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The SDSS-IV MaNGA Sample: Design, Optimization, and Usage The SDSS-IV MaNGA Sample: Design, Optimization, and Usage
Considerations Considerations
David A. Wake
The Open University, UK
Kevin Bundy
University of California - Santa Cruz
Aleksandar M. Diamond-Stanic
University of Wisconsin - Madison
Renbin Yan
University of Kentucky
, yanrenbin@uky.edu
Michael R. Blanton
New York University
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Repository Citation Repository Citation
Wake, David A.; Bundy, Kevin; Diamond-Stanic, Aleksandar M.; Yan, Renbin; Blanton, Michael R.; Bershady,
Matthew A.; Sánchez-Gallego, José R.; Drory, Niv; Jones, Amy; Kauffmann, Guinevere; Law, David R.; Li,
Cheng; MacDonald, Nicholas; Masters, Karen; Thomas, Daniel; Tinker, Jeremy; Weijmans, Anne-Marie; and
Brownstein, Joel R., "The SDSS-IV MaNGA Sample: Design, Optimization, and Usage Considerations"
(2017).
Physics and Astronomy Faculty Publications
. 487.
https://uknowledge.uky.edu/physastron_facpub/487
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The SDSS-IV MaNGA Sample: Design, Optimization, and Usage Considerations The SDSS-IV MaNGA Sample: Design, Optimization, and Usage Considerations
Digital Object IdentiEer (DOI)
https://doi.org/10.3847/1538-3881/aa7ecc
Notes/Citation Information Notes/Citation Information
Published in
The Astronomical Journal
, v. 154, no. 3, 86, p. 1-26.
© 2017. The American Astronomical Society. All rights reserved.
The copyright holder has granted the permission for posting the article here.
Authors Authors
David A. Wake, Kevin Bundy, Aleksandar M. Diamond-Stanic, Renbin Yan, Michael R. Blanton, Matthew A.
Bershady, José R. Sánchez-Gallego, Niv Drory, Amy Jones, Guinevere Kauffmann, David R. Law, Cheng Li,
Nicholas MacDonald, Karen Masters, Daniel Thomas, Jeremy Tinker, Anne-Marie Weijmans, and Joel R.
Brownstein
This article is available at UKnowledge: https://uknowledge.uky.edu/physastron_facpub/487
The SDSS-IV MaNGA Sample: Design, Optimization, and Usage Considerations
David A. Wake
1,2,3
, Kevin Bundy
4,5
, Aleksandar M. Diamond-Stanic
2,6
, Renbin Yan
7
, Michael R. Blanton
8
,
Matthew A. Bershady
2
, José R. Sánchez-Gallego
9
, Niv Drory
10
, Amy Jones
11
, Guinevere Kauffmann
11
,
David R. Law
12
, Cheng Li
13,14
, Nicholas MacDonald
9
, Karen Masters
15,18
, Daniel Thomas
15,18
,
Jeremy Tinker
8
, Anne-Marie Weijmans
16
, and Joel R. Brownstein
17
1
School of Physical Sciences, The Open University, Milton Keynes, MK7 6AA UK; david.wake@open.ac.uk
2
Astronomy Department, University of Wisconsin-Madison, Madison, WI 53706, USA
3
Department of Physics, University of North Carolina Asheville, One University Heights, Asheville, NC 28804, USA
4
Dept. of Astronomy and Astrophysics, UC Santa Cruz, MS: UCO/LICK, 1156 High St, Santa Cruz, CA 95064, USA
5
Kavli IPMU (WPI), UTIAS, The University of Tokyo, Kashiwa, Chiba 277-8583, Japan
6
Department of Physics and Astronomy, Bates College, 44 Campus Avenue, Carnegie Science Hall, Lewiston, Maine 04240, USA
7
Department of Physics and Astronomy, University of Kentucky, 505 Rose Street, Lexington, KY 40506-0057, USA
8
Center for Cosmology and Particle Physics, Department of Physics, New York University, 4 Washington Place, NY 10003, New York, USA
9
Department of Astronomy, Box 351580, University of Washington, Seattle, WA 98195, USA
10
McDonald Observatory, University of Texas at Austin, 1 University Station, Austin, TX 78712-0259, USA
11
Max-Planck Institut für Astrophysik, D-85741 Garching, Germany
12
Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218, USA
13
Department of Physics and Tsinghua Center for Astrophysics, Tsinghua University, Beijing 100084, China
14
Shanghai Astronomical Observatory, Nandan Road 80, Shanghai 200030, China
15
Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth, UK
16
School of Physics and Astronomy, University of St Andrews, North Haugh, St Andrews KY16 9SS, UK
17
