Siena Galaxy Atlas 2020

Primary contacts: John Moustakas (Siena College) and Dustin Lang (Perimeter Institute).

Data Portal

The SGA-2020 data products can be browsed and downloaded at the SGA web-portal, both for individual galaxies and the full sample. Below, we describe the data model for these data products. A forthcoming paper (Moustakas, Lang, et al., in preparation) will describe the SGA-2020 in detail.

Overview

The Siena Galaxy Atlas (SGA) is a multiwavelength atlas of 383,620 nearby galaxies selected based on their apparent angular diameter.

These galaxies are intrinsically large enough to be spatially resolved from our vantage point in the universe, providing a unique and powerful window into the fossil record of galaxy formation and evolution and the galaxy-halo connection. In addition, the atlas serves as a valuable angular map of the foreground extragalactic sky for cosmological surveys of distant galaxies like the Dark Energy Spectroscopic Instrument (DESI) survey.

The SGA delivers precise coordinates, multiwavelength mosaics, azimuthally averaged optical surface brightness and color profiles, integrated and aperture photometry, model images and photometry, and additional metadata for the full sample based on the deep, wide-field grz imaging from the DESI Legacy Imaging Surveys DR9 and the all-sky infrared imaging at 3.4-22 microns from unWISE.

The 2020 version of the atlas, SGA-2020, is primarily selected from the Hyperleda extragalactic database of known large angular-diameter galaxies, supplemented with a small number of other catalogs. Future versions of the atlas will supplement this sample with galaxies identified from the DR9 imaging itself, which will improve the uniformity of the catalog, particularly with respect to surface brightness completeness.

Coupled with existing and forthcoming optical spectroscopy from DESI, particularly the Bright Galaxy Survey (BGS) of 10 million galaxies brigher than $r=20$, the SGA-2020 will yield important new insights into the star formation and mass assembly histories of galaxies, enable myriad complementary studies of the nearby and distant universe, and help engage the broader public in astronomy with visually striking color imaging of large, well-resolved, nearby galaxies.

Sample Selection

In this section we briefly describe the construction of the parent SGA-2020 sample.

Hyperleda Catalog

To construct the initial galaxy catalog, we query the Hyperleda extragalactic database for galaxies with angular diameter $D(25)>0.2$ arcmin, where $D(25)$ is the diameter at the $25\ \mathrm{mag\ arcsec}^{-2}$ surface brightness isophote (in the optical, typically the B-band), a traditional measure of the "size" of the galaxy popularized by the Third Reference Catalog of Bright Galaxies (RC3).

We execute the following query on the 2018 November 14 version of the Hyperleda database, resulting in a catalog of 1,436,176 galaxies:

WITH
"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
)

SELECT
m.pgc, m.objname, m.objtype, m.al2000, m.de2000, m.type, m.bar, m.ring, m.multiple, m.compactness, m.t,
m.logd25, m.logr25, m.pa, m.bt, m.it, m.kt, m.v, m.modbest, "R50".lax, "R50".sax, "IR".lax, "IR".sax,
FROM
m000 AS m
LEFT JOIN "R50" USING (pgc)
LEFT JOIN "IR" USING (pgc)
WHERE
objtype='G' 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 resulting sample by applying the following additional cuts:

1. We limit the sample to $0.333<D(25)<180$ arcmin, which removes roughly 900,000 galaxies (approximately 65% of the original sample), including the Magellanic Clouds and the Sagittarius Dwarf Galaxy at the large-diameter end. After this cut, the largest angular-diameter galaxies which remain are NGC0224=M31 and NGC0598=M33 with $D(25)$ diameters of 178 and 62 arcmin, respectively.

Among smaller systems, we implement the $D(25)<20$ arcsec cut because we find that the fraction of spurious sources (or sources with incorrect diameters) in Hyperleda increases rapidly below this diameter; moreover, galaxies smaller than this size are modeled reasonably well as part of the standard Tractor pipeline (see Tractor implementation details).

1. We remove approximately 3800 galaxies with no magnitude estimate in Hyperleda (as selected by our query), galaxies which we find to be largely spurious based on visual inspection.
1. We remove an additional roughly 6500 spurious sources (or galaxies with significantly overestimated diameters) based on visual inspection.
1. Finally, we reject approximately 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 arcmin. Based on visual inspection, we find that many of these sources are due to shredding or are spurious sources with grossly over-estimated diameters.

