A Stellar kinematic maps

Stellar kinematics across the Hubble sequence in the CALIFA survey: General properties and aperture corrections

Key Words.:
Galaxies: kinematics and dynamics – Galaxies: elliptical and lenticular, cD – Galaxies: spiral – Galaxies: structure – Galaxies: evolution – Galaxies: formation

We present the stellar kinematic maps of a large sample of galaxies from the integral-field spectroscopic survey CALIFA. The sample comprises 300 galaxies displaying a wide range of morphologies across the Hubble sequence, from ellipticals to late-type spirals. This dataset allows us to homogeneously extract stellar kinematics up to several effective radii. In this paper, we describe the level of completeness of this subset of galaxies with respect to the full CALIFA sample, as well as the virtues and limitations of the kinematic extraction compared to other well-known integral-field surveys. In addition, we provide averaged integrated velocity dispersion radial profiles for different galaxy types, which are particularly useful to apply aperture corrections for single aperture measurements or poorly resolved stellar kinematics of high-redshift sources. The work presented in this paper sets the basis for the study of more general properties of galaxies that will be explored in subsequent papers of the survey.

1 Introduction

The motion of stars within galaxies is a fundamental property set very early on in their life. Ever since the detection of rotation of stars in the Milky Way and nearby systems (e.g., Lindblad 1927; Mayall 1951; Münch & Münch 1960), the study of stellar motions has been a fruitful avenue to pose important constraints on our knowledge about galaxy formation and evolution. The analysis of rotational over random motions in early-type galaxies, for instance, has led to the realization that bright early-type galaxies are likely triaxial objects supported by orbital anisotropy (e.g., Bertola & Capaccioli 1975; Illingworth 1977; Binney 1978), rather than rotation.

The coupling of long-slit spectrographs with telescopes 2 to 4 m in size has provided, over the last three decades, a wealth of spatially resolved observations that has greatly improved our understanding of the overall stellar motion and level of kinematic substructure in external galaxies (e.g., Davies et al. 1983; Bertola et al. 1984; Bender et al. 1994; Fisher 1997; Simien & Prugniel 1997; Rubin et al. 1999; Vega Beltrán et al. 2001; Aguerri et al. 2003; Falcón-Barroso et al. 2003; Pizzella et al. 2004; van den Bosch et al. 2015).

While the first integral-field units (IFUs) were already in place in the mid-90’s (e.g., Bacon et al. 1995), the first serious efforts to measure stellar kinematics on large samples of galaxies using these kinds of instruments did not occur until year 2001. One of the pioneer projects in this respect was the SAURON survey (Bacon et al. 2001; de Zeeuw et al. 2002). With a representative sample of 72 galaxies (24 ellipticals, 24 lenticulars, and 24 early-type spirals, later extended with observations of 18 late-type spirals), this survey has set the reference for stellar kinematic IFU studies (e.g., Emsellem et al. 2004; Falcón-Barroso et al. 2006; Ganda et al. 2006). The discovery of the slow and fast rotator families in early-type galaxies (Emsellem et al. 2007) served as the trigger for a larger project: the ATLAS survey (Cappellari et al. 2011), in which a volume complete sample of 260 early-type galaxies revisited many kinematic aspects, from the amount of global angular momentum (Emsellem et al. 2011) to a detailed account of kinemetric features (Krajnović et al. 2006, 2011). In parallel, the DiskMass survey mapped, the stellar kinematic properties of nearby late-type spirals with the aid of the PPak IFU (Roth et al. 2005; Kelz et al. 2006).