Department of Physics and Astronomy, University of Utah, 115 S. 1400 E., Salt Lake City, UT 84112, USA
Received 2017 April 27; revised 2017 July 7; accepted 2017 July 7; published 2017 August 4
Abstract
We describe the sample design for the SDSS-IV MaNGA survey and present the final properties of the main
samples along with important considerations for using these samples for science. Our target selection criteria were
developed while simultaneously optimizing the size distribution of the MaNGA integral field units (IFUs), the IFU
allocation strategy, and the target density to produce a survey defined in terms of maximizing signal-to-noise ratio,
spatial resolution, and sample size. Our selection strategy makes use of redshift limits that only depend on i-band
absolute magnitude (M
i
), or, for a small subset of our sample, M
i
and color (NUV − i). Such a strategy ensures that
all galaxies span the same range in angular size irrespective of luminosity and are therefore covered evenly by the
adopted range of IFU sizes. We define three samples: the Primary and Secondary samples are selected to have a flat
number density with respect to M
i
and are targeted to have spectroscopic coverage to 1.5 and 2.5 effective radii
(R
e
), respectively. The Color-Enhanced supplement increases the number of galaxies in the low-density regions of
color–magnitude space by extending the redshift limits of the Primary sample in the appropriate color bins. The
samples cover the stellar mass range
*
´´
-
MMh
5
10 3 10
8112
and are sampled at median physical
resolutions of 1.37 and 2.5 kpc for the Primary and Secondary samples, respectively. We provide weights that will
statistically correct for our luminosity and color-dependent selection function and IFU allocation strategy, thus
correcting the observed sample to a volume-limited sample.
Key words: galaxies: evolution – galaxies: general – galaxies: statistics – surveys
1. Introduction
The SDSS-IV MaNGA survey (Bundy et al. 2015; Blanton
et al. 2017) is using the ARC 2.5 m telescope (Gunn et al.
2006) and the BOSS spectrographs (Smee et al. 2013) with its
fibers bundled into multiple integral field units (IFUs; Drory
et al. 2015) to measure spatially resolved spectroscopy of
∼10,000 nearby galaxies. We have chosen to target a well-
defined sample that has uniform spatial coverage in units of
r-band effective radius along the major axis (R
e
), and an
approximately flat stellar mass distribution with
*
M10
9
-
Mh 10
211
. In this paper, we discuss the motivation and
methodology of the MaNGA sample selection, and we present
the resulting sample in a way that allows for its use in statistical
analysis of galaxy properties.
The challenge of designing a survey like MaNGA is to
balance the need for sample size, spatial coverage, and spatial
resolution; these three parameters compete with each other for
finite fiber resources. We have chosen a sweet spot in this
multi-parameter space that best matches our science require-
ments (outlined in Bundy et al. 2015; Yan et al. 2016) in the
context of a six-year survey duration, existing spectrographs,
and telescope field of view. Since the sample design and the
modifications to the BOSS spectrographs’ fiber feeds (Drory
et al. 2015) occurred concurrently, we were able to optimize
both together to a considerable degree. Specifically, we
determined the optimal IFU size complement within the
confines of a total fiber budget and viable sample design.
Fortuitously, the redshift range
z
0
.02 0.1
that balances
angular size versus resolution also delivers a target surface
density that is well matched to the telescope field of view
(3 degrees in diameter) and the roughly 1500 fibers with 2″
diameters of MaNGA’s feed to the BOSS spectrographs. While
we had not foreseen how well matched the telescope and
instrument “grasp” were to our optimized target density, in
The Astronomical Journal, 154:86 (26pp), 2017 September https://doi.org/10.3847/1538-3881/aa7ecc
© 2017. The American Astronomical Society. All rights reserved.
