Mon. Not. R. Astron. Soc. 000, 000–000 (0000)

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arXiv:1406.4867v1 [astro-ph.GA] 18 Jun 2014

The 6dF Galaxy Survey: Fundamental Plane Data
Lachlan A. Campbell1 , John R. Lucey2? , Matthew Colless1,3 , D. Heath Jones1,4 ,Christopher M. Springob1,5,6 , Christina Magoulas1,7 Robert N. Proctor8 ,Jeremy R. Mould7,9 , Mike A. Read10 , Sarah Brough1 , Tom Jarrett11,12 ,Alex I. Merson2,13 , Philip Lah3 , Florian Beutler5,14 , Michelle E. Cluver1,11 ,andQuentin A. Parker1,15
1Australian Astronomical Observatory, PO Box 915, North Ryde, NSW 1670, Australia

2 Department of Physics, Durham University, Durham DH1 3LE, UK
3 Research School of Astronomy & Astrophysics, The Australian National University, Canberra, ACT 2611, Australia
4 School of Physics, Monash University, Clayton, VIC 3800, Australia
5 International Centre for Radio Astronomy Research, The University of Western Australia, Crawley, WA 6009, Australia
6 ARC Centre of Excellence for All-sky Astrophysics (CAASTRO)
7 School of Physics, University of Melbourne, Parkville, VIC 3010, Australia
8 Observatório Nacional, Rua Gal. José Cristino 77, 20921-400 Rio de Janeiro, Brasil
9 Centre for Astrophysics & Supercomputing, Swinburne University of Technology, PO Box 218, Hawthorn, VIC 3122, Australia
10 Institute for Astronomy, School of Physics and Astronomy, Royal Observatory, Blackford Hill, Edinburgh EH9 3HJ, UK
11 Spitzer Science Center, California Institute of Technology, Pasadena, CA 91125, USA
12 Astronomy Department, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa
13 Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, UK
14 Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
15 Department of Physics & Astronomy, Macquarie University, Sydney, NSW 2109, Australia

20 June 2014

ABSTRACTWe report the 6dFGS Fundamental Plane (6dFGSv) catalogue that is used to estimate distances and peculiar velocities for nearly 9 000 early-type galaxies in the local (z<0.055) universe. Velocity dispersions are derived by cross-correlation from 6dF V-band spectra withtypical S/N of 12.9 Å−1 for a sample of 11 315 galaxies; the median velocity dispersion is
163 km s−1 and the median measurement error is 12.9%. The photometric Fundamental Plane(FP) parameters (effective radii and surface brightnesses) are determined from the JHK
2MASS images for 11 102 galaxies. Comparison of the independent J- and K-band measurements implies that the average uncertainty in XF P , the combined photometric parameterthat enters the FP, is 0.013 dex (3%) for each band. Visual classification of morphologies wasused to select a sample of nearly 9 000 early-type galaxies that form 6dFGSv. This catalogue
has been used to study the effects of stellar populations on galaxy scaling relations, to investigate the variation of the FP with environment and galaxy morphology, to explore trends instellar populations through, along and across the FP, and to map and analyse the local peculiar
velocity field.
Key words: galaxies:fundamental parameters, galaxies: elliptical and lenticular, galaxies:
evolution, galaxies: structure

1

INTRODUCTION

The primary goals of the 6-degree Field Galaxy Survey (6dFGS;
Jones et al. 2004, 2005, 2009) were, first, to improve our knowledge of the cosmography of the nearby universe over the southern?

Email: john.lucey@durham.ac.uk

c 0000 RAS

hemisphere and, secondly, to provide a sample of ∼10 000 nearby
early-type galaxies in order to study their intrinsic properties and tomeasure peculiar velocities via the determination of Fundamental
Plane distances.
An important aspect of 6dFGS is that all observations were obtained on a single system, the UK Schmidt Telescope (UKST) andthe 6-degree Field spectrograph (6dF; Watson, Parker & Miziarski

2Campbell et al.

1998), resulting in a homogeneous spectroscopic dataset. The 6dF
system provided 150 optical fibres, each with a 6.7 arcsec diameter
aperture, that were deployed across a field of view of nearly 6 degrees. 6dFGS covers the entire southern hemisphere with |b|>10◦ .
The primary galaxy sample for the 6dFGS was selected in the Ks
band from the Two Micron All Sky Survey (2MASS; Jarrett et al.
2000). Near-infrared (NIR) selection means the primary sample
provides a relatively unbiased (i.e. approximately mass-selected)
survey of galaxies with old stellar populations in the nearby universe. Over the period of the 6dFGS observations, from 2001 to
2006, survey observations produced approximately 137 000 spectra. The 6dFGS database (available at www-wfau.roe.ac.uk/6dfgs)
contains these spectra along with 124 000 galaxy redshifts and the
2MASS photometry and images. The median redshift is 0.05 andthe redshift completeness of 6dFGS is 88%; full details are given
in Jones et al. (2009).
As well as the galaxy redshift survey, 6dFGS had two further
major aims that focussed on the early-type galaxies.
The first aim was to provide an NIR-selected spectroscopic
and photometric sample with which to study the Fundamental Plane(FP), the scaling relation linking the velocity dispersion, effective
radius and surface brightness of early-type galaxies (Dressler et al.
1987; Djorgovski & Davis 1987). The 6dFGS subsample of earlytype galaxies with measured FP parameters has been used to investigate the physical origins of the FP, and its implications forgalaxy formation and evolution, by exploring variations in the FP
with galaxy and environmental properties, including stellar population and morphology (see Proctor et al. 2008; Magoulas et al. 2012;
Springob et al. 2012).
The second aim was to compile a large homogeneous peculiar
velocity catalogue derived from NIR FP distances in order to map
the nearby large-scale mass distribution for cosmological studies.
Peculiar velocities are a direct tracer of the underlying distributionof mass in the universe, so a combined redshift and peculiar velocity survey over the same volume can provide even better constraints
on parameters of cosmological interest than a survey of redshifts
alone (Burkey & Taylor 2004; Zaroubi & Branchini 2005). Several
recent reconstructions of the peculiar velocity field in the southern hemisphere have used the redshifts provided by 6dFGS (e.g.
Erdoǧdu et al. 2006; Lavaux et al. 2010; Lavaux & Hudson 2011).
While the first studies of FP-based peculiar velocities usedvelocity dispersions that were measured individually (e.g. Davies
et al. 1987; Lynden-Bell et al. 1988), the advent of multi-object
fibre systems dramatically improved the observing efficiency formeasuring velocity dispersions, particularly when studying galaxies in rich clusters (e.g. Colless & Hewett 1987; Lucey & Carter
1988; Jorgensen, Franx & Kjaergaard 1995b). The 6dFGS peculiar velocity survey (6dFGSv) was designed to build on the success of these first fibre-based studies and provide a large set of
FP peculiar velocities over the whole of the southern hemisphere.
The 6dFGSv sample was selected from the brightest (and so highest spectroscopic S/N) ellipticals, lenticulars and early-type spiralbulges in the primary 6dFGS redshift sample over the volume out
to ∼16 500 km s−1 .
Here we present the 6dFGSv dataset. The structure of the paper is as follows. In Section 2 we describe in detail the procedures
used to determine velocity dispersions of 11 315 early-type galaxies and provide a comparison with external catalogues. In Section 3
we outline the techniques we adopted to measure PSF-corrected
FP photometric parameters (Re and hµe i) from the 2MASS image tiles and demonstrate that these new NIR measurements are inexcellent agreement with values previously derived from NIR and

optical data. In Section 4 we describe the visual classification of the
6dFGSv galaxy morphologies, which is used to restrict the sample
to those objects likely to yield reliable FP results. The constructionof the 6dFGSv catalogue that is used in our FP studies is reported in
Section 5. Finally, we present some brief conclusions and highlightongoing studies in Section 6.

2
2.1

SPECTROSCOPIC MEASUREMENTS
6dF spectra and sample selection

The main characteristics of the 6dFGS, including the 6-degree Field(6dF) instrument, target catalogue construction, the allocation offields and fibres to targets, data reduction, and procedures for redshift determination, are reported in Jones et al. (2004). Briefly, 6dF
uses 6.7 arcsec diameter fibres to observe 150 spectra simultaneously covering two spectral ranges; the ’V-band’ data cover 3900–
5600 Å and the ‘R-band’ data cover 5400–7500 Å. When estimating redshifts, these two bands were spliced together to maximize
the signal. However, to avoid problems with discontinuities anddispersion changes only the V-band spectra were used to estimate
velocity dispersions. The pixel scale was 1.64 Å and the average 1σspectral resolution was ∼140 km s−1 .
Not all the ∼136 000 galaxy spectra of the full 6dFGS redshift survey are suitable for velocity dispersion measurements, andwe applied a number of criteria in order to define the 6dFGSv sample. We only selected galaxies with the higher quality redshift flags,
Q = 3–5, see Jones et al. (2004). This ensured the selected spectra had reliable redshifts before their use for velocity dispersion
information. At redshifts greater than 16 500 km s−1 the importantstrong Mgb feature starts to be affected by the red wavelength cutoff of the 6dF V-band spectra; hence we excluded galaxies with
redshifts greater than 16 500 km s−1 . Further selection was madeby spectral classification, based on the best-match template spectra
identified in the redshift estimation. This was initially based on the
R-value returned from our cross-correlation analysis (see Section2.2) which reflected how closely the galaxy spectrum matched thatof our K-giant star templates. We found that spectra with a R-valueof 8 had a velocity dispersion uncertainty of ∼0.12 dex. Hence asa practical choice we used a R > 8 criteria to select spectra thatgave sufficiently reliable velocity dispersion measurements for ourstudy. These various selection criteria restricted the total sample forvelocity dispersion measurements to ∼18 000 galaxies.
While the above filtering process excluded spectra with strong
emission lines, about ∼690 spectra had weak emission features.
All spectra were visually checked and poorly subtracted sky-lines,
foreground Hβ emission lines and weak galaxy emission lines (e.g.
Hβ, [OIII] 4959/5007) were removed by local interpolation. We
tested the sensitivity of our interpolation procedure by running analternative pixel-based analysis in which the pixels that were previously modified by the interpolation were flagged as invalid. The derived velocity dispersions changed, on average, by only 1 km s−1 ;the rms scatter of the difference was 15 km s−1 . On average, theremoval of the weak emission features by the local interpolation
technique increased the R-value by 3 and changed the derived velocity dispersions by –7 km s−1 with an rms scatter of 17 km s−1 .

