The Siena Galaxy Atlas

Page author and contact: John Moustakas

The Siena Galaxy Atlas (SGA) is an angular diameter-limited sample of galaxies constructed as part of the DESI Legacy Imaging Surveys. It provides custom, wide-area, optical and infrared mosaics (in \(grz\) and \(W1-W4\)), azimuthally averaged surface brightness profiles, and both aperture and integrated photometry for a sample of approximately 400,000 galaxies over \(20{,}000\,\mathrm{deg}^2\).

All the code used to generate and analyze the catalog is publicly available:

Constructing the atlas involves three major steps. First, we build a parent catalog of known large galaxies. Second, we carry out ellipse-fitting on custom multi-band mosaics. And finally we assemble all the data to produce the final Siena Galaxy Atlas.

Parent Catalog


We begin building the parent sample by querying the HyperLeda extragalactic database for galaxies with angular diameters, \(D(25)>0.2\,\mathrm{arcmin}\) (\(12\,\mathrm{arcsec}\)), where \(D(25)\) is the diameter at the \(25 \mathrm{mag}/\mathrm{arcsec}^2\) surface brightness isophote (in the optical, typically the B-band), a traditional measure of the size of the galaxy popularized by the The Third Reference Catalog of Bright Galaxies (RC3).

The following query was carried out on the version of the HyperLeda database in place on 2018 November 14, resulting in a catalog of 1,436,176 galaxies:

"R50" AS (
  SELECT pgc, avg(lax) AS lax, avg(sax) AS sax
  FROM rawdia
  WHERE quality=0 and dcode=5 and band between 4400 and 4499 GROUP BY pgc
"IR" AS (
  SELECT pgc, avg(lax) AS lax, avg(sax) AS sax
  FROM rawdia
  WHERE quality=0 and iref in (27129) and dcode=7 and band=0 GROUP BY pgc


  m000 AS m
  LEFT JOIN "R50" USING (pgc)
    and (
    m.logd25>0.2 or "R50".lax>0.2 or "IR".lax>0.2

Based on a large number of visual inspections and both quantitative and qualitative tests, we cull the sample by applying the following additional cuts:

  • First, we limit the sample to \(0.333<D(25)<180\,\mathrm{arcmin}\), which removes ~900,000 galaxies (~63% of the original sample), including the Magellanic Clouds and the Sagittarius Dwarf Galaxy at the large-diameter end). We implement the \(D(25)<20\,\mathrm{arcsec}\) cut because we find that the fraction of spurious sources (or sources with incorrect diameters) increases rapidly below this diameter; moreover, galaxies smaller than this size are modeled reasonably well as part of the standard Tractor pipeline.
  • Next, we remove ~3,800 galaxies with no magnitude estimate in HyperLeda, galaxies which we find to be largely spurious based on visual inspection.
  • Third, we remove ~3,800 spurious sources (or galaxies with significantly overestimated diameters) based on visual inspection.
  • Finally, we remove ~1700 galaxies whose primary galaxy identifier (in HyperLeda) is from either SDSS or 2MASS and whose central coordinates place it inside the elliptical aperture of another (non-SDSS and non-2MASS) galaxy with diameter greater than \(0.5\,\mathrm{arcmin}\). Based on visual inspection, we find that many of these sources are shredded, spurious sources with incorrect diameters.

In addition, we visually inspect all the galaxies in the sample with \(D(25)>0.75\,\mathrm{arcmin}\), including all the NGC/IC galaxies, and assess their published elliptical geometry and coordinates. Where necessary, we update the diameter, position angle, minor-to-major axis ratio, and, in some cases, celestial coordinates of these galaxies "by hand" (as indicated in the BYHAND column, described in the data model below). The NASA Extragalactic Database proved invaluable for these cross-checks.

Supplemental Catalogs

To improve the completeness of the HyperLeda catalog, we incorporate several additional, external catalogs into the parent sample.