In addition, we visually inspect all galaxies in the sample with $D(25)>0.75$ 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, central coordinates "by hand", as indicated in the BYHAND column described in the SGA-2020.fits catalog.

We note that the NASA/IPAC Extragalactic Database (NED) proved invaluable for these cross-checks.

Supplemental Catalogs

To improve the completeness of the Hyperleda catalog, we supplement the sample with several additional catalogs:

1. 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 insufficiently resolved to be part of the SGA-2020 (e.g., Ursa Minor), and so we remove them from the sample.
1. 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 have high average surface brightness.
1. Finally, we use the DR8 photometric catalogs to identify additional large-diameter galaxies. This supplemental catalog consists of two subsamples:
1. First, after applying a variety of catalog-level quality cuts (and extensive visual inspection), we identify all objects in DR8 with half-light radii $r(50)>14$ arcsec based on their Tractor model fits;
2. And 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).

Final Parent Sample

The final parent sample contains 531,677 galaxies approximately limited to $D(25)>20$ arcsec, spanning a wide range of magnitude and mean surface brightness. Of these, 383,620 have grz imaging from DR9 and end up in the final SGA-2020 catalog (see Custom Mosaics & Ellipse-Fitting).

Group Catalog

Galaxies which are close to one another (in apparent, angular coordinates) must be analyzed jointly. Consequently, we build a simple group catalog from the Final Parent Sample using a friends-of-friends algorithm and a 10 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.

Using this procedure, we identify 512,825 unique groups, of which roughly 93% have just one member. Among the remaining groups, approximately 15,000 have two members, 1585 groups have 3-5 members, 51 have 6-10 members, and just four groups have more than 10 galaxies, including the center of the Coma Cluster.

Custom Mosaics & Ellipse-Fitting

We analyze every galaxy group in the parent SGA-2020 catalog independently (noting that the pipeline is MPI-parallelized, and so it scales reasonably well). In the following two sections (Custom Mosaics and Ellipse-Fitting) we describe our procedure in more detail.

Information regarding the resulting data products and their organization on-disk can be found in the Data Products section.

Custom Mosaics

We run the DR9 pipeline on a "custom brick" based on the estimated center and diameter of the galaxy group (using GROUP_RA, GROUP_DEC, and GROUP_DIAMETER defined in SGA-2020.fits). Specifically, we generate mosaics according to the following criteria:

• For groups with GROUP_DIAMETER$\,<14$ arcmin we use a mosaic diameter of $3\, \times$ GROUP_DIAMETER;
• For groups with $14\,<$ GROUP_DIAMETER$\,<30$ arcmin we use a mosaic diameter of $2\, \times$ GROUP_DIAMETER;
• And for groups with GROUP_DIAMETER$\,>30$ arcmin (which only affects NGC0598_GROUP) we use a mosaic diameter of $1.4\, \times$ GROUP_DIAMETER.

In all cases, for the grz imaging we adopt a fixed pixel scale of 0.262 arcsec/pixel and for the unWISE mosaics we use 2.75 arcsec/pixel.

Unlike in DR9, we use a couple different options when calling the legacypipe photometric pipeline:

1. 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, the inverse variance pixel weights are rescaled to prevent Tractor from fitting the central part of the (typically large, high-surface brightness) galaxy at the expense of the outer envelope.
• The source detection and model fitting steps are carried out on the coadded images using the average, inverse-variance weighted pixelized PSF in each bandpass.
• Objects detected within the elliptical mask of each SGA large galaxy are not forced to be point sources.
1. 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).

The saddle-fraction parameter controls the fractional peak height for identifying new sources around existing sources, and the saddle-min parameter is the minimum required saddle point depth (in units of the standard deviation of pixel values above the noise) from existing sources down to new sources.

We find these options necessary in order to prevent excessive shredding and overfitting of the "resolved" galactic structure in individual galaxies (e.g., HII regions).

Ellipse-Fitting

We measure the multi-band surface brightness profiles of each galaxy in the SGA 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.