18
SEPnet, South East Physics Network (www.sepnet.ac.uk).
1
hindsight it is a lesson learned for planning future surveys. One
of the aims of this paper is to demonstrate how, with adequate
knowledge of target density, well-matched instrumentation can
be optimally configured to achieve well-motivated survey
science requirements.
A number of our design choices, such as an even sampling in
stellar mass, roughly uniform radial coverage, and a sample
size in the thousands, are similar in spirit to those of the SAMI
survey (Croom et al. 2012; Bryant et al. 2015). Such choices
result naturally from a desire to efficiently study the local
galaxy population and produce several similar features in the
sample selection approach, such as a stellar mass-dependent
redshift range. However, our ability to simultaneously design
the IFU size distribution and sample selection using a telescope
with a larger field does offer further advantages for
optimization.
1.1. Design Strategy
A number of strategic and tactical choices inform technical
elements of the sample design. A starting point was to select
from the well-understood SDSS Main Sample (Strauss et al.
2002) with enhanced redshift completeness and remeasured
photometry, as described in Section 2. Because the redshifts
and global properties of SDSS galaxies are well known, the
distributions of these properties in the final MaNGA sample
can be carefully constructed by effectively weighting the
MaNGA selection in order to maximize its scientific utility.
1. Sample size: Paramount is the requirement for a large,
statistically powerful sample size, a choice that comes at
the expense of higher quality data for individual galaxies
within the sample. As described in Bundy et al. (2015),
the specific argument for sampling 10,000 galaxies arises
from the desire to divide galaxies into 6
3
groups of ∼50
galaxies each. These groups, or bins (i) sample each of
three “principal components” defining galaxy populations
—stellar mass, SFR, and environment; ( ii) divide each
“dimension” into six bins, sufficient to distinguish the
functional form of trends across each dimension; and
finally (iii) contain adequate counting statistics (galaxies)
such that differences in mean properties between bins can
be detected at the five-sigma level even when the
measurement precision for individual galaxies is compar-
able to this difference. This optimization dovetails
MaNGA’s scientific goals for statistical analyses of
resolved galaxy samples, and complements existing,
smaller data sets such as ATLAS3D (Cappellari et al.
2011), DiskMass (Bershady et al. 2010), and CALIFA
(Sánchez et al. 2012), as well as forthcoming data from
instruments such as MUSE (Bacon et al. 2010) and
KCWI (Martin et al. 2010) capable of producing even
higher fidelity data for more modest samples.
2. Sampling in stellar mass: We desire the MaNGA sample
to have a roughly flat distribution in
*
Mlog
so that studies
of mass-dependent trends could make use of adequate
numbers of high-mass galaxies compared to more
numerous low-mass systems. A flat stellar mass distribu-
tion requires an upper redshift limit that is stellar mass
dependent, so a larger volume is sampled for rarer high-
mass galaxies.
3. Radial coverage: We desire roughly uniform radial
coverage as defined by some multiple of the effective
radius. This choice is motivated by the existence of well-
known scaling relations that emphasize the importance of
the relative length scale of galaxy stellar density profiles.
Uniform spatial coverage in units of R
e
requires a lower
redshift limit that is stellar mass dependent, so larger
more massive galaxies have the same angular size as
smaller lower mass galaxies. MaNGA therefore samples
the same relative extent of the declining surface bright-
ness profile, but at the cost of not maintaining the same
physical spatial resolution across the sample.
4. Maximize spatial resolution and signal-to-noise ratio
(S/N): With current facilities, we wish to build a data set
of IFU spectroscopy for 10,000 galaxies with the
maximum possible per galaxy physical spatial resolution,
spectral coverage and resolution, and S/N per spatial
element. These requirements lead to several inevitable
tactical features of the selection criteria:
(a) to maximize the spatial resolution and total S/N
requires the selection of galaxies at as low redshift as
possible.
(b) to reach our goal of 10,000 galaxies requires a
sufficiently broad redshift distribution so as to have
enough galaxies per plate to maximize efficiency in
IFU allocation.