  1. First, we add the sample of Local Group Dwarf Galaxies from McConnachie (2012), making sure to account for any systems already in the HyperLeda catalog. Using visual inspection, we determine that approximately half these systems are too low surface brightness to be part of the SGA (and therefore require special handling in DR9 of the Legacy Surveys), and so we remove them from the sample. In addition, we move Fornax and Sculptor to the globular cluster sample for special handling in source detection and photometry.
  2. Next, we identify the sample of galaxies in the RC3 and OpenNGC catalogs which are missing from the HyperLeda sample. Surprisingly, many of these systems are large and high surface-brightness.
  3. Finally, we use the photometric catalogs from DR8 of the Legacy Surveys to identify additional, previously unknown large-diameter galaxies. This supplemental catalog consists of two subsamples:
    • First, after applying a variety of catalog-level quality cuts (and extensive visual inspection), we identify all objects in DR8 with half-light radii \(r(50)>14\,\mathrm{arcsec}\) based on their Tractor fits;
    • Second, we construct a candidate sample of compact galaxies which would otherwise be forced to be point sources in DR9 based on their Gaia catalog properties (see this notebook for details).

Group Catalog

Next, we build a simple group catalog based on the angular separation of the galaxies in the sample. We join galaxies together using a friends-of-friends algorithm and a \(10\,\mathrm{arcmin}\) linking length, taking care to ensure that galaxies which overlap (within two times their circularized \(D(25)\) diameter) are assigned to the same group.

We identify ~515,000 unique groups, of which approximately 93% contain just one member. Among the remaining 7% of groups, we find ~15,000 groups with two members, ~1700 groups with 3-5 members, ~50 groups with 6-10 members, and just four groups with 10 or more galaxies (including the Coma Cluster).

We also identify galaxies lying within and outside the Legacy Surveys imaging footprint.

Final Parent Sample and Data Model

The final parent sample contains 535,787 galaxies approximately limited to \(D(25)>20\,\mathrm{arcsec}\), spanning a wide range of magnitude and mean surface brightness. Of these, approximately 400,000 (~75%) lie within the DESI footprint.

Note that because of the supplemental catalogs, this sample includes a small fraction of sources with \(D(25)<20\,\mathrm{arcsec}\); however we retain these galaxies in the parent sample because some of them are historically important NGC/IC galaxies.

XXX Possible figures to make (also need to mention the heterogeneity of the sample and the surface brightness incompleteness somewhere, maybe in a "known issues" page).

  • distribution on the sky
  • diameter vs magnitude;
  • something about the groups?

The table below documents the data model of the current version (v3.0) of the parent sample, $LEGACYHALOS_DATA/SGA-parent-v3.0.fits, where $LEGACYHALOS_DATA is the top-level output directory (environment variable) of the SGA pipeline. For DR9 of the Legacy Surveys, this corresponds to /global/cfs/cdirs/cosmo/data/legacysurvey/dr9/XXX at NERSC.