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

1. We begin by reading the GROUP_NAME-largegalaxy-tractor.fits and GROUP_NAME-largegalaxy-sample.fits catalogs for each group (see the Images and Catalogs section) and reject the following sources from the subsequent ellipse-fitting step, if any:

• objects missing from the Tractor catalog (i.e., they were dropped during Tractor modeling);
• objects with negative r-band flux or objects fit by Tractor as type PSF;
• galaxies fit as Tractor type REX which have a measured half-light major-axis length shape_r $<5$ arcsec;
• galaxies fit as Tractor types EXP, DEV, or SER which have a measured half-light major-axis length shape_r $<2$ arcsec;

The first two criteria identify spurious sources in the initial parent catalog or objects with grossly over-estimated diameters, and all these objects already have been removed from the SGA-2020.fits 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 and have been deemed to not require special handling. These sources will likely be removed from future versions of the SGA.

2. Next, we read the grz images and corresponding inverse variance and model images. Here and throughout our analysis we use the r-band image as the "reference band." We also read the GROUP_NAME-largegalaxy-maskbits.fits image (see Images and Catalogs) but only retain the BRIGHT, MEDIUM, CLUSTER, ALLMASK_G, ALLMASK_R, and ALLMASK_Z bits (defined on the DR9 bitmasks page). Hereafter, we refer to this mask as the starmask.

With these data in hand, we carry out the following steps:

1. 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 $5\sigma$ (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% the size of the mosaic.
1. Next, we iterate on each galaxy in the group from brightest to faintest based on its r-band flux (from Tractor). 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 $>27\ \mathrm{mag\ arcsec}^{-2}$ and we median-filter the image with a 3-pixel boxcar to smooth out any small-scale galactic structure.

Finally, we combine the residual_mask with the starmask (using Boolean logic), but unmask pixels belonging to the galaxy based on the ellipse moments geometry, but using 1.5 times the estimated semi-major axis of the galaxy.

1. The preceding algorithm fails in fields containing more than one galaxy if the central coordinates of one of the 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.

Note that this algorithm is not perfect, particularly in crowded fields (e.g., the center of the Coma Cluster), but will be improved in future versions of the SGA.

1. Another occasional failure mode is if the flux-weighted position of the galaxy based on the ellipse moments differs by 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 and record this occurence in the largeshift bit (see the Bitmasks page).
1. Finally, we convert the data images and variance images to surface brightness in units of $\mathrm{nanomaggies\ arcsec}^{-2}$ and $\mathrm{nanomaggies}^2\ \mathrm{arcsec}^{-4}$, respectively.
3. 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 ellipse moments previously measured, 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 (0.262 arcsec) intervals.

In detail, we measure the surface brightness (and the uncertainty) using nclip=3, sclip=3, and integrmode=median, i.e., two sigma-clipping iterations, a $3\sigma$ clipping threshold, and median area integration, respectively, as documented in the photutils.isophote.Ellipse.fit_image method.

From the r-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 $26\ \mathrm{mag\ arcsec}^{-2}$ . We perform this measurement by fitting a linear model to the surface brightness profile converted to $\mathrm{mag\ arcsec}^{-2}$ vs $r^{0.25}$ (which would be a straight line for a de Vaucouleurs galaxy profile), but only consider measurements which are within $\pm1\ \mathrm{mag\ arcsec}^{-2}$ 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 or 0.262 arcsec intervals).

We fit the resulting curve-of-growth, $m(r)$ using the following empirical model:

\begin{equation*} m(r) = m_{tot} + m_{0} \log_{e}\left[1 + \alpha_{1} \left(\frac{r}{r_{0}}\right)^{-\alpha_{2}} \right] \end{equation*}

where $m_{tot}$, $m_{0}$, $\alpha_{1}$, $\alpha_{2}$, and $r_{0}$ are constant parameters of the model and r is the semi-major axis in arcsec. In our analysis we take the radius scale factor $r_{0}=10$ arcsec to be fixed. Note that in the limit $r\rightarrow\infty$, $m_{tot}$ is the total, integrated magnitude.

With this model, the half-light semi-major axis, $r_{50}$, can be inferred from the best-fitting model parameters:

\begin{equation*} r_{50} = r_{0} \left\{ \frac{1}{\alpha_{1}} \left[ \exp\left( -\frac{\log_{10}(0.5)}{0.4 m_{0}} \right) -1 \right] \right\}^{-1/\alpha_{2}} \end{equation*}

Finally, we package all the measurements, one per galaxy, into an astropy.QTable table (including units on all the quantities), and write out the results documented in the Data Products section.