The CALIFA survey (Sánchez et al. 2012) was born to fill in existing gaps in other IFU surveys and to provide a morphologically unbiased view of the stellar kinematics in galaxies based on a large ( 600 galaxies) and homogeneous integral-field spectroscopic dataset. The main advantage of CALIFA over existing surveys resides in a sample selection that includes all morphological types, as well as a field-of-view (FoV) that extends up to several effective radii (R). While CALIFA is no longer the IFU survey with the largest number of observed objects in the nearby Universe, it still provides the best compromise between spatial coverage (1.8–3.7 R) and sampling (1 kpc). Currently ongoing IFU surveys are hampered in one way or another by these factors, for example, SAMI covers areas within 1.1–2.9 R with a spatial sampling  1.7 kpc (Croom et al. 2012; Bryant et al. 2015), while MaNGA primary sample targets have a spatial sampling of 3 kpc within 1.5 R (Bundy et al. 2015). The real revolution in this respect will take place when MUSE at the Very Large Telescope (Bacon et al. 2010) is used in survey mode, as anticipated by the very spectacular stellar kinematic cases presented in the first few years of operations (e.g., Emsellem et al. 2014; Krajnović et al. 2015; Gadotti et al. 2015; Iodice et al. 2015).

The goal of this paper is to present the first stellar kinematic maps extracted from the CALIFA survey, describe all the technical details of the extraction, and provide basic stellar velocity dispersion aperture corrections for elliptical and spiral galaxies. The maps presented here have already been used within the survey to establish the effect of galaxy interactions on the stellar kinematics of galaxies (Barrera-Ballesteros et al. 2014, 2015), constrain the pattern speed of barred galaxies across the Hubble sequence (Aguerri et al. 2015), to present a volume-complete Tully-Fisher relation (Bekeraitė et al. 2016a), and the velocity function of galaxies as a benchmark for numerical simulations (Bekeraitė et al. 2016b). Forthcoming papers of the survey will make use of this information, for example, to revisit the distribution of global angular momentum in nearby galaxies and determine their dark matter content. Falcón-Barroso et al. (2015) provides a preview of some highlights. For results on the kinematics of the ionized gas in CALIFA, see García-Lorenzo et al. (2015).

The paper is organized as follows. Section §2 describes the sample of 300 galaxies used in our study and how this sample compares with the full CALIFA sample. Section 3 summarizes the instrumental setup employed during the observations. In section 4 we provide details of our kinematic extraction and comparisons with other major IFU surveys. Section 5 explains the limit set by our instrumental setup in the measurement of stellar velocity dispersions. In section 6 we provide velocity dispersion aperture corrections for elliptical and spiral galaxies. Finally, we summarize our work and conclusions in section 7.

Figure 1: Distribution of galaxies in the sample of CALIFA galaxies presented in this paper (see §2) as a function of Hubble type, stellar mass, and absolute magnitude in the band. For convenience, along with the color bar, we indicate the number of galaxies in each bin.

2 The CALIFA sample

This study is based on observations of 300 galaxies drawn from the CALIFA mother and extended samples1, which are part of the photometric catalog of the seventh data release (Abazajian et al. 2009) of the Sloan Digital Sky Survey (SDSS). The main selection criteria in the survey is an angular isophotal diameter (), which is followed by a limited range in redshift, . These constraints ensure an efficient use of the PPak IFU and excludes, together with an apparent magnitude cut at band Petrosian magnitude of 20 mag, the presence of too many dwarf galaxies in our sample. Walcher et al. (2014) provides more details about the sample selection criteria and an in-depth discussion of the effects they introduce in the survey.

The CALIFA sample contains a large number of galaxies with diverse kinematic properties: from slow rotating ellipticals, to disk-dominated fast rotating galaxies, and perturbed interacting systems. This paper is based on the V1200 data (see §3) available until June 2014. We removed from our original sample of 375 galaxies those cases where the quality of the resulting stellar kinematic maps was not sufficient (e.g., poor spatial sampling due to low-quality data) to guarantee a meaningful analysis. We also selected out those cases whose stellar kinematics appeared highly disturbed by the presence of large nearby companions or had clear indications of being in final stages of a merging process. While this criteria excluded cases like ARP 220 (shown in Figure 3), it did not remove cases like the Mice galaxies (see Wild et al. 2014 for a detailed CALIFA study of this system), where the interaction has not drastically affected the observed kinematics. Barrera-Ballesteros et al. (2014, 2015) carefully examine the stellar kinematics of merging systems in the CALIFA survey. Our final sample thus consists of 300 galaxies.