Name Type Units Description
SGA_ID int64   Unique identifier, corresponding to the row of the original catalog. Galaxies from HyperLeda have IDs 0-1436175; Local Group dwarfs have IDs 2000000-2000092; galaxies from the RC3 and OpenNGC have IDs 3000000-3023010 and 4000000-4013957, respectively; and sources from DR8 of the Legacy Surveys have IDs >5000000
GALAXY char[29]   Unique galaxy name
PGC int64   Unique number from the Principal Catalogue of Galaxies (-1 if none or not known)
RA float64 degree Right Ascension (J2000)
DEC float64 degree Declination (J2000)
MORPHTYPE char[21]   Visual morphological type (note: heterogeneous and incomplete)
PA_LEDA float32 degree Position angle (astronomical convention, clockwise from North)
D25_LEDA float32 arcmin Diameter at the \(25\,\mathrm{mag}/\mathrm{arcsec}^2\) (optical) surface brightness
BA_LEDA float32   Ratio of the semi-minor axis to the semi-major axis
Z_LEDA float32   Heliocentric redshift from HyperLeda (note: missing values, represented with -1.0, do not imply that no redshift exists)
SB_D25_LEDA float32 Vega \(\mathrm{mag}/\mathrm{arcsec}^2\) Mean surface brightness within D25_LEDA based on the brightness in MAG_LEDA
MAG_LEDA float32 Vega mag Estimated total brightness (note: heterogeneous in both bandpass and aperture; but for most galaxies MAG_LEDA is measured in the B-band)
BYHAND boolean   Flag indicating that one or more quantities (RA, DEC, D25_LEDA, PA_LEDA, BA_LEDA, or MAG_LEDA were changed from their published values)
REF char[13]   Unique reference name: LEDA-20181114, LGDWARFS, RC3, OpenNGC, or DR8
IN_FOOTPRINT boolean   Flag indicating whether the galaxy lies within the Legacy Surveys imaging footprint (~74% of the sample)
IN_FOOTPRINT_GRZ boolean   Union of IN_FOOTPRINT and three-band optical coverage at the central position of the galaxy (~70% of the sample)
GROUP_ID int64   Unique group number
GROUP_NAME char[35]   Unique group name, constructed from the name of its largest member (based on D25_LEDA) and the suffix _GROUP
GROUP_MULT int16   Group multiplicity (i.e., number of members)
GROUP_PRIMARY boolean   Flag indicating the primary (i.e., largest) member
GROUP_RA float64 degree Right Ascencion of the group weighted by D25_LEDA
GROUP_DEC float64 degree Declination of the group weighted by D25_LEDA
GROUP_DIAMETER float32 arcmin Approximate group diameter. For groups with a single galaxy, this quantity equals D25_LEDA. For galaxies with multiple members, we estimate the diameter of the group as the maximum separation of all the pairs of group members (plus their D25_LEDA diameter)
BRICKNAME char[8]   Name of brick, encoding the brick sky position, eg "1126p222" near RA=112.6, Dec=+22.2
DIAM float32 arcmin Placeholder column documented below, but in this catalog populated with D25_LEDA
DIAM_REF char[4]   Placeholder column documented below, but in this catalog populated with the string LEDA
PA float32 degree Placeholder column documented below, but in this catalog populated with PA_LEDA
BA float32   Placeholder column documented below, but in this catalog populated with BA_LEDA
ELLIPSEBIT int32   Placeholder column documented below

Custom Mosaics and Ellipse-Fitting

With the parent sample in hand, we analyze every galaxy group independently; however, the code is MPI-parallelized and scales well. Specifically, we

  1. build custom mosaics;
  2. measure surface-brightness profiles by performing ellipse-fitting; and
  3. generate diagnostic plots and webpage visualizations. XXX: Not yet documented

Custom Mosaics

We run the DR9 pipeline on a custom brick centered on the mean coordinates of the galaxy group and using a mosaic width (in pixels) equal to three times the group diameter (based on the GROUP_RA, GROUP_DEC, and GROUP_DIAMETER properties; see the data model table, above). We restrict our analysis to galaxies with \(grz\) coverage in the DESI footprint, and adopt a fixed \(0.262\,\mathrm{arcsec}/\mathrm{pixel}\) pixel scale for the optical imaging throughout.