Data Products

The principal SGA-2020 data product is the SGA-2020.fits catalog, which contains detailed information for 383,620 galaxies with three-band (grz) imaging from DR9, spanning approximately 20,000 square degrees (see the Contents of DR9 page for a more precise area).

For these systems we generate custom multiband mosaics, perform Tractor modeling of all the sources in the field, and (for most systems) measure the surface-brightness profiles, color profiles, and optical curves of growth using standard ellipse-fitting techniques. These additional data products are documented in the Group Files section.

The figure below shows the distribution of the SGA-2020 sample in an equal-area Aitoff projection in equatorial coordinates.

SGA-2020.fits

Number EXTNAME Type Contents
HDU00 PRIMARY IMAGE Empty.
HDU01 ELLIPSE BINTABLE Ellipse-fitting results.
HDU02 TRACTOR BINTABLE Tractor modeling results.

ELLIPSE

Name Type Units Description
SGA_ID int64   Unique integer identifier.
SGA_GALAXY char[16]   SGA galaxy name, constructed as "SGA-2020 SGA_ID".
GALAXY char[29]   Unique galaxy name.
PGC int64   Unique identifier from the Principal Catalogue of Galaxies (-1 if none or not known).
RA_LEDA float64 degree Right ascension (J2000) from the reference indicated in REF (but see also the BYHAND column).
DEC_LEDA float64 degree Declination (J2000) from the reference indicated in REF (but see also the BYHAND column).
MORPHTYPE char[21]   Visual morphological type from Hyperleda (if available).
PA_LEDA float32 degree Galaxy position angle, measured positive clockwise from North, taken from the reference indicated in REF (but see also the BYHAND column).
D25_LEDA float32 arcmin Approximate major-axis diameter at the $25\ \mathrm{mag}\ \mathrm{arcsec}^{-2}$ (optical) surface brightness isophote, taken from the reference indicated in REF (but see also the BYHAND column).
BA_LEDA float32   Ratio of the semi-minor axis to the semi-major axis, taken from the reference indicated in REF (but see also the BYHAND column).
Z_LEDA float32   Heliocentric redshift from HyperLeda. Note: a missing value, represented with -1.0, does 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 Approximate brightness (Note: this magnitude estimate is heterogeneous in both bandpass and aperture but for most galaxies it is measured in the B-band within D25_LEDA ; use with care.)
BYHAND Boolean   Flag indicating whether one or more of RA_LEDA, DEC_LEDA, D25_LEDA, PA_LEDA, BA_LEDA, or MAG_LEDA were changed from their published HyperLeda values, generally based on visual inspection.
REF char[13]   Unique reference name indicating the original source of the object, as described in Sample Selection: LEDA-20181114, LGDWARFS, RC3, OpenNGC, or DR8.
GROUP_ID int64   Unique group identification number.
GROUP_NAME char[35]   Unique group name, constructed from the name of its largest member (based on D25_LEDA) and the suffix _GROUP (e.g., PGC193199_GROUP).
GROUP_MULT int16   Group multiplicity (i.e., number of group members from the parent sample).
GROUP_PRIMARY Boolean   Flag indicating the primary (i.e., largest) group 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, e.g. "1126p222" is centered on RA=112.6, Dec=+22.2.
RA float64 degree Right ascension (J2000) based on the Tractor model fit; identical to RA in the TRACTOR HDU.
DEC float64 degree Declination (J2000) based on the Tractor model fit; identical to DEC in the TRACTOR HDU.
D26 float32 arcmin Major axis diameter measured at the $\mu=26\ \mathrm{mag}\ \mathrm{arcsec}^{-2}$ r-band isophote based on SMA_SB26. If the r-band surface-brightness profile could not be measured at this level, the diameter is set equal to $2.5\times$ SMA_SB25 or $1.5\times$ D25_LEDA, in that order of priority.
D26_REF char[4]   Reference indicating the origin of the DIAM measurement: SB26, SB25, or LEDA.