In Fig. 1 we show the distribution of CALIFA galaxies presented in this paper as a function of Hubble type, stellar mass, and total absolute magnitude in the band. Hubble type classification was determined after a careful visual inspection by several members of the team. Stellar masses and total absolute magnitudes were derived following the prescriptions described in Walcher et al. (2014). Stellar masses assume a Chabrier initial-mass function (Chabrier 2003). While the number of galaxies represents a major improvement over other integral-field surveys, the selection criteria adopted in the CALIFA survey introduce an important shortcoming: the lack of low-mass, low-luminosity early-type systems and high-mass, high-luminosity late-type galaxies. Another important aspect is that our selection criteria favors edge-on orientations for the lowest mass and fainter systems (i.e., Sd galaxies). The advantage of this selection, however, is that it allows us to volume-correct averaged quantities and thus provide kinematic results that are representative of the general population of galaxies. Table 1 contains the basic properties of the subset of galaxies of our study.

We illustrate how representative our subsample is with respect to the mother sample in Fig. 2. The top and middle panels of the figure show the distribution of both the mother sample and our subsample in redshift, isophotal diameter (A) and petrosian -band magnitude (M)2. The vertical lines indicate the limits in absolute magnitude in which the CALIFA mother sample is representative. In this space of parameters, the distribution of our subsample is consistent with that shown by the mother sample. Furthermore our galaxies cover all areas sampled by the mother sample. The bottom panel compares the luminosity function of SDSS (Blanton et al. 2003), the CALIFA mother sample, and the subset of 300 galaxies of the kinematic sample. We have applied a Kolmogorov-Smirnov test to the different parameters and confirm that the kinematic and mother samples are statistically consistent. Therefore the set of 300 galaxies studied in this paper are a good representation of the overall population of galaxies of all morphological types in the nearby Universe, within the luminosity and size constraints imposed by the CALIFA target selection.

Figure 2: (Top and middle panels) Distribution of our sample of 300 galaxies (orange circles) in redshift, isophotal diameter (A), and absolute -band petrosian magnitude (M). For reference, the CALIFA mother sample is shown with black dots. The vertical lines indicate the limits in absolute magnitude in which the CALIFA mother sample is representative. (Bottom panel) Comparison of the luminosity functions of the SDSS (Blanton et al. 2003, thick dashed line), CALIFA mother sample (dark blue circles), and the kinematic sample presented here (orange circles).
Figure 3: Examples of line-of-sight stellar kinematic maps from the CALIFA V1200 grating dataset. (Top row) Color-composite SDSS image of each galaxy. (Middle row) Stellar velocity maps. (Bottom row) Stellar velocity dispersion maps. From left to right: NGC 6125, a slow-rotator elliptical in our sample (i.e., low velocity amplitude and overall large velocity dispersion); NGC 1167, an early-type spiral galaxy with large velocity and central velocity dispersion amplitudes; NGC 4210, a disk-dominated galaxy (i.e., high velocity amplitude and overall small velocity dispersion); ARP 220, an interacting system (i.e., with complex stellar velocity and velocity dispersions maps). All maps share the same velocity and velocity dispersion scale and are in units of km s as indicated in the colorbars. Isophotes (black lines) are constructed from the V1200 CALIFA data cube.

3 Instrumental setup

The data presented in this paper is part of the CALIFA survey and as such were observed with the PMAS instrument (Roth et al. 2005) in the PPak mode (Verheijen et al. 2004; Kelz et al. 2006), mounted at the 3.5 m telescope of the Calar Alto observatory. For each galaxy, our observations cover the central 74″  64″ using a hexagonal fiber bundle. For a detailed description of the observations and data reduction, see the CALIFA presentation article (Sánchez et al. 2012) and the CALIFA Data Release papers 1 and 2 (Husemann et al. 2013; García-Benito et al. 2015). The stellar kinematics presented in this paper is based on data from the v1.4 data reduction pipeline. Here we give a brief overview of the features of the observational setup that are relevant to our scientific interests.