Unlike the DR9 production runs, we use a couple different options when invoking the photometric pipeline:

  • First, we invoke the --fit-on-coadds option, which triggers the following specialized behavior:
    • After reading the individual, sky-subtracted CCD images and rejecting outlier pixels, we increase the dynamic range of the pixel weights and then generate inverse-variance weighted coadds. We rescale the relative weights in order to downweight the bright central region of the galaxy (even more than from Poisson noise) to prevent Tractor from fitting the central part of the galaxy at the expense of the outer envelope. In addition, we generate an inverse-variance weighted pixelized PSF for each bandpass, and we turn off the default behavior of only fitting point sources to objects detected within the elliptical mask of each large galaxy. Finally, we continue with source detection and model fitting (on the coadded images), as in the normal pipeline.
  • Second, we increase the threshold for detecting and deblending sources by specifying --saddle-fraction 0.2 (the default value is 0.1) and --saddle-min 4.0 (versus the default 2.0). These parameters control the fractional peak height for identifying new sources around existing sources, and the minimum required saddle point depth (in units of sigma above the noise) from existing sources down to new sources, respectively. We find these options necessary in order to prevent excessive shredding and overfitting of the resolved galactic structure of galaxies like HII regions.

This portion of the SGA pipeline produces the files described in the table below. The files are organized into the directory structure $LEGACYHALOS_DATA/RASLICE/GROUP_NAME, where RASLICE [0-359] is the one-degree wide slice of the sky that the object belongs to (in Python, RASLICE='{:06d}'.format(int(GROUP_RA*1000))[:3]), and GROUP_NAME is the name of the galaxy group (see the data model table, above).

Most of these files are standard outputs of the DR9 photometric pipeline and are described on the DR9 files page, although they have been renamed for organizational purposes. Also note that we use the -largegalaxy suffix in many of these files to differentiate other possible variations of the pipeline which produce the same files (but with a different suffix).

File Description
DR9 pipeline ''grz'' files  
GROUP_NAME-ccds-south.fits See the DR9 files page
GROUP_NAME-largegalaxy-blobs.fits.gz See the DR9 files page
GROUP_NAME-largegalaxy-maskbits.fits.fz See the DR9 files page
GROUP_NAME-largegalaxy-outlier-mask.fits.fz See the DR9 files page
GROUP_NAME-largegalaxy-tractor.fits See the DR9 files page
GROUP_NAME-depth-[g,r,z].fits.fz See the DR9 files page
GROUP_NAME-largegalaxy-psf-[g,r,z].fits.fz See the DR9 files page
GROUP_NAME-largegalaxy-[image,invvar,model]-[g,r,z].fits.fz See the DR9 files page
GROUP_NAME-largegalaxy-[image,model,resid]-grz.jpg See the DR9 files page
DR9 pipeline "unWISE" outputs  
GROUP_NAME-[image,invvar,model]-[W1,W2,W3,W4].fits.fz See the DR9 files page
GROUP_NAME-image-W1W2.jpg See the DR9 files page
GROUP_NAME-model-W1W2.jpg See the DR9 files page
SGC pipeline files  
GROUP_NAME-largegalaxy-sample.fits Catalog that corresponds to the data model table, above containing just the galaxies in this galaxy group
GROUP_NAME-coadds.log Logging output for this portion of the pipeline
GROUP_NAME-largegalaxy-coadds.[isdone, isfail] Zero-byte file indicating whether this portion of the pipeline completed successfully (isdone) or failed (isfail)


Next, we measure the multi-band surface brightness profiles of all the galaxies in our sample using the ellipse-fitting tools in the astropy-affiliated package photutils. Once again, we analyze each galaxy group independently and use MPI parallelization to process the full sample in finite time.

Specifically, we carry out the following steps for each galaxy group:

  1. We begin by reading the -largegalaxy-tractor.fits and -largegalaxy-sample.fits catalogs for the field, and reject the following sources from the subsequent ellipse-fitting step, if any:

    • objects missing from the Tractor catalogs (i.e., they were dropped during fitting);
    • objects with negative \(r\hbox{-}\mathrm{band}\) flux or objects fit by Tractor as type PSF;
    • galaxies fit as Tractor type REX which have a measured half-light radius of \(\mathrm{shape\_r}<5\,\mathrm{arcsec}\);
    • galaxies fit as Tractor types EXP, DEV, or SER which have a measured half-light radius of \(\mathrm{shape\_r}<2\,\mathrm{arcsec}\).