2.2

Velocity dispersion measurements

Our analysis of the 6dF V-band spectra was undertaken in IRAF
using the RV package. The fxcor routine, which implements thec 0000 RAS, MNRAS 000, 000–000

6dFGS Fundamental Plane Data

3

Table 1. 6dFGSv stellar templates used with fxcor.
Name

Type

HD 223679
HD 813
HD 225166
CD-45 15239
HD 214
HD 224420
HD 321G8 III
G8/K0 IV
G8/K0 IV
K0
K1 IIIK1 IIIK2 III

Velocity
km s−1

Plate

Fibre

FWHM
Å

+4
+24
+123
+39
+63
+66
+39

2
2
1
2
1
1
2

62
109
86
101
130
116
137

5.672
5.211
5.365
5.107
5.459
5.212
5.154

method of from Tonry & Davis (1979), hereafter TD79, was used
for the velocity dispersion determination. Spectra of a set of template stars ranging in stellar type from G8 to K2 giants were observed with 6dF for the analysis (see Table 1).
To prepare for cross-correlation, the spectra were shifted toa rest-frame wavelength based on the redshift calculated in runz
(Colless et al. 2001; Saunders, Cannon & Sutherland 2004) and rebinned to a log-wavelength scale. Spectra were truncated to havea minimum wavelength of 4000 Å, and a maximum limit depending upon the redshift of the galaxy; this ranged from 5570 Å at z=0
to 5280 Å at z=0.055. Cropping the spectra in this way gave themaximal coverage of desired spectral features such as Mg and Fe
lines, while excluding problematic features such as the night sky
line residuals of [OI] 5577 and intrinsically broad Ca H & K spectral lines. The spectral continuum was then subtracted and 5% of
the pixels at each end of the spectra were apodized to prevent ringing in the correlation. The spectra were zero-padded and converted
to Fourier space, then filtered with a ramp filter to remove any highfrequency noise or low-frequency power due to continuum residuals and the apodisation.
The galaxy spectra were correlated with the templates, and thelargest peak in Fourier space was fit over a range of eleven pixels
using a Gaussian function to determine the centre and FWHM. The
FWHM was converted to an estimated galaxy velocity dispersion
using the empirically established response, for each template, of
the cross-correlation peak to velocity broadening. The final velocity dispersion was the average of all templates, weighted by thestrength of the cross-correlation, R.
As an independent test of our velocity dispersion measurement procedure we used the spectra from the MILES library(Sánchez-Blázquez et al. 2006) which have a FWHM spectral resolution of 2.54 Å, i.e. a factor of two lower than the 6dF dataset. We
selected six stars with spectral types ranging from G8III to K1III.
These were broadened to a range of velocity dispersions, degradedto a range of S/Ns and matched to the spectral range and resolution
characteristics of the 6dF dataset. For each input velocity dispersion and S/N we produced a set of ∼1000 mock spectra and thesewere processed with our pipeline using the templates listed in Table 1. The systematic differences between the input velocity dispersions and those recovered are presented in Figure 1. The systematic
biases are typically less than 0.02 dex. At lowest S/N the biases increase to 0.04 dex. Our fxcor-based pipeline best recovers the input velocity dispersions from the spectra constructed from the two
K0III stars as this is the average spectral type of our templates.
The 6dF instrument, like many earlier multi-fibre spectrographsystems (e.g. WYFFOS, see Moore et al. 2002), has an instrumental profile that varies non-linearly both along the spectrum from asingle fibre and from fibre-to-fibre (see Figure 2). Also, over thesix years of the 6dF survey, the resolution showed variations due
c 0000 RAS, MNRAS 000, 000–000

Figure 1. Our fxcor-based pipeline systematics. The log ratio of the input
and the recovered velocity dispersions is plotted against S/N. The input velocity dispersions tested were 100, 130, 160, 190, 220, 250 and 280 km s−1 .
The black lines are the average values. The grey lines show the individual
velocity dispersions; for clarity, these are only plotted for the first star listed
in each panel.

to causes such as spectrograph focus and fibre degradation. The
average 6dF spectral resolution was 140 km s−1 with 95% of fibres having mid-wavelength resolutions between 128 km s−1 and150 km s−1 . Hence there were, in some cases, significant differences in resolution between the template and object spectra. It is
implicit in the TD79 method that the instrumental resolution of thetemplate spectrum and the galaxy spectrum be identical, so that
µ2 = σ 2 + 2τ 2 ,(1)

where µ is the width of the cross-correlation peak, τ is the dispersion due to instrumental resolution, and σ is the dispersion due
to velocity broadening. The fact that this was not always the casewould therefore result, unless corrected, in biased velocity dispersion estimates.
Any difference in resolution between template and object
spectra (∆τ ) is simply added in quadrature to the cross-correlation

4

Campbell et al.

Figure 2. Instrumental spectral FWHM of V-band spectra for a subset
of 6dF fibres. The instrumental FWHM were measured from multiple
HgCdHe arc spectra observations and fitted with a third-order polynomial.
The dashed line shows the median resolution as a function of wavelength.
A FWHM of 5 Å corresponds to a 1σ resolution of 127 km s−1 .

peak width. However, prior to the cross-correlation the spectra arefiltered and, as seen in Figure 1 of TD79, this changes the relationship between the width of lines in the spectra, τ , and the width ofthe peak, µ. To properly correct for differences in resolution wemeasured the change in µ as a function of ∆τ . Auto-correlation ofa stellar template was performed over a range of τ values equivalent to adding in quadrature template and object spectra with differences in resolution of between −40 km s−1 and +40 km s−1 . Theµ values responded in a linear fashion to changes in τ , and were
essentially independent of the stellar template and the wavelength
range of the spectra. Based on these results, a look-up table was
generated giving the fits as a function of ∆τ , from which a corrected cross-correlation peak width µ could be obtained. A zerothorder estimation of the resolution of the template and object spectra, centred roughly around 5200 Å (the major source of signal in
the Fourier cross-correlation comes from the Mg and Fe lines inearly-type galaxies), gave ∆τ for each observation, and the corresponding µ was then converted to a resolution-corrected velocity
dispersion using the normal template calibration curve.
Twilight exposures (skyflats), which result in high S/N solar
spectra being observed in each fibre, provided a useful test of ourresolution correction procedure. The source spectrum is in each fibre is identical, but the different fibres and positions on the camera
mean the resolution naturally varies. We selected three fibre spectrawith spectral resolutions of 126, 140 and 166 km s−1 to act as the
spectral templates. All the twilight spectra were then broadened by100, 150 and 200 km s−1 to simulate the galaxy observations. We
then applied our resolution correction procedure and confirmed thatthe input broadening was recovered. Figure 3 shows the results ofusing the 166 km s−1 resolution fibre as the template with the twilight sky spectra broadened by 150 km s−1 , and demonstrates thevalidity of our procedure.
As a further independent test of our resolution correction, we
examined 120 galaxies that also had velocity dispersion measure-

Figure 3. Skyflat velocity dispersions for 6dF fibre #4 before and after applying a resolution correction. The distributions of the differences between
skyflat velocity dispersions before (the shaded histogram) and after (thehatched histogram) applying a resolution correction for the case of an applied broadening of 150 km s−1 and a template with a spectral resolutionof 166 km s−1 . This is the case with the poorest resolution and hence thelargest correction.

ments from SDSS. We divided these galaxies into sub-samples
with lower and higher 6dF resolution (τ < 135 km s−1 and τ >145 km s−1 ). The comparison confirmed the existence of the resolution bias, and validated the correction procedure. It also demonstrated that the correction introduced no appreciable increase in
scatter (610 km s−1 ).
Consequently, the resolution correction using this method
were applied to all the 6dFGS velocity dispersion measurements.
Figure 4 shows the scale of these corrections, as a function of thedifference in resolution between template and object spectra (∆τ ),for the velocity dispersion sample. The figure clearly illustrates
how the correction had a much more significant impact for lower
velocity dispersion measurements. Moreover, the correction is simple, well-behaved at all velocity dispersions, and straightforward,
supporting the adoption of the correction procedure.
While many previous FP peculiar velocity surveys have usedthe cross-correlation technique as implemented in fxcor to measure velocity dispersions, e.g. the ENEAR survey (Wegner et al.
2003) and the NOAO Fundamental Plane survey (Smith et al. 2004;
NFPS), recent studies of galaxy dynamics have mainly used pixelbased fitting techniques. In order to directly assess the difference
between these two widely used techniques we have measured velocity dispersions with pPXF (Cappellari & Emsellem 2004) for asubset of our spectra. We selected the four highest resolution templates (HD 813, CD-45 15239, HD 224420, HD 321) and the galaxy
spectra that were observed with instrumental resolutions that weregreater than that of the four templates. Extra instrumental (gaussian) broadening was added to the templates to match the individual resolutions of the galaxy spectra and the velocity dispersions
were determined using pPXF. There is good agreement between
the two techniques with only a moderate systematic difference (seeFigure 5). The observed systematic offsets at velocity dispersions
c 0000 RAS, MNRAS 000, 000–000

6dFGS Fundamental Plane Data

5

Figure 4. The scale of these corrections, as a function of the difference
in resolution between template and object spectra (∆τ ), for the velocity
dispersion sample. The lines show the average correction applied for objects
with a σ of 125, 150, 175, 200, 250, and 300 km s−1 . For clarity, the actual
corrections applied are only shown for individual galaxies with σ ∼ 125
km s−1 (crosses) and σ ∼ 300 km s−1 (squares).

of 2.00 and 2.50 dex are relatively small, i.e. +0.022 dex and –0.032 dex, respectively. These differences between the methods arecomparable to the systematic variations that can result from template mismatch (see Fig.1). We conclude that both fxcor and pPXF
are limited by systematic uncertainties from template mismatch andspectral filtering to a precision of about 0.02 dex. As shown below
this is substantially less than the typical random error for the 6dFGS
velocity dispersions.
We note that any systematic trend in velocity dispersion measurements will influence the derived parameters of the FP, notably
the coefficient associated with the log σ term. Clearly this is a potential source of bias and is particularly relevant when combining
datasets from different sources. Fortunately there is now a large
number of early-type galaxies with velocity dispersion measurements from more than one source (see Section 2.5) which allow
this bias to be fully investigated.
2.3

Velocity dispersion uncertainties

Measurement errors in velocity dispersions are due to a numberof factors: template–galaxy mismatch, noise in the spectra, nonoptimal filtering, and calibration errors. Errors due to template mismatch were minimized by using appropriate templates, which inthe case of early-type galaxies means late G-type and early K-typestars. Filter values were optimized using a range of stellar templates. Calibration errors depend upon the accuracy of the functionused to fit the template calibration curve, in this case a fifth orderpolynomial. Twilight flats were used to test the scale of calibration
errors, and these were found to be negligible, as they did not exceed
0.005 dex for velocity dispersions larger than ∼100 km s−1 .
The statistical uncertainties of our velocity dispersion measurements were determined via a bootstrap technique. Along withthe galaxy spectrum, the 6dF data reduction pipeline also provides
an associated variance spectrum. We used the galaxy and variancespectra to create many (∼900) bootstrap realisations of the original spectrum and for each we measured the velocity dispersion. We
adopted the standard deviation of these measurements as the statistical error of the velocity dispersion. This procedure was tested byusing synthetic spectra that mimicked the characteristics of the 6dF
c 0000 RAS, MNRAS 000, 000–000

Figure 5. Comparison of fxcor- and pPXF-based velocity dispersion measurements. The solid line indicates the best fit linear relation between the two sets of measurements. This has the form:log σ(pPXF) = 0.891 × log σ(fxcor) + 0.239 .

spectra, i.e. spectral coverage, velocity dispersion and S/N range,
and was found to give reliable estimates of the measurement errors. For the lowest S/N spectra modelled, i.e. S/N = 5, this technique returns values for the errors that are corrected, on average, atthe level of a few (∼5) percent. Figure 6 illustrates how the fraction
error depends on the inverse S/N.