PA float32 degree Galaxy position angle, measured positive clockwise from North, as measured from the ellipse moments (or equivalent to PA_LEDA if the ellipse moments could not be measured).
BA float32   Minor-to-major axis ratio, as measured from the ellipse moments (or equivalent to BA_LEDA if the ellipse moments could not be measured).
RA_MOMENT float64 degree Light-weighted right ascension (J2000), as measured from the ellipse moments. Equivalent to RA_X0 in the Ellipse Fits table but set to RA_LEDA if ellipse-fitting was not carried out.
DEC_MOMENT float64 degree Light-weighted declination (J2000), as measured from the ellipse moments. Equivalent to DEC_Y0 in the Ellipse Fits table but set to DEC_LEDA if ellipse-fitting was not carried out.
SMA_MOMENT float32 arcsec Second moment of the light distribution along the major axis based on the measured ellipse moments (-1 if not measured). Equivalent to MAJORAXIS (but converted to arcsec) in the Ellipse Fits table.
[G,R,Z]_SMA50 float32 arcsec Half-light semi-major axis length in each bandpass based on the fit to the curve of growth (see the Ellipse-Fitting section; -1 if not measured).
SMA_SB[22,22.5,23,23.5,24,24.5,25,25.5,26] float32 arcsec Semi-major axis length at the $\mu=22$, 22.5, 23, 23.5, 24, 24.5, 25, 25.5, and $26 \mathrm{mag}\ \mathrm{arcsec}^{-2}$ isophote in the r-band (-1 if not measured).
SMA_SB[22,22.5,23,23.5,24,24.5,25,25.5,26]_ERR float32 arcsec Uncertainty in SMA_SB[22,22.5,23,23.5,24,24.5,25,25.5,26] ($1\sigma$).
[G,R,Z]_MAG_SB[22,22.5,23,23.5,24,24.5,25,25.5,26] float32 AB mag Cumulative brightness measured within SMA_SB[22,22.5,23,23.5,24,24.5,25,25.5,26] (-1 if not measured).
[G,R,Z]_MAG_SB[22,22.5,23,23.5,24,24.5,25,25.5,26]_ERR float32 AB mag Uncertainty in [G,R,Z]_MAG_SB[22,22.5,23,23.5,24,24.5,25,25.5,26] ($1\sigma$).
[G,R,Z]_COG_PARAMS_MTOT float32 AB mag Best-fitting parameter $m_{1}$ based on the fit to the curve of growth (see the Ellipse-Fitting section).
[G,R,Z]_COG_PARAMS_M0 float32 AB mag Best-fitting parameter $m_{0}$ based on the fit to the curve of growth (see the Ellipse-Fitting section).
[G,R,Z]_COG_PARAMS_ALPHA1 float32   Best-fitting parameter $\alpha_{1}$ based on the fit to the curve of growth (see the Ellipse-Fitting section).
[G,R,Z]_COG_PARAMS_ALPHA2 float32   Best-fitting parameter $\alpha_{2}$ based on the fit to the curve of growth (see the Ellipse-Fitting section).
[G,R,Z]_COG_PARAMS_CHI2 float32   $\chi^{2}$ of the fit to the curve of growth (see the Ellipse-Fitting section).
ELLIPSEBIT int32   See the Bitmasks documentation.

TRACTOR

This binary table is row-matched to the ELLIPSE table in the preceding HDU and contains all the columns documented in the DR9 Tractor catalogs, supplemented (for convenience) with SGA_ID.

Note that all sources in this table have REF_CAT=="L3" and REF_ID is identical to SGA_ID, as described in the external catalogs documentation.

Group Files

For each galaxy group in the SGA-2020 (i.e., each row in SGA-2020.fits) we produce the set of files described in the Images and Catalogs table and the Custom Mosaics & Ellipse-Fitting documentation section.

These files are organized into the directory structure RASLICE/GROUP_NAME, where GROUP_NAME is the name of the galaxy group and RASLICE (000-359) is the one-degree wide slice of the sky that the object belongs to. Specifically, in Python:

RASLICE = '{:06d}'.format(int(GROUP_RA*1000))[:3]


Images and Catalogs

The table below documents the nominal set of files produced by the SGA pipeline. Many of these files are standard DR9 data products (see the DR9 files documentation), although they are based on slightly different inputs than those used for nominal DR9 processing (see Custom Mosaics for more details) and with names which are specific to the SGA.