1.1.1. Subsamples
The question of how to set the target radius motivated
significant thought during the sample design. Smaller multiples
of the effective radius would yield greater spatial resolution and
more spatial samples with higher S/N. Larger radial coverage
would contain more of the galaxy’s light, reach into the dark-
matter-dominated regime, and probe unchartered territory in
the outskirts of galaxies. After studying a number of options, a
compromise was reached to cover out to
R1.5
e
(the majority of
the light distribution) for two-thirds of the sample and to cover
out to
R
2
.5
e
for one-third of the sample. Going to larger radii,
while compelling, was deemed too costly in terms of the
number of spatial samples per IFU with very low S/N. The
sample split was motivated by basic binning arguments (see
Bundy et al. 2015) and officially adopted by the science team
after the first year of observations.
With the main sample roughly flat in
*
Mlog
, it was possible
to consider a further optimization, that is, balancing the rest-
frame color distribution (a proxy for star formation rate) at
fixed M
*
. In this way, rare populations of star-forming massive
galaxies and non-star-forming low-mass galaxies could be
upweighted in the final sample. The primary objection was a
concern that unexpected biases could be introduced into the
sample and more generally that the selection would become
unnecessarily complicated. As described below, a practical
solution was discovered, however, that helps balance the color
distribution through an additional and modest “Color-Enhanced
supplement.” Should it prove biased or undesirable, the
supplemental sample could be easily separated from the
Primary sample, and in the worst-case scenario, even ignored.
With the risk mitigated, the decision was made to include the
Color-Enhanced supplement in the selection.
To summarize, the final full MaNGA sample with which we
began the survey consists of three main subsamples. The
Primary sample, which will initially make up 50% of the
targets, is designed to be covered by our IFUs to 1.5 R
e
and has
2
The Astronomical Journal, 154:86 (26pp), 2017 September Wake et al.
a flat distribution in K-corrected i-band absolute magnitude
(M
i
). The Secondary sample, making up 33% of the initial
targets, is again designed to have a flat distribution in M
i
but
with coverage to 2.5 R
e
. Finally, the Color-Enhanced
supplement is designed to add galaxies in regions of the
NUV−i versus M
i
color–magnitude plane that are under-
represented in the Primary sample, such as high-mass blue
galaxies and low-mass red galaxies, and will make up 17% of
the initial targets. The combination of the Primary and Color-
Enhanced samples is called the Primary+ sample.
This complexity leads to the final strategic choice in the
survey design:
5. Selection simplicity: While we have described the basic
strategic and tactical motivations behind various choices
for the sample design, we were also driven to make the
selection as simple and reproducible as possible. The
implementation of the “weighting” described above to
deliver a MaNGA sample with desired global distribu-
tions is carried out entirely through a set of selection
criteria involving basic observables that are relatively
model independent: redshift, i-band luminosity, and, for
the Color-Enhanced supplement, (NUV − i) color. Note
that the selection does not depend on effective radius
explicitly (although a radius estimate is used when
choosing what sized IFU to allocate to given galaxy
target). We also emphasize that while much of the sample
design studies made use of M
*
estimates, the final
selection employs i-band absolute magnitudes as a proxy
for M
*
.
19
We did not use M
*
estimates specifically in
order to avoid potential systematic biases and the use of a
“black-box” estimator that may be difficult to reproduce.
1.2. Extant Instrumentation
Various aspects of the sample design are dependent on the
nature of the MaNGA instrumentation. We highlight a few
details here and refer to Drory et al. (2015) for more details.
The MaNGA instrumentation suite is composed of fiber-
bundle IFUs dedicated to observing galaxy targets, with a
number of additional IFUs and single fibers reserved for
calibration. The total number of fibers, 1423, is limited by the
size of the inherited BOSS spectrographs. The science IFUs
contain circular, buffered optical fibers tightly arranged in a
hexagonal format. This geometry enables IFUs of different
sizes, with specific numbers of fibers for each IFU size. With a
“live-core” fiber diameter of 2″ and full outer diameter of 2
5,
the smallest of the science IFUs contains a central fiber and two
outer, hexagonal rings for a total of 19 fibers and long-axis IFU
diameter of 12
5. Other possible IFU sizes are 37 fibers
(17
5),61fibers (22 5),91fibers (27 5), 127 fibers (32 5),
169 fibers (37
5), 217 fibers (42 5), and so on. Choosing the
largest IFU size as well as the optimal distribution of IFU sizes
is a major focus of this paper.