    The first two criteria identify spurious sources in the parent catalog, or objects with grossly over-estimated diameters; we reject these objects from the final SGA catalog. The second two criteria identify galaxies which are too small to benefit from ellipse-fitting (i.e., they are well-fit by the standard photometric pipeline); these objects also get special handling when we assemble the final SGA catalog.

  1. Next, we read the \(grz\) images and the corresponding inverse variance and model images. Here and throughout our analysis we use the \(r\hbox{-}\mathrm{band}\) image as the reference band. We also read the -largegalaxy-maskbits.fits image but only retain the BRIGHT, MEDIUM, CLUSTER, ALLMASK_G, ALLMASK_R, and ALLMASK_Z bitmasks (hereafter, we refer to this mask as the starmask). With these pieces in hand, we carry out the following steps:
    • First, we build a residual_mask which accounts for statistically significant differences between the data and the Tractor models. In detail, we flag all pixels which deviate by more than \(5\hbox{-}\sigma\) (in any bandpass) from the absolute value of the Gaussian-smoothed residual image, which we construct by subtracting the model image from the data and smoothing with a 2-pixel Gaussian kernel. This step obviously masks all sources including the galaxy of interest, but we restore those pixels in the next step. In addition, we iteratively dilate the mask two times, and we also mask pixels along the border of the mosaic with a border equal to 2% of the size of the mosaic.
    • Next, we iterate on each galaxy in the group from brightest to faintest based on its \(r\hbox{-}\mathrm{band}\) flux. For each galaxy, we construct the model image from all the Tractor sources in the field except the galaxy of interest, and subtract this model image from the data. We then measure the mean elliptical geometry of the galaxy based on the second moment of the light distribution using a modified version of Michele Cappellari's mge.find_galaxy algorithm (hereafter, the ellipse moments). When computing the ellipse moments, we only use pixels with surface brightness \(>27\,\mathrm{mag}/\mathrm{arcsec}^2\), and we median-filter the image with a 3-pixel boxcar to smooth out any small-scale galactic structure. We then combine the residual_mask with the starmask (using Boolean logic), but unmask pixels belonging to the galaxy based on the geometry of the ellipse moments, but using 1.5 times the estimated semi-major axis of the galaxy.
    • The preceding algorithm fails in fields containing more than one galaxy if the central coordinates of one of galaxies is masked by a previous (brighter) system. (We consider a source to be impacted if any pixels in a 5-pixel diameter box centered on the Tractor position of the galaxy are masked.) In this case, we iteratively shrink the elliptical mask of any of the previous galaxies until the central position of the current galaxy is unmasked.
    • Another occasional failure mode is if the flux-weighted position of the galaxy based on the ellipse moments differs from the Tractor position by more than 10 pixels, which can happen in crowded fields and near bright stars and unmasked image artifacts. In this case we revert to using the Tractor coordinates and model geometry.
    • Finally, we convert the images to surface brightness in \(\mathrm{nanomaggies}/\mathrm{arcsec}^2\) and the weight maps to variance images in \(\mathrm{nanomaggies}^2/\mathrm{arcsec}^4\).
  1. With the images and individual masks for each galaxy in hand, we can now measure the multi-band surface-brightness profiles of each galaxy. We assume a fixed elliptical geometry based on the previously measured ellipse moments, and robustly determine the surface brightness along the elliptical path from the central pixel to two times the estimated semi-major axis of the galaxy (based on the ellipse moments), in 1-pixel intervals. In detail, we measure the surface brightness (and the uncertainty) using nclip=2, sclip=3, and integrmode=median, i.e., two sigma-clipping iterations, a \(3\hbox{-}\sigma\) clipping threshold, and median area integration, respectively, as documented in the photutils.isophote.Ellipse.fit_image method.