File Description
DR9 Pipeline Catalogs
GROUP_NAME-ccds-[north,south].fits Input table of north or south CCDs used to generate the optical image stacks.
GROUP_NAME-largegalaxy-blobs.fits.gz Enumerated segmentation ("blob") image (see the metrics documentation); may be removed in future releases.
GROUP_NAME-largegalaxy-tractor.fits Tractor catalog of all detected sources in the field.
DR9 Pipeline Mosaics and Catalogs
GROUP_NAME-largegalaxy-outlier-mask.fits.fz Image of pixels rejecting during outlier masking (see the metrics documentation); may be removed in future releases.
GROUP_NAME-depth-[g,r,z].fits.fz Image of the $5\sigma$ point-source depth at each pixel (see also the DR9 image stacks documentation).
GROUP_NAME-largegalaxy-psf-[g,r,z].fits.fz Postage stamp of the inverse-variance weighted mean pixelized grz PSF at the center of the field (see the PSF documentation for more details).
GROUP_NAME-largegalaxy-[image,invvar,model]-[g,r,z].fits.fz Inverse-variance weighted image, inverse variance image, and Tractor model image based on the input grz imaging (see the DR9 image stacks documentation for more details).
GROUP_NAME-largegalaxy-[image,model,resid]-grz.jpg JPEG visualization of the data, model, and residual grz mosaics.
GROUP_NAME-[image,invvar]-[W1,W2,W3,W4].fits.fz Inverse-variance weighted image and inverse variance image based on the input W1-W4 imaging (see the DR9 image stacks documentation for more details). Note: there is no largegalaxy prefix because the data used to generate these files is independent of the SGA.
GROUP_NAME-largegalaxy-model-[W1,W2,W3,W4].fits.fz unWISE Tractor model W1-W4 mosaic based on the forced photometry technique used in DR9. Note that the largegalaxy prefix is present because the Tractor models used to generate this image rely on assumptions made specifically for the SGA.
GROUP_NAME-[image,model]-W1W2.jpg JPEG visualization of the data and model W1W2 mosaics.
SGA Pipeline Files
GROUP_NAME-largegalaxy-sample.fits Catalog of (one or more) galaxies from SGA-2020.fits belonging to this group.
GROUP_NAME-largegalaxy-SGA_ID-ellipse.fits See the Ellipse Fits data model; note that this file may be missing (for the galaxy of a given SGA_ID) if ellipse-fitting failed or is not carried out (see Bitmasks).
GROUP_NAME-coadds.log Logging output for the coadds stage of the pipeline; may be missing in some cases.
GROUP_NAME-ellipse.log Logging output for the ellipse stage of the pipeline; may be missing in some cases.

Ellipse Fits

We produce a single FITS table to store the ellipse-fitting results for each galaxy in the SGA-2020 which could be ellipse-fit (see the Ellipse-Fitting documentation for more details).

Many of the ellipse-fitting measurements are taken directly from the photutils.isophote.IsophoteList attributes, although in many cases the column names have been renamed for clarity.