Identical sets of the MaNGA instrumentation suite are
installed in six SDSS “cartridges.” These sturdy, cylindrical
structures house the light-collecting IFUs and fibers, the field-
specific plug plate, and the output pseudo-slit, which is directed
into the spectrographs when the cartridge is mounted on the
telescope. The ferrules and jacketing of single fibers and
MaNGA IFUs are similar, with dimensions that facilitate hand-
plugging of these elements into pre-drilled plates. As a result
there is a “ collision radius” that defines the minimum distance
between plugged elements. For the MaNGA IFUs this distance
is 120″. The mounting of the plate in the cartridge makes use of
a post that attaches to the center of the plate helping to deform
the plate to the shape of the focal plane. This center post
introduces a second “collision radius” about the center of the
plate of 150″.
The balance of this paper is organized as follows: In
Section 2 we describe the construction of the parent catalogs
from the NASA-Sloan Atlas (NSA). In Section 3.1 we describe
the process by which we select the upper and lower redshift
cuts for our Primary and Secondary samples. In Section 3.2 and
Section 3.3 we describe the methodology that we use to
optimize the IFU size distribution. In Section 3.4 we describe
how we select the sample space density. In Section 4 we
describe the results of applying these processes, the selection of
the Color-Enhanced Supplement, and the properties of the final
samples. In Section 5 we describe how we tile the survey area
and allocate IFUs to the targets. In Section 6 we discuss how to
use the sample for statistical analyses of MaNGA data.
Where applicable we use a flat Lamda-CDM cosmology
with
W=0.
3
M
and
=
--
H
70 km s Mpc
0
11
except for absolute
magnitudes and stellar mass, which are calculated assuming
=
--
H
h100 km s Mpc
0
11
with h=1, following previous
versions of the NSA.
2. Parent Catalogs
The primary input for the selection of all MaNGA galaxies is
an enhanced version of the NSA (Blanton M. http://www.
nsatlas.org). The NSA is a catalog of nearby galaxies within
200 Mpc (z ;0.055), primarily based on the SDSS DR7 MAIN
galaxy sample (Abazajian et al. 2009), but incorporating data
from additional sources. The SDSS imaging has been
reprocessed to be better suited to the analysis of these large
nearby galaxies (Blanton et al. 2011). In particular it has
improved background subtraction and deblending more suited
to nearby large galaxies resulting in more accurate size and
luminosity measurements for such galaxies. In addition to a
reanalysis of the SDSS imaging, a similar analysis is applied to
the GALEX near- and far-UV images, and several derived
parameters, such as K-corrections and absolute magnitudes
20
(using kcorrect v4_3), Sérsic profile fits, and stellar masses are
determined.
The NSA also provides a
~30%
improvement in spectro-
scopic completeness over the standard SDSS spectroscopic
catalog for the very brightest sources by adding redshifts from
the NASA Extragalactic Database (NED
21
), the CfA Redshift
Survey (ZCAT;
22
Huchra & Geller 1991), the Arecibo Legacy
Fast ALFA Survey (ALFALFA; Giovanelli et al. 2005), the
2dF Galaxy Redshift Survey (2dF; Colless et al. 2001), and the
6dF Galaxy Redshift Survey (6dF; Jones et al. 2009). The
SDSS is 70% complete at
~
r
1
4
AB
and 95% complete at
~
r
16
AB
, emphasizing the importance of these other redshift
19
For the initial IFU size distribution optimization process we used the stellar
mass as estimated by the kcorrect code (Blanton & Roweis 2007) applied to the
five-band SDSS photometry. For the final samples we have switched to using
just i-band absolute magnitude in order to simplify the selection function (see
Section 4.1).
20
K-corrections in the NSA catalog do not account for extinction explicitly,
and make no attempt to apply an inclination-dependent extinction correction.
21
https://ned.ipac.caltech.edu/
22
https://www.cfa.harvard.edu/~dfabricant/huchra/zcat/
3
The Astronomical Journal, 154:86 (26pp), 2017 September Wake et al.