2.4

The 6dFGSv velocity dispersion catalogue

To construct our final 6dFGSv velocity dispersion sample, several
criteria were applied. As previously noted, we excluded galaxies
with z > 0.055, where the strong Mgb feature is affected by the
red limit of the V-band spectral coverage. In order to ensure a reliable set of measurements, we only included galaxies that had spectral measurements with S/N > 5, R > 8 and σ > 100 km s−1 . These
criteria resulted in a velocity dispersion sample with 11 520 measurements; 185 galaxies have two measurements, ten have threemeasurements.
The 6dFGSv velocity dispersion catalogue is presented in Table 2 for an example set of galaxies; the velocity dispersion given inthis table are not aperture corrected. The full sample has a medianvelocity dispersion of 163 km s−1 , a median S/N of 12.9 Å−1 , anda median velocity dispersion error of 12.9%.

6

Campbell et al.

Table 2. 6dFGSv Velocity Dispersion Catalogue. The columns are as follows: (1) 6dFGSv unique reference number; (2) 6dFGS input catalogue standard
galaxy name; (3) 2MASS Extended Source Catalogue designation; (4) Observation Modified Julian Date; (5) Heliocentric redshift; (6) Signal-to-noise ratio
per Å measured from a region between 4900-5000 Å; (7) TD79 R-value in the Fourier cross-correlation; (8) Velocity dispersion in km s−1 (6.7 arcsec diameter
fibre aperture) corrected for instrumental resolution but not corrected for aperture size; (9) Log error in the velocity dispersion, i.e. log σ ≡ δ log σ. The fullversion of this table has 11 315 galaxies (185 galaxies have two measurements, ten have three measurements; all are listed, so this table has 11 520 data lines)
and is provided in the online Supporting Information.
#(1)

6dFGS ID
(2)

2MASS XSC name
(3)

MJD
(4)

zhelio
(5)

S/N
(6)

TDR
(7)

σ
(8)

log σ
(9)

00001
00002
00003
00004
00005
00006
00007
00008
00009
00010
00011
00012
00012

g0000144-765225
g0000222-013746
g0000235-065610
g0000251-260240
g0000356-014547
g0000358-403432
g0000428-721715
g0000459-815803
g0000482-551119
g0000523-355037
g0000532-355911
g0000558-255421
g0000558-255421

2MASXJ00001440-7652248
2MASXJ00002213-0137463
2MASXJ00002348-0656103
2MASXJ00002509-2602401
2MASXJ00003564-0145472
2MASXJ00003574-4034323
2MASXJ00004283-7217148
2MASXJ00004596-8158024
2MASXJ00004816-5511192
2MASXJ00005234-3550370
2MASXJ00005317-3559104
2MASXJ00005579-2554210
2MASXJ00005579-2554210

53614.60
52962.44
52966.4652846.78
52962.4452881.58
53618.50
53646.46
52969.51
52872.63
52872.6352846.7852880.53

0.05330.03830.03760.05080.02440.05000.03480.04220.03230.05200.05000.05050.0505

13.2
24.1
15.2
13.2
14.0
9.6
13.9
14.9
11.1
14.9
15.6
10.3
15.6

14.0
18.4
16.6
13.0
16.3
8.9
19.0
9.6
14.4
10.2
13.3
15.4
19.5

129.0
199.1
156.2
218.5
133.4
143.7
151.2
200.9
135.4
242.3
207.0
188.4
149.9

0.073
0.023
0.045
0.040
0.066
0.093
0.049
0.058
0.081
0.048
0.040
0.042
0.038

calculated by minimizing the χ2 sum(a σx + b − σy )2 / ((σx )2 + (σy )2 )(2)where σx and (σx ) refer to the 6dFGSv measurements and theirerrors, and σy and (σy ) refer to the external dataset. Data points
with χ2 > 6 were excluded from the fit. In Table 3 we summarize
the observed relations between 6dFGSv velocity dispersions andthose from the four external sources. All comparisons are consistent with unit slope, and hence, within the typical slope uncertaintyof 0.035 dex, there is no evidence of systematic bias. Also listed
in Table 3 is the median offsets between the velocity dispersion
measurements of these four external sources and 6dFGSv. While
the only significant offset is that for the 6dFGSv – ENEAR comparison, i.e. ∆ log σ = 0.021 ± 0.005, this difference is similarin size to that found when inter-comparing σ datasets (see Hudson
et al. 2001, Table 2). A detailed inter-comparison of the various velocity dispersion datasets, including the four considered here, will
be presented in future work where a standardized all-sky velocity
dispersion catalogue optimized for FP studies will be constructed.

3
Figure 6. Velocity dispersion fractional error as a function of inverse S/N.
0.8, 4.7 and 19.8 percent of the 6dFGSv sample have fractional errors
greater than 0.4, 0.3 and 0.2 respectively.

2.5

External Comparisons

There are a sizable number of galaxies in the 6dFGSv sample thathave dispersion velocity measurements from previous large studies,i.e. the Streaming Motions of Abell Clusters survey (Hudson et al.
2001; SMAC); the ENEAR redshift-distance survey (Wegner et al.
2003); the NOAO Fundamental Plane survey (Smith et al. 2004;
NFPS); and the Sloan Digital Sky Survey DR8 (Aihara et al. 2011;
SDSS). The comparison of the 6dFGSv velocity dispersions withthose values reported by SDSS and NFPS are presented in Figure 7.
The best-fit relation between these independent measurements was

3.1

PHOTOMETRIC MEASUREMENTS
PSF-corrected effective radii and surface brightnesses

The Fundamental Plane (FP) is the relationship between two photometric parameters, a characteristic radius and the mean surface
brightness within that radius, and the central velocity dispersion.
The key step in determining a galaxy’s photometric parameters isthe measurement of the total flux. Early work (e.g. Burstein et al.
1987) derived the total magnitude mT through a r1/4 growth curve
fit to photoelectric aperture photometry, and measured the effective(half-light) radius Re by interpolation. The mean surface brightness
hµe i within Re was determined via
hµe i = mT + 2.5 log(2πRe2 ) .

(3)For simplicity, early FP studies using CCD imaging data mimicked this approach (e.g. Lucey & Carter 1988, Lucey et al. 1991)
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Table 3. Comparison of 6dFGSv velocity dispersion measurements with external sources. For this comparison aperture corrections to the velocity dispersions
have been applied, see Section 5.1. n is the number of objects common between the dataset and 6dFGSv. nFIT is the number of objects remaining after theχ2 > 6 clipping has been applied. a and b are defined in Equation 2. ∆ log σ is the median offset (6dF – Source) between the two systems after clipping. rms
is the scatter of the difference between the two system after clipping.
Source

n

nFIT

a

SDSS
NPFS
SMAC
ENEAR

419
146
99
174

397
137
93
155

1.009 ± 0.025
1.056 ± 0.045
0.931 ± 0.035
0.985 ± 0.030

Figure 7. Comparisons of 6dFGSv velocity dispersion measurements withthose from SDSS (top panel) and NFPS (lower panel). The dashed line
shows the one-to-one relationship for reference. The solid line shows thebest linear fit, calculated as described in Section 2.5. The black squares
show galaxies included in the fit. The grey squares show objects that were
excluded from the fit. For clarity only error bars on the excluded data points
are displayed.

b–0.016 ± 0.057–0.126 ± 0.107+0.167 ± 0.081+0.057 ± 0.068∆ log σrms

χ20.008 ± 0.004
0.010 ± 0.006
0.005 ± 0.006
0.021 ± 0.005

0.057
0.058
0.045
0.049

0.984
1.060
1.003
1.351

with the addition of an empirical correction for the PSF blurring.
Later work using CCD imaging data undertook more sophisticatedanalysis to determine the total magnitude. For example, Jorgensen,
Franx & Kjaergaard (1995a) fitted ellipses to the surface photometry and used model fitting (r1/4 ) of the growth curves of thephotometry. Bernardi et al. (2003) used the r1/4 (or exponential)‘model’ magnitudes from the SDSS image processing software
which are derived from PSF-convolved two-dimensional surface
fitting. Alternative approaches used to determine Re include fitting
seeing-convolved Sérsic models to circularized profiles or major
and minor axis profiles (Saglia et al. 1997; D’Onofrio et al. 2008)
or full two-dimensional surface brightness fitting of a Sérsic model
convolved with a point spread function (Gargiulo et al. 2009) orSérsic plus exponential disk models (e.g. Fernández Lorenzo et al.
2011).
For the 6dFGSv sample we analysed the imaging data fromthe 2MASS survey (Skrutskie et al. 1997) to derive the FP photometric parameters. The key advantage of the 2MASS data is
the near-complete all-sky coverage and the excellent photometricuniformity. Furthermore, the online NASA/IPAC Infrared Science
Archive provides straightforward access to the 2MASS image tiles,
which allows the measurement of additional phototometric parameters not provided by the 2MASS Extended Source Catalog (XSC;
Jarrett et al. 2000).
The 2MASS point-spread function (PSF) has a relatively large
FWHM (∼3.2 arcsec) and required the development of a procedure
to derive PSF-corrected photometric parameters. For each target
galaxy we analysed the pixel data provided by the 2MASS Extended Source Image Server as follows. The 2MASS image data for
the J, H and K bands were analysed independently. We adopted
the total apparent magnitude (mT ) reported by 2MASS from the‘fit extrapolation’ method (i.e. j m ext, h m ext, k m ext; see
Jarrett 2000). First we determined the circular apparent effective
radius (rapp ) of the target galaxy on the 2MASS image by finding, via interpolation, the radius that contained half the total flux.
A model two-dimensional Gaussian PSF image was derived fromstars on the parent 2MASS data tile. GALFIT (Peng et al. 2010)
was used with the galaxy image and model PSF image as inputs
to find the best-fit two-dimensional Sérsic model. The half-lightradius was determined for the Sérsic model before and after convolution with the PSF (rmodel and rsmodel respectively) to find thePSF correction (∆r) given by∆ r = rsmodel − rmodel .

(4)

The PSF-corrected effective radius (Re ) was then derived via
Re = rapp − ∆r .

(5)

Hence the effective radius is determined as the empirical half-lightradius corrected for the effects of the PSF using the Sérsic model
fit. Note that the GALFIT model fit is only used to determine thec 0000 RAS, MNRAS 000, 000–0008Campbell et al.