Name Type Units Description
SGA_ID int64   See ELLIPSE data model.
GALAXY char[?]   See ELLIPSE data model.
RA float64 degree See ELLIPSE data model.
DEC float64 degree See ELLIPSE data model.
PGC int64   See ELLIPSE data model.
PA_LEDA float32 degree See ELLIPSE data model.
BA_LEDA float32   See ELLIPSE data model.
D25_LEDA float32 arcmin See ELLIPSE data model.
BANDS char[1][3]   List of bandpasses fitted (here, always g,r,z).
REFBAND char[1]   Reference band (here, always r).
REFPIXSCALE float32 arcsec/pixel Pixel scale in REFBAND.
SUCCESS Boolean   Flag indicating ellipse-fitting 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, in which case we adopt the Tractor model position.
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 BA given in the SGA-2020.fits table.
PA float32 degree Galaxy position angle (astronomical convention, measured clockwise from North); equivalent to PA in the SGA-2020.fits table.
THETA float32 degree Galaxy position angle (physics convention, measured clockwise from the x-axis), and given by [$(270-PA)$ mod 180].
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 (typically taken to be $2\times$ MAJORAXIS).
INTEGRMODE char[6]   photutils.isophote.Ellipse.fit_image integration mode (here, always median).
SCLIP int16   photutils.isophote.Ellipse.fit_image sigma-clipping (here, always 3).
NCLIP int16   Number of photutils.isophote.Ellipse.fit_image sigma-clipping iterations (here, always 3).
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 AB 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[N] pixel Semi-major axis position, where N is the total number of (pixel) samples along the semi-major axis.
[G,R,Z]_INTENS float32[N] $\mathrm{nanomaggies}/\mathrm{arcsec}^2$ Linear surface brightness at the semi-major axis position given by [G,R,Z]_SMA.
[G,R,Z]_INTENS_ERR float32[N] $\mathrm{nanomaggies}/\mathrm{arcsec}^2$ Uncertainty in [G,R,Z]_INTENS ($1\sigma$).
[G,R,Z]_EPS float32[N]   Ellipticity along the semi-major axis; here, taken to be fixed at the value given by EPS.
[G,R,Z]_EPS_ERR float32[N]   Uncertainty in [G,R,Z]_EPS ($1\sigma$).
[G,R,Z]_PA float32[N] degree Position angle along the semi-major axis; here, taken to be fixed at the value given by PA.
[G,R,Z]_PA_ERR float32[N] degree Uncertainty in [G,R,Z]_PA ($1\sigma$).
[G,R,Z]_X0 float32[N] pixel Pixel coordinate of the ellipse along the x-axis; here, taken to be fixed at the value given by X0.
[G,R,Z]_X0_ERR float32[N] pixel Uncertainty in [G,R,Z]_X0 ($1\sigma$).
[G,R,Z]_Y0 float32[N] pixel Pixel coordinate of the ellipse along the x-axis; here, taken to be fixed at the value given by Y0.
[G,R,Z]_Y0_ERR float32[N] pixel Uncertainty in [G,R,Z]_Y0 ($1\sigma$).
[G,R,Z]_A3 float32[N]   Third-order harmonic coefficient (see photutils.isophote.IsophoteList); not used.
[G,R,Z]_A3_ERR float32[N]   Uncertainty in [G,R,Z]_A3 ($1\sigma$).
[G,R,Z]_A4 float32[N]   Fourth-order harmonic coefficient (see photutils.isophote.IsophoteList); not used.
[G,R,Z]_A4_ERR float32[N]   Uncertainty in [G,R,Z]_A4 ($1\sigma$).
[G,R,Z]_RMS float32[N] $\mathrm{nanomaggies}/\mathrm{arcsec}^2$ Root-mean-square of the surface brightness along the elliptical path (see photutils.isophote.IsophoteList).
[G,R,Z]_PIX_STDDEV float32[N] $\mathrm{nanomaggies}/\mathrm{arcsec}^2$ Estimate of the pixel standard deviation along the elliptical path (see photutils.isophote.IsophoteList).
[G,R,Z]_STOP_CODE int16[N]   Fitting stop code (see photutils.isophote.IsophoteList and photutils.isophote.Isophote).
[G,R,Z]_NDATA int16[N]   Number of data points used for the fit (see photutils.isophote.IsophoteList).
[G,R,Z]_NFLAG int16[N]   Number of points rejected during the fit (see photutils.isophote.IsophoteList).
[G,R,Z]_NITER int16[N]   Number of fitting iterations (see photutils.isophote.IsophoteList).
[G,R,Z]_COG_SMA float32[M] pixel Do not use; see the Known Issues.
[G,R,Z]_COG_MAG float32[M] AB mag Do not use; see the Known Issues.
[G,R,Z]_COG_MAGERR float32[M] AB mag Do not use; see the Known Issues.
[G,R,Z]_COG_PARAMS_MTOT float32 AB mag Do not use; see the Known Issues.
[G,R,Z]_COG_PARAMS_M0 float32 AB mag Do not use; see the Known Issues.
[G,R,Z]_COG_PARAMS_ALPHA1 float32   Do not use; see the Known Issues.
[G,R,Z]_COG_PARAMS_ALPHA2 float32   Do not use; see the Known Issues.
[G,R,Z]_COG_PARAMS_CHI2 float32   Do not use; see the Known Issues.
RADIUS_SB[22,22.5,23,23.5,24,24.5,25,25.5,26] float32 arcsec Do not use; see the Known Issues.
RADIUS_SB[22,22.5,23,23.5,24,24.5,25,25.5,26]_ERR float32 arcsec Do not use; see the Known Issues.
[G,R,Z]_MAG_SB[22,22.5,23,23.5,24,24.5,25,25.5,26] float32 AB mag Do not use; see the Known Issues.
[G,R,Z]_MAG_SB[22,22.5,23,23.5,24,24.5,25,25.5,26]_ERR float32 AB mag Do not use; see the Known Issues.