    From the \(r\hbox{-}\mathrm{band}\) surface brightness profile, we also robustly measure the size of the galaxy at the following surface brightness thresholds: 22, 22.5, 23, 23.5, 24, 24.5, 25, 25.5 and 26 \(\mathrm{mag}/\mathrm{arcsec}^2\). We perform this measurement by fitting a linear model to the surface brightness profile converted to \(\mathrm{mag}/\mathrm{arcsec}^2\) vs. \(r^{0.25}\) (which would be a straight line for a de Vaucouleurs galaxy profile), but only consider measurements that are within \(\pm1\,\mathrm{mag}/\mathrm{arcsec}^2\) of the desired surface brightness threshold. To estimate the uncertainty in this radius we generate Monte Carlo realizations of the surface brightness profile and use the standard deviation of the resulting distribution of radii.

    Finally, we also measure the curve-of-growth in each bandpass using the tools in photutils.aperture. Briefly, we integrate the image and variance image in each bandpass using elliptical apertures from the center of the galaxy to two times its estimated semi-major axis (based on the ellipse moments, again, in 1-pixel intervals). We fit the curve-of-growth, \(m(r)\) using the following empirical model (based on an equation from Observational Astronomy by Birney, Gonzalez, & Oesper):

    \(m(r) = m_1 + m_0\{1 - \exp[-\alpha_1(r/r_0)^{-\alpha_2}]\}\)

    where \(m_1, m_0, \alpha_1, \alpha_2\) and \(r_0\) are constant parameters of the model and \(r\) is the galactocentric radius (semi-major axis) in arcsec. In our analysis we take the radius scale factor \(r_0=10\,\mathrm{arcsec}\) to be fixed. Note that in the limit \(r \rightarrow\infty\), \(m_1\) is the total, integrated magnitude, and as \(r \rightarrow 0\), \(m_0 + m_1\) is the brightness at the center of the galaxy.

Finally, we package all the measurements, one per galaxy, into an astropy.QTable table (including units for all the quantities), and write out the results. Specifically, this part of the pipeline writes out the following files:

File Description
GROUP_NAME-largegalaxy-ID-ellipse.fits Table containing the ellipse-fitting results for the galaxy with SGA identification number ID, using the data model from the table below
GROUP_NAME-ellipse.log Logging output for this portion of the pipeline
GROUP_NAME-largegalaxy-ellipse.[isdone, isfail] Zero-byte file indicating whether this portion of the pipeline completed successfully (isdone) or failed (isfail)

The data model for the ellipse-fitting results is:

Name Type Units Description
SGA_ID int64   See the data model (the first table on this page)
GALAXY char[?]   See the data model (the first table on this page)
RA float64 degree See the data model (the first table on this page)
DEC float64 degree See the data model (the first table on this page)
PGC int64   See the data model (the first table on this page)
PA_LEDA float32 degree See the data model (the first table on this page)
BA_LEDA float32   See the data model (the first table on this page)
D25_LEDA float32 arcmin See the data model (the first table on this page)
BANDS char[1][3]   List of bandpasses fitted
REFBAND char[1]   Reference band
REFPIXSCALE float32 arcsec/pixel Pixel scale in the reference band
SUCCESS boolean   Flag indicating success or failure
FITGEOMETRY boolean   Flag indicating whether the ellipse geometry was allowed to vary with semi-major axis (here, always False)
INPUT_ELLIPSE boolean   Flag indicating whether ellipse parameters were passed from an external file (here, always False)
LARGESHIFT boolean   Flag indicating that the light-weighted center (from the ellipse moments) is different from the Tractor position by more than 10 pixels in either dimension
RA_X0 float64 degree Right ascension (J2000) at pixel position X0
DEC_Y0 float64 degree Declination (J2000) at pixel position Y0
X0 float32 pixel Light-weighted position along the x-axis (from ellipse moments)
Y0 float32 pixel Light-weighted position along the y-axis (from ellipse moments)
EPS float32   Ellipticity (\(e=1-b/a\), where \(b/a\) is the semi-minor to semi-major axis ratio) see this FAQ for details (from ellipse moments)
PA float32 degree Position angle (astronomical convention, clockwise from North; from ellipse moments)
THETA float32 degree Position angle measured clockwise from the x-axis, given by [\((270-PA)\) mod 180] (from ellipse moments)
MAJORAXIS float32 pixel Light-weighted length of the semi-major axis (from ellipse moments)
MAXSMA float32 pixel Maximum semi-major axis length used for the ellipse-fitting and curve-of-growth measurements (taken to be two times MAJORAXIS)
INTEGRMODE char[6]   photutils.isophote.Ellipse.fit_image integration mode
SCLIP int16   photutils.isophote.Ellipse.fit_image sigma-clipping threshold
NCLIP int16   Number of photutils.isophote.Ellipse.fit_image sigma-clipping iterations
PSFSIZE_[G,R,Z] float32 arcsec Mean width of the point-spread function over the full mosaic (derived from the PSFSIZE_[G,R,Z] columns in the Tractor catalogs)
PSFDEPTH_[G,R,Z] float32 mag Mean \(5\hbox{-}\sigma\) depth over the full mosaic (derived from the PSFDEPTH_[G,R,Z] columns in the Tractor catalogs)
MW_TRANSMISSION_[G,R,Z] float32   Galactic transmission fraction (taken from the corresponding Tractor catalog at the central coordinates of the galaxy)
REFBAND_WIDTH float32 pixel Width of the optical mosaics in REFBAND
REFBAND_HEIGHT float32 pixel Height of the optical mosaics in REFBAND (always equal to REFBAND_WIDTH)
[G,R,Z]_SMA float32 pixel  
[G,R,Z]_EPS float32    
[G,R,Z]_EPS_ERR float32    
[G,R,Z]_PA float32 degree  
[G,R,Z]_PA_ERR float32 degree  
[G,R,Z]_INTENS float32 \(\mathrm{nanomaggies}/\mathrm{arcsec}^2\)  
[G,R,Z]_INTENS_ERR float32 \(\mathrm{nanomaggies}/\mathrm{arcsec}^2\)  
[G,R,Z]_X0 float32 pixel  
[G,R,Z]_X0_ERR float32 pixel  
[G,R,Z]_Y0 float32 pixel  
[G,R,Z]_Y0_ERR float32 pixel  
[G,R,Z]_A3 float32    
[G,R,Z]_A3_ERR float32    
[G,R,Z]_A4 float32    
[G,R,Z]_A4_ERR float32    
[G,R,Z]_RMS float32 \(\mathrm{nanomaggies}/\mathrm{arcsec}^2\)  
[G,R,Z]_PIX_STDDEV float32 \(\mathrm{nanomaggies}/\mathrm{arcsec}^2\)  
[G,R,Z]_STOP_CODE int16    
[G,R,Z]_NDATA int16    
[G,R,Z]_NFLAG int16    
[G,R,Z]_NITER int16    
[G,R,Z]_COG_SMA float32 pixel  
[G,R,Z]_COG_MAG float32 mag  
[G,R,Z]_COG_MAGERR float32 mag  
[G,R,Z]_COG_PARAMS_MTOT float32 mag  
[G,R,Z]_COG_PARAMS_M0 float32 mag  
[G,R,Z]_COG_PARAMS_ALPHA1 float32    
[G,R,Z]_COG_PARAMS_ALPHA2 float32    
[G,R,Z]_COG_PARAMS_CHI2 float32    
RADIUS_SB[23,23.5,24,24.5,25,25.5,26] float32    
RADIUS_SB[23,23.5,24,24.5,25,25.5,26]_ERR float32    
[G,R,Z]_MAG_SB[23,23.5,24,24.5,25,25.5,26] float32    
[G,R,Z]_MAG_SB[23,23.5,24,24.5,25,25.5,26]_ERR float32