Figure 8. J-band PSF correction as determined by GALFIT modelling. The
upper panel displays the measured PSF correction as a function of rapp
normalised by the PSF FWHM on the parent 2MASS data tile. The verticaldashed line is where rapp = 2 arcsec. The black line in the upper panel
follows the median values. For values of (3.0 / FWHM) log rapp of 0.3,
0.4, ..., 1.4 the log rapp – log Re corrections are 0.3143, 0.1768, 0.0950,
0.0515, 0.0291, 0.0173, 0.0108, 0.0068, 0.0040, 0.0028, 0.0020, 0.0010 respectively. The lower panel displays the residual between a median trend
and the data.

PSF correction. The size and variation of ∆r as a function of rapp
for the J-band is given in Figure 8.
For J-band apparent effective radii of 10, 8, 6, 4, 3 and2 arcsec, the average ∆r and its dispersion are (0.15, 0.04), (0.20,0.04), (0.27, 0.05), (0.51, 0.12), (0.88, 0.21) and (1.87, 1.10) respectively. As the 2MASS PSF is nearly Gaussian, these corrections are very similar to those derived from smoothing a Sérsic
model with the appropriate 2D Gaussian. For galaxies with apparent effective radii of 2 arcsec, the PSF correction is of similar sizeand so the relative uncertainty of the correction is near unity. Hence
this is the practical limit where galaxy sizes can be measured reliably on the 2MASS image tiles. Only 2% of the 6dFGSv FP sample
have apparent effective radii less than 2.4 arcsec, where the averagecorrection is 1.4 arcsec and hence the 2MASS image data is suitable for our study.
The derived PSF-corrected JHK effective radii for 11 102
galaxies are presented in Table 4. For 213 galaxies listed in Table 2,
i.e. 1.9% of the sample, derivation of reliable photometric parameters was not possible due to either deblending/masking issues or
because rapp was less than 2 arcsec.
The comparison of the PSF-corrected effective radii derived
independently from the J- and K-band 2MASS image data is presented in Figure 9. There is very good agreement between the two

Figure 9. Comparison of the PSF-corrected J- and K-band measured Re .
The rms scatter of the difference is 0.078 dex. The squares are the medianvalues at evenly log Re steps. In the lower panel the lines show the locationsof ± one standard deviations in the distribution.

sets of measurements. On average the measured J-band radii are∼7% larger than those in the K-band. If the errors are equally distributed between the two datasets, the average error on each measurement is 12%.
For each galaxy, the PSF-corrected effective surface brightness hµe i0 is computed ashµe i0 = mT + 2.5 log(2πRe2 )(6)

where mT is the 2MASS total magnitude (e.g. j m ext) and Re is
the PSF-corrected effective radius. In Section 5.2 we describe corrections applied to hµe i0 for cosmological surface brightness dimming, the k-correction, and galactic extinction in order to derive thefully-corrected effective surface brightness hµe i.

3.2Internal XF P comparisons

As is well-known the measurements of Re and hµe i are highly correlated; if Re is overestimated then a fainter hµe i is derived, andconversely (e.g. Burstein et al. 1987; Lucey et al. 1991). However
the linear combination of these parameters that appears in the FP,
which is approximately XF P ≡ Re − 0.3hµe i, is well determined(e.g. Jorgensen, Franx & Kjaergaard 1995a). In Figure 10 we compare our XF P measurements for the J- and K-bands. To aid thecomparison for each band the average effective surface brightness
has been subtracted from the hµe i values which ensures that the average ∆ XF P is near zero. The low intrinsic scatter between these
two independent measurements implies excellent internal precision
for each dataset (regardless of any intrinsic colour-size relation).
c 0000 RAS, MNRAS 000, 000–000

6dFGS Fundamental Plane Data

9

Table 4. 6dFGSv 2MASS Photometric Parameters. The columns are as follows: (1) 2MASS Extended Source Catalog name; (2) J-band total extended
magnitude (j m ext); (3) J-band PSF FWHM in arcsec; (4) J-band ∆r correction applied in arcsec; (5) log J-band PSF-corrected circular effective radiusin arcsec; the observed mean effective surface brightness can be derived via equation 6; (6) J-band Sérsic index from GALFIT; (7) H-band total extended
magnitude (h m ext); (8) H-band PSF FWHM in arcsec; (9) H-band ∆r correction applied in arcsec; (10) log H-band PSF-corrected circular effective
radius in arcsec; (11) K-band total extended magnitude (k m ext); (12) K-band PSF FWHM in arcsec; (13) K-band ∆r correction applied in arcsec; (14)log K-band PSF-corrected circular effective radius in arcsec. The apparent magnitudes (j m ext, h m ext and k m ext) are from the 2MASS Extended Source
Catalog and are not corrected for galactic extinction. The full version of this table is provided in the online Supporting Information.
J-bandH-bandK-band2MASS name(1)

j m ext
(2)

FWHM
(3)

∆r
(4)

log Re(5)Sérsic n(6)h m ext
(7)

FWHM
(8)

∆r
(9)

log Re(10)k m ext
(11)

FWHM
(12)

∆r
(13)

log Re(14)

2MASXJ00001440-7652248
2MASXJ00002213-0137463
2MASXJ00002348-0656103
2MASXJ00002509-2602401
2MASXJ00003564-0145472
2MASXJ00003574-4034323
2MASXJ00004283-7217148
2MASXJ00004596-8158024
2MASXJ00004816-5511192
2MASXJ00005234-3550370
2MASXJ00005317-3559104

13.17712.62512.59512.49512.24213.44312.94212.95813.01812.12712.445

2.97
3.08
2.90
2.98
3.10
3.03
2.94
2.88
3.09
3.06
3.02

0.36
0.85
0.33
0.38
0.23
0.45
0.40
0.59
0.34
0.24
0.33

0.605
0.312
0.638
0.538
0.800
0.493
0.544
0.446
0.660
0.832
0.671

5.43
4.52
6.00
3.59
3.24
2.22
3.89
3.43
0.82
5.38
4.14

12.49011.89611.857
11.781
11.60512.84912.187
12.276
12.21811.49011.620

2.97
3.06
2.88
2.95
3.03
2.92
2.97
2.89
3.02
2.85
2.94

0.51
0.95
0.30
0.42
0.24
0.57
0.40
0.48
0.35
0.22
0.25

0.503
0.278
0.665
0.505
0.770
0.426
0.559
0.381
0.690
0.770
0.743

12.20611.741
11.86911.55311.281
12.442
11.89811.80911.86911.13411.488

2.98
3.13
2.94
2.89
3.09
2.99
3.08
2.95
3.01
2.91
2.98

0.64
0.95
0.63
0.54
0.25
0.49
0.46
0.60
0.28
0.25
0.37

0.401
0.201
0.428
0.477
0.804
0.436
0.600
0.407
0.739
0.821
0.651

dius derived using GALFIT. An alternative approach would havebeen to directly use the total Sérsic magnitude and effective radii
returned by GALFIT. The XF P parameters from these two techniques are in excellent agreement (see Figure 11) with an average
difference of 0.001 dex and the scatter in the difference of 0.021
dex. Hence for the relatively low S/N 2MASS image data these twotechniques are equivalent. In our analysis we prefer our approachas this does not force a particular parametric photometric form on
the galaxy light distribution.

3.3

Figure 10. Comparison of XF P (≡ log Re − 0.3hµe i) for the J- and Kband measurements. The average effective surface brightness for the sample
in each band has been subtracted from the hµe i values. For this comparison
we include corrections to the effective surface brightnesses for galactic extinction, the k-correction and cosmological surface brightness dimming (seeSection 5.2). The squares are the median values at uniform XF P steps. In
the lower panel the lines show the locations of ± one standard deviations in
the distribution. The rms scatter of the difference is 0.018 dex.

The implied average measurement error of XF P , if equally divided between the two datasets, is 0.013 dex (3%).
Our analysis used the total apparent magnitude reported by
2MASS, e.g. j m ext, and PSF-corrected apparent effective rac 0000 RAS, MNRAS 000, 000–000

External XF P comparisons

Pahre (1999) measured K-band FP photometric parameter for 341
nearby early-type galaxies primarily in clusters. We have analysed
the 2MASS K-band image data following the procedure outlined
above and measured Re and hµe i for the Pahre sample. 2MASSbased measurements could be derived for 229 galaxies; the missing
galaxies are either too faint, unresolved at the 2MASS resolution
or strongly contaminated by an adjacent star. The comparison of
the XF P measurements is presented in Figure 12. There is excellent agreement between the two datasets with a systematic offset
of 0.017 dex. The implied average measurement error, if equallydivided between the two dataset, is 0.014 dex (3%).
The SMAC survey (Hudson et al. 2004 and references therein)presented FP photometric parameters derived from either R- or
V -band data an all-sky sample of 699 early-type galaxies in 56
clusters. For all galaxies in the SMAC standardized FP catalogue(Hudson et al. 2001, Table 7) we have analysed the 2MASS J-band
image data following the procedure outlined above and measured
Re and hµe i. 2MASS-based measurements could be derived for668 galaxies. On average ReJ /ReV,R = 0.70. The comparison of
the XF P measurements is presented in Figure 13. In this comparison the average effective surface brightness in each band has been
subtracted from the hµe i values. The low intrinsic scatter between
these two fully independent sets of measurements implies excellent
internal precision for each dataset. The implied average measurement error of XF P for each band is 0.019 dex (4%). The residual
trend in the lower panel is a direct result of the colour-magnituderelation; at fixed log Re (J)/Re (R), ∆XF P ∝ hµe (J)i−hµe (R)i
∝ J − R colour and XF P ∝ J magnitude.
The ENEAR survey (da Costa et al. 2000) presented R-band10Campbell et al.

Figure 11. Comparison of XF P derived from our technique and directlywith GALFIT. To aid the comparison, the average effective surface brightness for the sample in each band has been subtracted from hµe i. The
squares are the median values at uniform XF P steps. In the lower panel
the lines show the locations of ± one standard deviations in the distribution. The rms scatter of the difference is 0.021 dex.

FP photometric parameters for an all-sky sample of 1332 earlytype galaxies. For all galaxies listed in the ENEAR photometriccatalogue (Alonso et al. 2003) we have analysed the 2MASS Jband image data following the procedure outlined above and measured Re and hµe i. 2MASS-based measurements could be derived
for 1270 galaxies. The comparison of the XF P measurements ispresented in Figure 14. There is good agreement between the twodatasets, with an implied average measurement error of XF P foreach band of 0.033 dex (7.5%).
In Magoulas et al. (2012) (see Section 3.3) we describe in detail how the uncertainties in Re and hµe i (and their correlated nature) are taken into account when we apply our maximum likelihood method to model the 3D FP.

4
4.1

VISUAL CLASSIFICATION
Overview

All galaxies in the 6dFGSv sample were examined visually to verify that they were bulge-dominated systems, appropriate for the FP
analysis. Moreover, we attempted to roughly classify the types ofgalaxies in the sample by Hubble Type, as well as to identify cases
which were problematic either in Type or in measurement. Visualclassification was undertaken via a private purpose-built interface
to the publicly-accessible 6dFGS online database. The interface,constructed by M. Read, recorded information input by our groupin SQL format so they could be linked to the original database

Figure 12. Comparison of the K-band XF P for our 2MASS-based
measurements and the Pahre measurements. The observed median offset(2MASS-Pahre) is 0.017 dex (equivalent to a magnitude offset of 0.05) andthe rms scatter is 0.020 dex.

entries for each galaxy. To this end, the interface showed scaled
JHK images from the 2MASS extended Source Catalog (XSC;
Jarrett et al. 2000) and bJ rF images from SuperCOSMOS (Hambly
et al. 2001). These images were displayed alongside the redshiftedlabelled spectrum for the galaxy (example shown in Fig. 15).
The classification work was undertaken by ten members of the
6dFGS project team1 and was organised to maximise the numberof galaxies viewed and classified independently by at least two people. Figure 16 shows the number of galaxies viewed a given numberof times by independent classifiers. It shows that there were 9 226
galaxies (83% of the full sample) classified two or more times. Repeating the classifications using different people for most of thesample means that the dispersion in the type assignments can beused as a measure of self-consistency.
Our classification scheme was modelled on the one used by
Dressler (1980), who targetted galaxies over comparable distanceswith similar resolution and sensitivity. Like all modern qualitative
schemes, ours was based on the de Vaucouleurs (1959) revisionof Hubble’s original morphological sequence (Hubble 1926). Although we did not distinguish between barred and normal spirals,
we did separate galaxies into the major categories of elliptical (E),lenticular (S0), spiral (Sp), and irregular (Irr) types, as well as intermediate cases (e.g. Sandage 1961; Jarrett 2000).
Table 5 lists the classification divisions used in our analysis.
We assigned numerical values (called m-types) running from −4 to1Beutler, Cluver, Colless, Jarrett, Jones, Lucey, Magoulas, Mould, Parker,
and Springob
c 0000 RAS, MNRAS 000, 000–000

6dFGS Fundamental Plane Data

11

Figure 15. Purpose-built interface used to morphologically classify galaxies. The example shown is the nearby barred-spiral ESO 242−G 017 (z = 0.02473).
This galaxy is an example of a late-type system with a bulge that fills the 6dF fibre aperture, giving an early-type spectrum with negligible line emission.

+6 on a scale of our own invention. Galaxy m-types (m for morphology) differ from other numerical classification schemes (notably T -types) in the way that galaxy orientation was encoded. The
problem is not so much orientation per se, but the way in which
the inclination affects different types disproportionally: disk galaxies are more likely to be misclassified at high inclination angles
because of their flattened shape.
The established morphological T -type scale (de Vaucouleurs,de Vaucouleurs & Corwin 1976; Simien & de Vaucouleurs 1986)
assigns values from −6 for ellipticals up to +7 for late-type spirals (Sc/Sd) and beyond (> 8) for irregular galaxies. Negative T types refer exclusively to early types (ellipticals/lenticulars) while
late-types (spirals/irregulars) have positive values. Fukugita et al.
(2007) used their own variation on T -types with a scale from 0 to
6 (with −1 reserved for unclassifiable objects).
A galaxy in our scheme with m = 0 is an ordinary elliptical.
Unambiguous, low-inclination-angle (i . 60◦ ) disk galaxies wereassigned m = +2 in the case of lenticulars (S0s) and m = +4
for ordinary spirals. No attempt was made to subdivide spirals into
early and late types. Irregular galaxies were allocated m = +6.
Face-on and low-inclination S0s are distinguished from Es by thepresence of a bulge. For the purpose of deriving a clean FP, ourability to distinguish spirals from S0s is paramount, much more sothan separating S0s and ellipticals.
The intermediate odd-numbered values (m = +1, +3, +5)
were used to denote the transition or intermediate cases between
the key types. Transition galaxies are those that are ambiguous inappearance, regardless of whether this reflects their true nature. Because of this, our use of the word transitional is not meant to imply
anything about how galaxies evolve.
Most importantly, negative m-types were reserved for edge-on
cases (inclination angles i & 60◦ ) where the galaxy has an obvious
disk but its nature as an S0 or spiral is ambiguous. Galaxies withm = −4 were edge-on cases with an obvious dust lane, whilem = −2 is given to those edge-on cases with no obvious dust
c 0000 RAS, MNRAS 000, 000–000

Table 5. Morphological classification of the 6dFGSv
Morphology

m-typeEdge-on disk galaxy
with full-length dust lane

−4Edge-on disk galaxy withpartial or no dust lane

−2

Elliptical

0

Transition case:Elliptical – S0

+1

S0 (inclined/face-on)+2

Transition case:
S0 – Spiral+3

Spiral (inclined/face-on)+4

Transition case:Spiral – Amorphous+5

Irregular or Amorphous+6

Unclassifiable

999

lane. Although our use of optical bJ rF imaging facilitated these
distinctions, edge-on dust lanes are not an unequivocal indicatorof spiral structure. However the m-type scheme incorporates thislimitation by allowing edge-on cases to be flagged.
The utility of the m-type scheme is that it makes provision

12

Campbell et al.

Figure 13. Comparison of the XF P for the 2MASS J-band and SMAC V , R-band measurements. To aid the comparison the average effective surface
brightness for the sample in each band has been subtracted from hµe i. For
this comparison we include corrections to the effective surface brightnesses
for galactic extinction, the k-correction and cosmological surface brightness
dimming. The rms scatter of the difference is 0.027 dex. The residual trend(lower panel) is a direct result of the colour-magnitude relation and is detected at high significance. The slope of the J − (V, R) colour-magnitude
for the SMAC sample is ∼0.1 and a line of slope 0.1 is shown in the lower
panel, i.e. this is the expected systematic trend that would result from theobserved colour-magnitude relation.

for inclination while simultaneously preserving classification information. By defining |m|-type as the absolute value of the m-type,edge-on disk galaxies (with m = −2, −4) are readily combinedwith all other disk galaxies in the sample (values m = +2, +4)
if one so chooses. Alternatively, if we want to remove the edge-on
cases entirely, we can do that by simply restricting the sample tom > 0.
The advantages of this approach were twofold. First, it acknowledges the unavoidable role played by inclination by incorporating it into the classification scheme. Secondly, it affords a simple way of differentiating between galaxies for which orientation
has played a role from those of the same morphological type on asurer footing. In this way we deferred any decision about whether
a particular classification was incorporated into our final sample.
This ensured that the decision-making process imposed on classifiers was kept as simple as possible, enabling a speedy and consistent classification of thousands of galaxies. Problem cases werealso tagged, ensuring a clean final sample. There were 389 galaxies(3.5% of the sample) for which two or more classifiers flagged aproblem.
As an alternative indicator of morphological type, we considered using our GALFIT-derived J-band Sérsic indices (see Ta-

Figure 14. Comparison of the XF P for the 2MASS J-band and ENEAR
R-band measurements. To aid the comparison the average effective surface
brightness for the sample in each band has been subtracted from hµe i. For
this comparison we include corrections to the effective surface brightness
for galactic extinction, the k-correction and cosmological surface brightness
dimming. The rms scatter of the difference is 0.047 dex.

Figure 16. Differential (solid) and cumulative (dashed) distributions of thenumber of galaxies with a given number of morphological classifications.
By classifications, we mean the number of times an individual galaxy was
independently viewed and assessed by one of our group. The cumulate runs
from right to left and represents the number of sample galaxies having atleast that number of independent classifications.

c 0000 RAS, MNRAS 000, 000–000

6dFGS Fundamental Plane Data

13

Figure 17. Comparison of visual morphological classification and J-bandSérsic. The trend of Sérsic index of 4 for ellipticals to 2 for spirals (bulges)
is shown as the dashed line.

ble 4). While there is a trend of decreasing Sérsic index as themorphogical type changes from elliptical to spiral, the scatter islarge (see Figure 17). The low signal-to-noise of the 2MASS image data and the bulge-dominated nature of the NIR light makesthese 2MASS-based Sérsic indices a poor discrimator of morphogical type and therefore the indices were not used for the selectionof our FP sample.
All galaxies were also assessed according to three quality controls and a code was assigned in each case (Table 6). The three
aspects were as follows.
(i) Fibre coverage of the galaxy: whether the fibre aperture wasdominated by light from the bulge alone, a disk (with little or nobulge), or a combination of the two. By dominated we mean that theestimated bulge contribution to the observed spectrum was >80%.
By little or no we mean that the bulge contribution was estimatedat <20%.
Figure 15 shows an example of a nearby barred-spiral galaxy
where the bulge fills the fibre aperture, giving an early-type spectrum for a late-type galaxy.
(ii) Source confusion: flagging cases of galaxy/galaxy or
galaxy/star overlap (within or adjacent to the fibre aperture), or
other special cases of note (e.g. Galactic sources, high redshift
QSOs).
(iii) Spectral nature: broadly characterising whether the spectrum was dominated by absorption or emission-line features, orboth. In addition, at this stage redshifts were checked for gross errors (none were found).

4.2

Results

Figure 18 summarises the results of our morphological classification for the full 6dFGSv sample. The final m-type for a given
galaxy was the average of the individual values assigned by theclassifiers for that galaxy. The number of galaxies for each typeare shown, divided into bins of |m|-type, which has the aforementioned property of merging the edge-on cases with all other disk
c 0000 RAS, MNRAS 000, 000–000

Figure 18. Distribution of galaxy types assigned to the 6dFGSv sample,
binned by |m|-type (i.e. the absolute value of m-type). The lower axis gives
the relationship between |m|-type and morphology. The counts at the halfclassification step arise due to the effect of averaging integer classifications
over multiple observations.