The following tables document some of the bit-masks used in the SGA pipeline, as listed in the SGA-2020.fits catalog. The bits are enumerated as a power of two, e.g., in Python, the expression

'ELLIPSEBIT' & 2**1 != 0


would return a Boolean array of the objects fitted as type REX which were too small to be ellipse-fit.

ELLIPSEBIT

The following bits largely pertain to galaxies with DR9 imaging; they indicate why a given object in the SGA-2020.fits catalog was not ellipse-fit.

Bit Name Description
0   Not used; ignore.
1 REX_TOOSMALL Object was not ellipse-fit because it was classified as too-small type REX (see the Ellipse-Fitting section for details).
2 NOTREX_TOOSMALL Object was not ellipse-fit because it was classified as too-small type EXP, DEV, or SER (see the Ellipse-Fitting section for details).
3 FAILED Ellipse-fitting was attempted but failed. (In SGA-2020 no galaxies have this bit set.)
4 NOTFIT Ellipse-fitting was not attempted. (In SGA-2020 only 27 galaxies in seven unique groups have this bit set; see the Known Issues).
5 REJECTED Ellipse-fitting results were rejected (generally based on visual inspection; see the Known Issues).

Known Issues

Here, we document known issues regarding the SGA-2020. We will periodically update this section as additional issues are identified or reported and will endeavor to address these issues in future versions of the catalog.

• In SGA-2020, 27 galaxies in seven unique groups were not ellipse fit, generally due to the (very large!) size of the primary galaxy:
• In 52 galaxies identified through visual inspection, we determined that the ellipse-fitting results were incorrect or unreliable, usually due to incomplete or imperfect masking of nearby bright stars or other galaxies. In these systems we vetoed the ellipse-fitting results and set the REJECTED bit in the final catalog (see the Bitmasks table).
• The curve-of-growth measurements (as recorded in the [G,R,Z]_COG_MAG and [G,R,Z]_COG_MAGERR columns of the Ellipse Fits files) were unfortunately compromised by a coding bug and should not be used. However, we have remeasured most of the key quantities of interest (specifically, RADIUS_SB[22,22.5,23,23.5,24,24.5,25,25.5,26], RADIUS_SB[22,22.5,23,23.5,24,24.5,25,25.5,26]_ERR, [G,R,Z]_MAG_SB[22,22.5,23,23.5,24,24.5,25,25.5,26], and [G,R,Z]_MAG_SB[22,22.5,23,23.5,24,24.5,25,25.5,26]_ERR) from the surface-brightness profiles, which were not affected by the bug and stored the results in the SGA-2020.fits table.

Feedback

We welcome questions and feedback from users, as well as requests for additional data products through the ticket system at

We also acknowledge that all the code used to select, build, and analyze the catalog is open source and publicly available:

Future Plans

Future versions of the SGA will focus on four main areas:

1. Improving the completeness of the sample over the full footprint, particularly with respect to lower surface-brightness galaxies;
2. Improving the data reduction and analysis of the very largest (angular diameter) galaxies in the sky, like NGC5194=M51 and NGC5457=M101.
3. Better handling of galaxies with close companions (e.g., in the Coma Cluster) and near bright stars.
4. Measuring the infrared surface-brightness profiles based on the unWISE imaging at 3.4-22 microns.

Acknowledgments

Use of the SGA-2020 data products must acknowledge the Scientific Publication Acknowledgment for the DESI Legacy Imaging Surveys, as well as the specific SGA acknowledgment.

We also acknowledge important contributions to the SGA-2020 from the following individuals:

Finally, we gratefully acknowledge the following invaluable external resources which made this project possible: