but for WISE W4 |
wise_coadd_id | char[8] | unWISE coadd brick name (corresponding to the, e.g., legacysurvey-<brick>-image-W1.fits.fz coadd file) for the center of each object | |
wise_x | float32 | pix | X position of coordinates in the brick image stack that corresponds to wise_coadd_id (see the DR9 updates page for transformations between wise_x and bx) |
wise_y | float32 | pix | Y position of coordinates in the brick image stack that corresponds to wise_coadd_id (see the DR9 updates page for transformations between wise_y and by) |
lc_flux_w1 | float32[15] | nanomaggies | flux_w1 in each of up to fifteen unWISE coadd epochs (AB system; defaults to zero for unused entries) |
lc_flux_w2 | float32[15] | nanomaggies | flux_w2 in each of up to fifteen unWISE coadd epochs (AB; defaults to zero for unused entries) |
lc_flux_ivar_w1 | float32[15] | 1/nanomaggies² | Inverse variance of lc_flux_w1 (AB system; defaults to zero for unused entries) |
lc_flux_ivar_w2 | float32[15] | 1/nanomaggies² | Inverse variance of lc_flux_w2 (AB; defaults to zero for unused entries) |
lc_nobs_w1 | int16[15] | nobs_w1 in each of up to fifteen unWISE coadd epochs | |
lc_nobs_w2 | int16[15] | nobs_w2 in each of up to fifteen unWISE coadd epochs | |
lc_fracflux_w1 | float32[15] | fracflux_w1 in each of up to fifteen unWISE coadd epochs (defaults to zero for unused entries) | |
lc_fracflux_w2 | float32[15] | fracflux_w2 in each of up to fifteen unWISE coadd epochs (defaults to zero for unused entries) | |
lc_rchisq_w1 | float32[15] | rchisq_w1 in each of up to fifteen unWISE coadd epochs (defaults to zero for unused entries) | |
lc_rchisq_w2 | float32[15] | rchisq_w2 in each of up to fifteen unWISE coadd epochs (defaults to zero for unused entries) | |
lc_mjd_w1 | float64[15] | mjd_w1 in each of up to fifteen unWISE coadd epochs (defaults to zero for unused entries) | |
lc_mjd_w2 | float64[15] | mjd_w2 in each of up to fifteen unWISE coadd epochs (defaults to zero for unused entries) | |
lc_epoch_index_w1 | int16[15] | Index number of unWISE epoch for W1 (defaults to -1 for unused entries) | |
lc_epoch_index_w2 | int16[15] | Index number of unWISE epoch for W2 (defaults to -1 for unused entries) | |
sersic | float32 | Power-law index for the Sersic profile model (type="SER") | |
sersic_ivar | float32 | Inverse variance of sersic | |
shape_r | float32 | arcsec | Half-light radius of galaxy model for galaxy type type (>0) |
shape_r_ivar | float32 | 1/arcsec² | Inverse variance of shape_r |
shape_e1 | float32 | Ellipticity component 1 of galaxy model for galaxy type type | |
shape_e1_ivar | float32 | Inverse variance of shape_e1 | |
shape_e2 | float32 | Ellipticity component 2 of galaxy model for galaxy type type | |
shape_e2_ivar | float32 | Inverse variance of shape_e2 |
Goodness-of-Fits and Morphological type
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 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 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:
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.
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(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.
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(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 are multiplied by the SFD98 E(B-V) 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(B-V) = 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
[1] | We define a mask for the aperture fluxes using an inverse variance of zero. So, pixels with undefined ("infinite") measurement errors are not used when calculating aperture fluxes in the Tractor catalogs. As the aperture fluxes are calculated from the coadd images described on the files page, pixels end up being ignored if they are masked in every overlapping exposure in a given band. Thus, for example, the saturated cores and bleed trails of bright stars will be masked. Further, in the case that a coadd is only built from a single image, cosmic rays and other mask bits will cause poorly measured and saturated pixels to be ignored for aperture flux measurements. |
[2] | blobmodel refers to the "blob-model" maps (i.e. the <AAA>/<brick>/legacysurvey-<brick>-blobmodel-<filter>.fits.fz maps described on the files page). |
[3] | The aperture sizes for WISE, and the rationale for including them, are detailed in issue #447. |