Figure 19. Distribution of the dispersion in the |m|-type values assigned
independently. The subsample shown excludes the 1897 galaxies with onlya single classification, which have σ = 0 by default.

galaxies. There were 1712 galaxies (15% of the sample) classified
as edge-on by at least half of the people viewing each one.
The sample was dominated by elliptical and S0 types (9 291
galaxies with |m|<+3, or 84% of the full sample). This was not
unexpected, since the input sample was selected by spectroscopic
matching to early-type galaxy template spectra. Importantly, Figure 18 also shows a small but non-negligible number of spirals(928 galaxies or 8%). This was also not surprising, since these are14

Campbell et al.

cases where the bulge of the spiral fully fills the fibre aperture. As
noted above, fibre coverage was one of the quality control parameters noted for each galaxy independently of morphology (Table 6).
Figure 18 also shows that transitional cases are relatively few. We
do not believe this reflects the true occurrence of these systems, as
the effect of increased distance blurs the distinction between different galaxy types.
The simplest internal consistency check we used was to examine the dispersion, σ(|m|), in |m|-types assigned by all classifiers for a given galaxy. Cases of full agreement between classifiers
should result in zero dispersion. Figure 19 shows the number ofgalaxies in bins of σ(|m|), excluding the 1 897 with only one classification. Over 93% of the sample has a classification dispersion ofless than a single class (σ(|m|) 6 1), including 4 629 galaxies withzero dispersion. A dispersion of less than two classes (σ(|m|) 6 2)
encompasses 99.8% of the sample. Given that the major types (elliptical/S0/spiral) are all separated by |m|-type intervals of +2,
Figure 19 demonstrates our self-consistency in distinguishing the
major types from one another for the vast majority of the 6dFGSv
sample.
The effects of increasing distance complicated the task ofgalaxy classification in two ways. We divide these into imaging
and spectroscopic classification biases.
Imaging classification bias relates to the increasing difficultyof recognising a galaxy type at the sensitivity and spatial resolution limits of the system. Faint and detailed structures such as
spiral arms and dust lanes demand the most stringent imaging requirements. As a consequence, galaxy classification schemes based
purely on imaging tend to increasingly misclassify late-type galaxies as early-types at higher redshifts. This effect is evident in large
catalogues such as Galaxy Zoo (e.g. Bamford et al. 2009), where
the number of unequivocal spirals (in the sense that more than 80%of the classifiers agreed) seen at redshift z ∼ 0.08 was half thenumber seen at z ∼ 0.01, while over the same interval the numberof ellipticals doubles. Obviously this is a major concern for surveys considering morphology in the absence of other (e.g. spectral)
information; fortunately, this was not the case for 6dFGSv.
Spectroscopic classification bias arises when galaxy types are
assigned on the basis of their spectra through a fixed aperture. Suchapertures are almost always circular and positioned over the galaxy
centre, meaning that light from the outer regions is only sampled
in cases where the galaxy is sufficiently small and/or distant. Thismeans that a sample ostensibly of early-types can in fact containlate-type galaxies whose proximity ensures the bulge fills the fibre
aperture, producing an early-type spectrum. Clearly this effect isdistance-dependent.
Both forms of bias act to underestimate the numbers of spirals
in different redshift regimes. Spectroscopic classification bias operates at lower redshifts while imaging classification bias tends tobe an issue at higher redshifts. However, since we were concernedwith constructing a FP sample of early types, biases that act to misslate types were not important. The only effect we concerned ourselves with were late types being mistaken for early types as a consequence of spectroscopic classification bias. This bias was present
in 6dFGSv but was also readily identifiable: our visual screeningof the galaxy images ensured that spiral bulges were identified correctly. As the 6dFGSv sample was limited to redshifts z < 0.055,
image classification bias was limited, as experience showed that
reliable classification is possible to an apparent magnitude-limit ofK ∼ 13 (or equivalently, bJ ∼ 17). A more stringent test was to
examine the composition of our sample with increasing distance.
These effects were explored by measuring the distribution

Figure 20. Galaxy distribution in each |m|-type classification bin as a function of redshift (left) and surface brightness (right).

of types in terms of surface brightness and redshift. The surface
brightness distributions in Figure 20 tended toward fainter values
for spirals and spiral transition cases. This was not unexpected
given the initial preselection of our sample based on bulge spectral properties. At a fixed luminosity, a spiral galaxy will, due to its
disk, have a lower overall surface brightness than an elliptical. The
fact that the range of surface brightness seen in Figure 20 does notchange more was likely due to our preference for early-types where
total galaxy luminosity was dominated by the contribution from thebulge over the light from any disk.
Figure 21 shows that the mix of types found by our classification scheme did not change appreciably over the redshift range
considered. We recorded a small rise in the fraction of ellipticals(from 25% to 35%) while the numbers of S0s and transition cases
remained flat. The corresponding decline in the number of spirals(from 15% to 6%) we attribute to two effects. First, the fact thatthe fraction of early types increases with luminosity means thatthey will also increase with redshift in a magnitude-limited sample. Secondly, the spectroscopic preselection (and the fixed fibre
aperture employed) will include the very lowest redshift spirals because of the early-type appearance of their bulge-dominated spectra. At higher redshifts, the spirals are spectroscopically rejected
as more of the fibre aperture flux is due to the light of the starforming disks, including emission lines from the HII regions. Thisemphasises the importance of our visual screening to counter the
spectroscopic classification bias inherent in the input sample.
The other check we used was to benchmark our classifications against those of another group. We made a comparison between Galaxy Zoo morphological types and our assignments forthe sample of 281 galaxies common to both. Galaxy Zoo (Lintott
et al. 2008) is the compilation of basic morphological typing obtained by crowd-sourcing the visual classification of 893 212 galaxies from the Sixth Data Release of the Sloan Digital Sky Survey
c 0000 RAS, MNRAS 000, 000–000

6dFGS Fundamental Plane Data

15

Figure 21. Relative galaxy number as a function of redshift.

(DR6; Adelman-McCarthy et al. 2008). For the Galaxy Zoo sample there was a median of 34 independent views per galaxy using
elliptical and spiral as the two main classification divisions.
Figure 22 shows the comparison between the 6dFGSv andGalaxy Zoo. To facilitate comparison we rescaled the combined
Galaxy Zoo type on an equivalent scale of 0 to 4 (elliptical to spiral). Edge-on and problem cases have been excluded from the sample, as have galaxies with only one independent classification. The
agreement between 6dFGSv and Galaxy Zoo is good with minimal
scatter (σ(|m|) ∼ 1). The amount of scatter about the 1:1 relationis less than the size of the step (∆|m| = 2) separating the major
galaxy classes (E:S0:Sp).
However, Figure 22 also shows a tendency towards earlytyping by Galaxy Zoo relative to the 6dFGSv classes, in that agreater number of galaxies lie below the 1:1 line. We contend
that this was due to the redder optical passbands used by Galaxy
Zoo, and their reliance on composite images rather than examination of separate images in each passband. Morphological typingof 6dFGSv galaxies was done using multiple independent opticalimages (bJ rF ), with supporting near-infrared JHK frames, and acolour near-infrared composite. In contrast, Galaxy Zoo classifications used only a single-colour composite image made from optical
gri passbands. Since none of these sample the galaxy light shortward of λ . 4000 Å, the blue light of the OB stars that dominate
the arms of spiral galaxies is under-represented in the composite
frames. Therefore, galaxies with fainter spiral features tend to beclassified as S0s (predominantly on the basis of their bulge).
The morphological classifications of 6dFGSv were used forboth trimming problem galaxies as well as examining FP trends
with morphology (Magoulas et al. 2012). The characteristics of thismorphologically clean sample are discussed below.

5

6dFGSv FUNDAMENTAL PLANE SAMPLE

In this section we describe how the 6dFGSv Fundamental Plane
sample and catalogue is constructed from the measurements described in previous sections.
5.1

Velocity dispersion aperture correction

Our measured velocity dispersions (σ) are corrected to a standard
physical aperture size of Re /8, using the empirically-derived formula given by Jorgensen, Franx & Kjaergaard (1995b):−0.04
Re /8
σ0
=
(7)
σ
Rap
c 0000 RAS, MNRAS 000, 000–000

Figure 22. Comparison of Galaxy Zoo (Lintott et al. 2008) and 6dFGS
morphological types for a sample of 281 galaxies in common. The Galaxy
Zoo classification has been transformed to the 6dFGS scheme indicated on
the horizontal axis. The dashed line shows the 1:1 relation and the dottedlines show the (+1.5, −2.5) boundaries that encompass most of the scatter.
Galaxy Zoo classifications have a tendency towards earlier types.

where σ0 is the ‘central’ velocity dispersion within a circular aperture of radius Re /8. The 6dF fibre diameter is 6.7 arcsec and so
Rap = 3.35 arcsec. Rather than use the above equation directlywith our NIR-based Re measurements, we first use an empirical
relation that maps Re (N IR) to Re (optical) in order to provide acloser match to previous optical-based studies.
Using a sample of galaxies in common with the ENEAR survey (Alonso et al. 2003), we derived the following relation between
the R- and J-band effective radiilog ReR = 1.029 log ReJ + 0.140 .

(8)

The range of ReJ for our sample is 1.6 to 16 arcsec, corresponding
to an ReR range of 2.2 to 24 arcsec. The correction factor to themeasured velocity dispersion ranges from 1.107 to 1.010.

5.2 Re and hIe i
We convert the effective radii Re in arcseconds, as given in Table 4,
to effective radii Rekpc in physical units of h−1 kpc using
log Rekpc = log Re + log DA + log (1000/206265)(9)where DA is the galaxy’s angular diameter distance in units of
h−1 Mpc derived from the measured galaxy redshift (in the local
CMB rest frame) using a flat cosmology with Ωm = 0.3, ΩΛ = 0.7
and H0 = 100 h km s−1 Mpc−1 .
We find that 3186 of the galaxies in the sample are members ofgroups, as identified by the friends-of-friends grouping algorithm
outlined by Magoulas et al. (2012). For these galaxies, we use theredshift of the group, rather than the individual galaxy redshift, tocompute the redshift distance and the corresponding physical radius. The group redshift is defined as the median redshift of all
galaxies in the group.

16

Campbell et al.

Table 6. 6dFGS quality control classification codes for (1) fibre coverage,(2) source confusion, and (3) spectral features.
Fibre coverage
bulge

Code
1

Description

2

Galaxy shows no bulge (Irr), or
fibre misses bulge completely.

bulge+disk

3

Fibre captures light from bothbulge and disk, or the galaxy is
an edge-on disk.

Source confusion

Code

5.3

Bulge is present (S0-Sp) and it
fills the fibre aperture, or thegalaxy has no disk (E).

disk

Description

none

1

—

galaxy+star

2

The light in the fibre has contributions from a nearby star.

galaxy+galaxy

3

The light in the fibre has contributions from two or more galaxies.

not a galaxy

4

The fibre light is dominated by astar/PN/HII region/QSO.

other

5

Any problem not fitting the above.

Spectral features

Code

Description

absorption only1

Galaxy continuum with only clearly
discernible absorption features.

absorption+emission

2

Both absorption and emission features
clearly discernible above the noise.

emission only3

Little or no detected continuum andno discernible absorption features,or absorption features of much lower
prominence than emission lines.

Corrections were made to the values of hµe i0 for (i) the cosmological surface brightness dimming, (ii) the k-correction, using
the approximations of +0.7z, +0.2z and −3.3z mag for the J-,
H- and K-bands respectively (Persson, Frogel & Aaronson 1979),
and (iii) galactic extinction using the values given by Schlafly &Finkbeiner (2011) as report by NASA NED. The fully corrected
effective surface brightness is calculated ashµe i = hµe i0 − 2.5 log (1 + z)4 − kλ − Aλ(10)where we use the redshift in the local CMB rest frame.
It is most natural to have all FP parameters in logarithmic
units, so surface brightness values were converted from magnitude units (i.e. hµe i in mag arcsec−2 ) to log-luminosity units (i.e.
loghIe i in L pc−2 ) using
loghIe i = 0.4Mλ − 0.4hµe i + 2 log (206265/10)

where the absolute magnitude of the Sun, Mλ , depends on the passband. For the J-band, MJ =3.67; for the H-band, MH =3.33; andfor the K-band, MK =3.29; these values for the absolute magnitudeof the Sun are from http://mips.as.arizona.edu/∼cnaw/sun.html.

(11)Construction of the 6dF Fundamental Plane Sample

By combining both the velocity dispersion measurements from Table 2 (with the above aperture correction) and 2MASS photometricmeasurements from Table 4 (with the corrections and conversion
given above) we construct the 6dF FP sample.
It is important to distinguish between four overlapping samples used in our analysis. There are the three samples used to fit
the FP, i.e. the J-, H- and K-band FP samples, while the fourthis the J-band sample used for the peculiar velocity analysis. Details of the galaxy selection for the latter are described in detail in
Magoulas et al. (2012), and only a brief summary is given here.
In Section 2.4 we described an initial set of selection criteria on the velocity dispersion sample (S/N > 5, R > 8,
σ > 100 km s−1 that resulted in a sample of 11 315 galaxies. In
Section 3, we reported photometric parameters for 11 102 of thesegalaxies. This is the parent sample from which additional selection
cuts were made to produce a sample suitable for the FP analysis.
These additional cuts are listed in Magoulas et al. (2012) Table 1.
We first limited the sample to galaxies with 3 000 6 czCM B 6
16 120 km s−1 and log σ0 > 2.05 (this latter cut is imposed rather
than the original log σ0 = 2.00 to ensure a uniform limit after theaperture correction was applied). We also eliminated galaxies on
the basis of the visual classification of the morphology describedin Section 4; galaxies were excluded if at least one of four conditions was met: (i) galaxy morphology classified as irregular or
amorphous; (ii) galaxy identified as edge-on with a full dust lane;(iii) significant fraction (>80%) of the light in fibre is from thedisk; or (iv) light in fibre is contaminated by nearby star, galaxy or
defect.
These selection cuts resulted in a sample of 9 656 galaxies.
From that sample, we constructed individual J-, H-, and K- band
samples by imposing additional apparent magnitude cuts in each of
the three bands. Our sample had slightly brighter flux limits thanthe original 6dFGS magnitude limits (Jones et al. 2009), reflecting
the changes in the 2MASS (and, consequently, 6dFGS) magnitude
limits that occurred after the 6dFGS sample was selected. To maintain high completeness in each passband over the whole sample
area, we imposed magnitude limits of J 6 13.65, H 6 12.85 andK 6 12.55.
In our FP fitting procedure, each galaxy was weighted by
the inverse of its selection probability. This means galaxies areweighted by 1/S, where S is the fraction of the survey volume
in which a galaxy of that absolute magnitude could have been included in the sample. For each of the three photometric bands, we
excluded galaxies for which S < 0.05 in order to prevent a small
number of heavily weighted galaxies from strongly biasing the FP
fit. Magoulas et al. (2012) also defined a χ2 statistic, computed foreach galaxy, which measured the galaxy’s deviation from the model
FP relation. Galaxies with χ2 values greater than 12, representing adeviation from the model of more than about 3.5σ, were also eliminated from the sample.
After applying these selection criteria, we obtained, J-, H-,
and K-band samples of 8 803, 8 472, and 8 461 galaxies respectively. However, once the FP has been fit, there are various FP applications for which it is appropriate to include some of the discarded galaxies, e.g. galaxies excluded by the lower redshift limit
c 0000 RAS, MNRAS 000, 000–000

6dFGS Fundamental Plane Dataof czCM B = 3000 km s−1 and the selection probability limitof S = 0.05. In Springob et al. (2014), for example, we present
the derivation of galaxy distances and peculiar velocities for the
6dFGSv sample. This is the sample of galaxies for which we havederived distances and peculiar velocities from the J-band data, andit includes these low-redshift and low-selection-probability galaxies, resulting in a total of 8 885 objects in the peculiar velocity sample. The parameters derived for each galaxy in these overlapping
samples are summarised in Table 7; the data table is given as Table 8.
Most of the parameters in this table have already been definedin this paper, however we elaborate here on Columns (21)–(31).
As explained in Section 5.2, we make use of a group catalogue explained in Magoulas et al. (2012), which is drawn from the muchlarger set of all galaxies with redshifts in 6dFGS, not just those in
the FP sample. Column (21) is the group ID number for the galaxy
in question, in those cases for which the galaxy is in a group. We
also define Nr in Column (22) as the group richness. Because thefaintest members of a group might not have been observed, the richness of a group is defined as the number of observed galaxies in thegroup brighter than a specified absolute magnitude, chosen so thatgalaxies brighter than this would be visible throughout the sample
volume. Column (23) is the redshift of the group, taken as the median redshift of all galaxies in the group (including those within the
6dFGS redshift sample that are not in the FP subsample).
We also include two additional measures of local environment.
Column (24) is the projected comoving distance to the 5th-nearest
neighbor (d5 ) in h−1 Mpc. Column (25), the surface density (Σ5 ),is defined using the projected co-moving distance to the 5th nearest
neighbour within ±1000 km s−1 within a volume limited densitydefining population with MK < –22 mag. Further details of Σ5can be found in Brough et al. (2013).
Columns (26) to (31) are binary codes to denote whether or
not a galaxy is included in the various subsamples previously described. For inclusion in a sample six selection criteria are assessed. These are described in Table 7. Values in column (26) referto the J-band data and are a six-character sequence which indicates which of the six selection criteria are met; 1 denotes inclusion and 0 denotes exclusion. Only galaxies with a value of 111111
in column (26) are included in the final J-band sample. Similarly,
columns (28) and (30) give the six-character codes for the H-band
and K-band data respectively. Columns (27), (29) and (31) denote
whether or not a galaxy is included in the J-, H-, K-band 6dFGSv
peculiar velocity samples respectively. As previously noted, we primarily used the J-band 6dFGSv sample for our peculiar velocity
studies.
In Figure 23 we show histograms of log Rekpc and loghIe i for
all three bands, as well as the histogram of log σ0 . In Figure 24 weshow histograms of the errors on each of these quantities, while in
Figure 25 we show the histograms of redshifts for the J-, H-, andK-band samples, as well as the peculiar velocity sample 6dFGSv(in J-band).
This is the first public release of the 6dFGS Fundamental Plane peculiar velocity catalogue (6dFGSv). In previous work
(Magoulas et al. 2012; Springob et al. 2012) we used a preliminary version of this catalogue. Table 8 is an updated version of ouroriginal catalogue.
The main changes are: (i) the inclusion of improved velocity
dispersion errors, calculated via a bootstrap technique as describedin Section 2.3 (previously these were derived using the TD79 error
formula); (ii) after visually inspecting the 2MASS postage stamp
images and the GALFIT residual images for all objects, excluc 0000 RAS, MNRAS 000, 000–000

17Figure 23. The distribution of the observed FP parameters, log Rekpc ,log σ0 and loghIe i for the J-, H-, and K- band samples. The top row
shows the histograms of the three parameters in J-band, the middle row in
H-band, and the bottom row in K-band. The log Rekpc and loghIe i values
vary with passband for every galaxy, while the log σ0 value for each galaxy
remains unchanged. However the log σ0 histograms do vary slightly withpassband, since the J-, H-, and K- band samples include a slightly different mix of galaxies. Bin widths on log Rekpc , log σ0 and loghIe i are 0.05,
0.02 and 0.05 dex respectively.

sion of 129 galaxies where either the 2MASS processing removed
a substantial part of the target galaxy, (e.g. 2MASXJ08532460–
0919124) or the presence of a strong core asymmetry indicatedmultiple structures (e.g. 2MASXJ13525793–4751401); (iii) improved masking of contaminating stars in the GALFIT analysis
for ∼100 galaxies; (iv) the use of updated Galactic extinction values from Schlafly & Finkbeiner (2011) rather than from Schlegel,
Finkbeiner & Davis (1998); and (v) recalculation of the individual
galaxy sample codes as a result of the above changes to the input
dataset.
There are no substantive changes to the conclusions reachedin our previous work when this updated 6dFGSv catalogue is used,as (apart from the new velocity dispersion errors) the changes to thedataset are very small. In addition, our FP analysis procedure (seeMagoulas et al. 2012) was robust, as outliers were iteratively culled.
Further details are reported in Magoulas et al. (2014, in preparation).

5.4

6dFGS stellar population parameters

For completeness we also present here, in Table 9, the stellar population parameters for the 6dFGSv sample that were derived in Proctor et al. (2008).
These stellar population parameters, which apply to the18

Campbell et al.

Table 7. Summary of 6dFGS Fundamental Plane sample parameters. For each parameter, the table below lists its name, units, and a short description. Units
enclosed by brackets are logarithmic quantities.

Table Column

Parameter

Unit

Description

(1)
(2)
(3)
(4)
(5)
(6)
(7)

6dFGSid
2MASSid
R.A.
Dec.
czCM Blog Rekpc (J)log Rkpc (J)——degreedegreekm s−1[kpc h−1 ][kpc h−1 ]Source name in 6dFGS catalogue
Source name in 2MASS XSC catalogue
Right ascension (J2000)
Declination (J2000)
CMB frame galaxy redshift
Logarithm of effective radius in the J-band
Error in log Rekpc (J)

(8)
(9)

log Rekpc (H)log Rkpc (H)[kpc h−1 ][kpc h−1 ]Logarithm of effective radius in the H-band
Error in log Rekpc (H)

(10)
(11)

log Rekpc (K)log Rkpc (K)[kpc h−1 ][kpc h−1 ]Logarithm of effective radius in the K-band
Error in log Rekpc (K)

(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
(25)
(26), (28), (30)loghIe i (J)loghIe i(J)loghIe i (H)loghIe i(H)loghIe i (K)loghIe i(K)log σ0log σ0m-typeGroupID
Nrczgroupd5
Σ5Sample Code

[ L pc−2 ][ L pc−2 ][ L pc−2 ][ L pc−2 ][ L pc−2 ][ L pc−2 ][ km s−1 ][ km s−1 ]———km s−1
Mpc h−1gals Mpc−2 h2-(27), (29), (31)

PV Code-Logarithm of mean J band surface brightness within Re (J)Error in loghIe i (J)Logarithm of mean H band surface brightness within Re (H)Error in loghIe i (H)Logarithm of mean K band surface brightness within Re (K)Error in loghIe i (K)Logarithm of aperture-corrected central velocity dispersion
Error in log σ0
Average morphological type classification, see Table 5Group or cluster identification number
Richness of galaxy group or cluster
Group or cluster median CMB redshift
Projected comoving distance to the 5th nearest neighbour (not available for 1165 galaxies)
Surface density measured to the 5th nearest neighbour (not available for 1165 galaxies)
The selection criteria that a galaxy satisfies are encoded in this sample code (JHK). These codes are
six-digit binary strings, where digit = 0 or 1 indicates that the galaxy fails or satisfies the selection criterion.
The six selection criteria (in left to right string order) are: (1) log σ0 > 2.05; (2) cz > 3000 andcz < 16120 km s−1 ; (3) morphological classification acceptable; (4) apparent magnitude < 13.65 (J),12.85 (H) or 12.55 (K); (5) selection probability > 0.05 (JHK); (6) χ2 < 12 (JHK).
Code to indicate whether galaxy excluded from (0) or included in (1) the JHK 6dFGSv sample.

e

e

e

Table 8. The 6dFGS Fundamental Plane Catalogue. Table 7 provides a description of each column. For ease of presentation here, the columns here are wrappedover three lines. Missing or not applicable values are set to –1, e.g. in column (21) for galaxies not assigned to a group. The full version of this table, with one
galaxy per row, is provided in the online Supporting Information.
(1)(2)
g0000144-765225
2MASXJ00001440-7652248
g0000222-013746
2MASXJ00002213-0137463
g0000235-065610
2MASXJ00002348-0656103
g0000251-260240
2MASXJ00002509-2602401
g0000356-014547
2MASXJ00003564-0145472

(3)
(12)
(21)

(4)
(13)
(22)

(5)
(14)
(23)

(6)
(15)
(24)

(7)
(16)
(25)

(8)
(17)
(26)

(9)
(18)
(27)

(10)
(19)
(28)

(11)
(20)
(29)

0.003992.940–10.006153.708–10.006523.067–10.006973.328–10.009902.859–1–76.87364
0.084–1–1.62947
0.071–1–6.936190.070–1–26.04450
0.068–1–1.76317
0.062
–1

15941
3.266
–1
11123
3.921
–1
10920
3.161
–1
14926
3.531
–1
6956
3.029
–1

0.465
0.102
4.27580.024
0.085
6.43180.343
0.084
6.21420.371
0.082
3.28270.316
0.077
3.89940.057
3.489
0.11760.048
4.067
0.03850.047
3.562
0.04480.046
3.592
0.14340.042
3.040
0.10360.363
0.136
111111–0.0100.118
1111110.370
0.123
1111110.338
0.110
111111
0.286
0.099
111111

0.069
2.137
1
0.057
2.338
1
0.057
2.219
1
0.055
2.369
1
0.052
2.144
1

0.261
0.073
111111–0.0870.023
111111
0.133
0.045
1111110.310
0.040
1111110.320
0.066
111111

0.092
2.2
1
0.079
2.5
1
0.083
3.7
1
0.074
2.3
1
0.067
3.0
1

(30)

(31)

111111

1111111

1111111

1111111

1111111

1

c 0000 RAS, MNRAS 000, 000–000

6dFGS Fundamental Plane Data

19

Table 9. 6dFGS Stellar Population Catalogue. Only galaxies that meet the criteria S/N > 9 Å−1 and QSP 6 10 are listed here (see Proctor et al. 2008). The
columns are: (1) source name in 6dFGS catalogue; (2) logarithm of the galaxy age in Gyr; (3) error in log age, age ; (4) Fe abundance, [Fe/H]; (5) error in[Fe/H], F e/H ; (6) α-element over-abundance ratio, [α/F e]; (7) error in [α/Fe], α/F e ; (8) overall metallicity, [Z/H]; (9) error in [Z/H], Z/H ; (10) qualityof the stellar population parameter fits, QSP . The full version of this table is provided in the online Supporting Information.
6dFGS IDlog ageage[Fe/H]

F e/H[α/F e]α/F e

[Z/H]

Z/H

QSP

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

g0000144-765225
g0000222-013746
g0000235-065610
g0000251-260240
g0000356-014547
g0000358-403432
g0000428-721715
g0000459-815803
g0000523-355037

0.650
0.500
0.475
0.775
0.375
0.500
1.175
0.250
0.700

0.088
0.058
0.075
0.108
0.046
0.107
0.021
0.042
0.083

–0.646
0.435
0.237
0.027
–0.0700.005–0.185–0.170–0.471

0.158
0.066
0.088
0.125
0.073
0.151
0.057
0.131
0.113

0.500
–0.090
0.120
0.210
0.180
0.180
0.090
0.180
0.500

0.111
0.040
0.038
0.064
0.046
0.070
0.048
0.082
0.073

–0.175
0.350
0.350
0.225
0.100
0.175
–0.100
0.000
0.000

0.063
0.047
0.067
0.072
0.045
0.099
0.030
0.067
0.054

5
7
5
6
5
8
4
6
8

Figure 24. The distribution of the errors on the observed FP parameters
log Rekpc , log σ0 and loghIe i for the J-, H-, and K-band samples (topto bottom). Bin widths on log r , log σ , and log i are 0.004, 0.002 and0.004 dex respectively.

Figure 25. The redshift distributions for the J-band (upper left), H-band(upper right), K-band (lower left) and the 6dFGSv peculiar velocity (lower
right) samples. The bins have width 500 km s−1 .

6.7 arcsec diameter central region of each galaxy covered by a 6dF
fibre, are based on Lick index measurements and the χ2 -fitting procedure of Proctor & Sansom (2002), which uses all available indices to obtain simultaneous estimates for log(age), [Fe/H], [Z/H]
and [α/Fe]. This method is relatively robust against the problems
that can afflict spectral index measurements, particularly when (asfor 6dFGS) the spectra cannot be flux-calibrated, and also, to a certain extent, against uncertainties in the stellar population models.
Here the models of Korn, Maraston & Thomas (2005) were used, as
they provide the required range in both age and metallicity and include the effects of varying α-element abundances. For the passive
galaxies in the FP sample, between 10 and 15 Lick indices ranging from Hδ to Fe5406 were included in the fits. Some indices inthis wavelength range were excluded, however, because they werepoorly fitted by the models: the Ca4455, Mg1 and Mg2 indices(which are sensitive to flux calibration), and the Hβ and Fe5015
indices (which suffer from low-level emission-line contamination).

For the passive galaxies in the FP sample, the mean reduced
χ2 was 1.08, indicating satisfactory fits given the measured errors in the Lick indices. For each galaxy, the errors in the stellar population parameters were estimated as the rms scatter in thevalues obtained from 50 Monte Carlo simulations of the fitting
procedure using the observational errors in the Lick indices forthat galaxy. Excluding low S/N galaxies with errors in log(age)greater than 0.3 dex, for which the stellar population parameters
are deemed unreliable, the mean errors in both log(age) and [Z/H]
are approximately 0.15 dex. These errors depend on spectral S/N:
for galaxies with S/N > 16 the mean errors in both quantities are∼0.1 dex, while for S/N < 16 they are ∼0.2 dex. The errors in age
and metallicity are, as usual, somewhat correlated. A quality parameter, QSP , was also defined as the sum of the integerized reduced χ2 and the number of clipped indices; galaxy spectra with a
QSP value of 1 have the best quality fits. Only data from galaxies
with S/N per angstrom greater than 9 and quality parameter of 10

c 0000 RAS, MNRAS 000, 000–00020Campbell et al.

or lower are used in our analysis, and included in Table 9 Proctor
et al. (2008) consider all these issues in detail, and further discussion of the stellar population parameters and their effect on the FP
is provided by Springob et al. (2012).

6

CONCLUSIONSThis paper reports the measurements of Fundamental Plane (FP)parameters for nearly 9000 galaxies in the 6dFGS peculiar velocity survey (6dFGSv) and describes how the FP catalogue was constructed. This is the largest and most homogeneous set of FP measurements for galaxies in the nearby universe obtained to date.
These data have previously been used to study the effects ofstellar population differences on galaxy scaling relations (Proctor et al. 2008), to characterise the near-infrared FP and investigate trends in the FP with environment and galaxy morphology(Magoulas et al. 2012), and to map the changing stellar populationsof galaxies as a function of their location within the FP (Springob
et al. 2012).
In papers now in preparation, this catalogue will be exploited
to derive FP distance estimates for these galaxies, and so determine
the peculiar velocity field throughout the southern hemisphere local
volume out to a redshift of ∼16 000 km s−1 (Springob et al. 2014);
this volume samples many important attractors including the Shapley supercluster. The velocity field will, in turn, lead to measurements of the local bulk flow on various scales and permit comparisons of the observed velocity field with that predicted from the
distribution of galaxies found in redshift surveys (Magoulas et al.
2014). The 6dFGS redshifts and peculiar velocities will be jointly
analysed to characterise the statistical properties of the density andvelocity fields captured by the galaxy-galaxy, galaxy-velocity andvelocity-velocity power spectra (Johnson et al. 2014; Koda et al.
2014), providing better determinations of cosmological parameters,such as the redshift space distortion (β) and the correlation between
galaxies and dark matter (rg ), that are degenerate given only the information provided by redshift surveys.
In the future, the southern 6dFGSv sample will be combined with the northern SDSS FP sample to recover the local
velocity field over about three-quarters of the sky. Comparisonswith new all-sky Tully-Fisher (TF) peculiar velocity samples,such as the ongoing 2MASS Tully-Fisher survey (2MTF; Masters,
Springob & Huchra 2008; Hong et al. 2013b,a) and the planned
WALLABY/WNSHS surveys (Duffy et al. 2012; Duffy, Moss &Staveley-Smith 2012), and with new supernova surveys (e.g. Turnbull et al. 2012; Ma & Scott 2013; Rathaus, Kovetz & Itzhaki 2013),
will provide independent cross-checks and refined measurements.
Together, these peculiar velocity surveys of thousands of galaxies
will provide a clear picture of the motions in the local universe.
This will complement the detailed density maps provided by redshift surveys and lead to tighter constraints on a wide range of cosmological parameters.

ACKNOWLEDGMENTS
We acknowledge the efforts of the staff of the Australian Astronomical Observatory (AAO), who developed the 6dF instrument andcarried out the observations for the survey. This publication makes
use of data products from the Two Micron All Sky Survey, which isa joint project of the University of Massachusetts and the Infrared
Processing and Analysis Center/California Institute of Technology,

funded by the National Aeronautics and Space Administration and
the National Science Foundation. This research has made use of theNASA/IPAC Extragalactic Database (NED), which is operated by
the Jet Propulsion Laboratory, California Institute of Technology,
under contract with the National Aeronautics and Space Administration. JRL acknowledges support from STFC via ST/I001573/1.
DHJ acknowledges support from Australian Research Council Discovery Projects Grant DP-0208876, administered by the Australian
National University. CM and JM acknowledge support from ARC
Discovery Projects Grant DP-1092666. CM was also supported bya scholarship from the Australian Astronomical Observatory. We
thank the anonymous referee for providing us with constructive andinsightful comments which helped improve this manuscript.

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