The CALIFA survey is conducted in two instrumental setups: a low resolution mode (V500) with  850 at  5000 Å and a medium resolution mode (V1200) with  1650 at  4500 Å. The V500 grating covers a broad spectral range (3700–7300 Å) and includes a number of absorption and emission features, from the Ca H+K and [O II]3727 to H and [S II]6731 lines. The V1200 grating covers a smaller spectral window (3400–4750 Å). After careful evaluation of the spectral resolutions of the two gratings we established a value of 6 Å (FWHM  327 km s) for the V500 and 2.3 Å (FWHM  169 km s) for the V1200 gratings, respectively (see Husemann et al. 2013).

4 Stellar kinematics extraction

We extracted the stellar kinematics from every galaxy in a uniform way using both instrumental setups, i.e., V500 and V1200. Before accomplishing this, we applied spatial masks to remove spurious effects such as bad pixels, nearby objects, and/or foreground stars. We then logarithmically rebinned the spectra in each data cube to conserve a linear step in velocity space. We trimmed the data to contain only a useful spectral range: 3800–7000 Å for the V500 and 3750–4550 Å for the V1200 setup. We then selected for future use all spaxels within the isophote level where the average signal-to-noise ratio3 (S/N) was larger than 3. This cut ensured the removal of low-quality spaxels, which could introduce undesired systematic effects in our data at low surface brightness regimes. The next step was to spatially bin the data cubes to achieve an approximately constant S/N of 20 (per pixel). This value allows us to conserve a good spatial resolution while still being able to reliably estimate the first two moments of the line-of-sight velocity distribution (LOSVD). For this step we used the Voronoi 2D binning method of Cappellari & Copin (2003). Special care was taken in the S/N calculation to account for the correlation in the error spectrum of nearby spaxels (see Husemann et al. 2013 for details).

We measured the stellar kinematics of all galaxies in our sample using the pPXF code of Cappellari & Emsellem (2004). We used as templates the Indo-U.S. spectral library (Valdes et al. 2004) from which we selected  330 stars that uniformly cover the parameter space in gravity, metallicity, and effective temperature. The careful choice of stellar spectra is crucial to minimize template mismatch effects. We confirmed that, using our subset of 300 stars, we could reproduce the same results obtained using the full library. A non-negative linear combination of those stellar templates, convolved with a Gaussian LOSVD, was fitted to the spectrum of each Voronoi bin. The best-fitting parameters were determined by minimization in pixel space. In the wavelength region covered by CALIFA, there are several emission lines that needed to be masked during the fitting process, for example, [O II], [Ne III], H, H, [S II], H, [Fe II], H, [O III], He II, [Ar IV], H, [N I], He I, [O I], [N II], and H. We used a generous band width of 500 km s around those lines during the fitting process. This window was enough to mask the emission in all our galaxies. We also masked the regions affected by sky line residuals and the sodium doublet at 5890 Å. Additionally, a low-order additive Legendre polynomial was included in the fit to account for small differences in the continuum shape between the galaxy spectra and the input library. An order 6 polynomial was the minimum that ensured no large-scale wiggles in the residual spectra. In the end, the best-fitting values ( and ), and their associated uncertainties, were determined as the bi-weight mean and standard deviations of a set of 100 Monte Carlo realizations of the fitting. As expected, the distribution of best-fitting parameters from the Monte Carlo iterations are well-behaved and follow a Gaussian distribution. The bi-weight values measured from those distributions agree very well with those obtained from the direct fitting of the original spectra.

In Fig. 3 we show a few representative stellar velocity and velocity dispersion maps obtained with the V1200 grating. The remaining maps are presented in Appendix A of the Online Material. The four examples shown in the figure illustrate the diversity in the kinematics observed in the survey. NGC 6125, is a slow-rotator (e.g., low velocity amplitude and overall large velocity dispersion). NGC 1167 is an early-type spiral galaxy with large velocity and central velocity dispersion amplitudes. NGC 4210 is a disk-dominated galaxy (e.g., high velocity amplitude and overall small velocity dispersion). ARP 220 is an interacting system (e.g., with complex stellar velocity and velocity dispersions maps).

Figure 4: Distribution of the radial extent of the CALIFA V1200 stellar kinematics. The maximum radius reached in our maps is normalized with the semimajor axis half-light radius (R). The red solid line shows the fraction of galaxies reaching a certain radial extent, as indicated in the right-hand side vertical axis.

4.1 Stellar kinematics coverage

The CALIFA data presented in this paper allow us to produce stellar kinematic maps up to a typical surface brightness level of  19 mag arcsec (and as faint as 20 mag arcsec) in band. We also quantified how far, in terms of R, our maps extend. This is shown in Fig. 4, where we plot the maximum radius reached by the measurements in our V1200 maps over R. More than 90% of the sample covers at least up to 1 R and 39% extends beyond 2 R, with 50% of the galaxies reaching at least 1.8 R. This is a significant improvement over previous IFU surveys (e.g., SAURON, ATLAS), which aimed to probe different properties up to 1 R. The strength of those surveys, however, resides in the study of nearby systems at a higher spatial resolution, which allows them to detect small-scale inner kinematic subcomponents (e.g., McDermid et al. 2006; Krajnović et al. 2011).

4.2 Comparison between V500 and V1200 kinematics

The two instrumental setups used for the CALIFA survey give us the interesting opportunity of measuring the stellar kinematics of galaxies from independent datasets. As described in §3, one of these setups (V500) offers a much lower spectral resolution than the other, which turns out to be not enough to measure the lowest velocity dispersions present in our sample. This issue is clearly shown in Fig. 5, which presents the difference in velocity dispersion for each setup, measured within a 3″diameter aperture centered in each galaxy. In this panel systematic differences appear at dispersion values below 100 km s. We also compared the line-of-sight velocities from each setup (not shown here) and, as expected, found that they are well within the uncertainties of our measurements. Given this limitation, from now on we only report about results coming from the V1200 grating.

4.3 Comparison with other surveys

As an additional test to check the reliability and accuracy of our kinematic extraction, we compared our central velocity dispersion values with those provided by the SDSS DR7 survey (Abazajian et al. 2009) for those galaxies in our sample with SDSS spectroscopy available. We mimicked the SDSS aperture and extracted our velocity dispersions within a 3″ diameter aperture centered in each galaxy. The result of this comparison is presented in Fig. 6. The agreement between the two sets of measurements is good in general showing only a small systematic offset of  km s, which is likely due to differences in the extraction method, set of templates, point-spread function (PSF)/seeing effects, and inaccuracies in the determination of the spectral resolution of both the data and templates. Similar levels of discrepancy and systematic differences have been identified in the past with SDSS DR7 measurements, even using the same SDSS dataset (see Fig. 6 in Oh et al. 2011).

Figure 5: Comparison of the stellar velocity dispersion from the CALIFA survey for the two instrumental setups: V1200 and V500. The dispersions were computed within an aperture of 3″diameter (i.e., equivalent to the SDSS fiber aperture). The vertical dashed lines indicate the spectral resolution of the V1200 ( 72 km s) and V500 ( 139 km s) setups.
Figure 6: Comparison of the stellar velocity dispersion from the CALIFA survey with the officially released SDSS DR7 measurements (Abazajian et al. 2009). The dispersions were computed within an aperture of 3″diameter (i.e., equivalent to the SDSS fiber aperture).
Figure 7: Comparison of the stellar velocity dispersion maps and radial profiles from the CALIFA and ATLAS surveys. Each row shows (from left to right): a color-composite SDSS image of the galaxy, the ATLAS velocity dispersion map, the CALIFA velocity dispersion map, and the radial velocity dispersion profile (extracted in circular apertures). ATLAS measurements are in gray while the CALIFA measurements are in color following the same color scheme of the maps (also indicated with the colorbar below). The black ellipse in the maps indicates one effective radius in those galaxies. This is also indicated in the radial profile panels with a dashed vertical line. Empty (i.e., white) regions within some of the CALIFA maps are areas masked during our kinematic extraction. All velocity dispersion measurements are expressed in km s.
Figure 8: Comparison of the stellar velocity dispersion maps and radial profiles from the CALIFA and DiskMass surveys. Each row shows (from left to right): a color-composite SDSS image of the galaxy, the DiskMass velocity dispersion map (see §5 for details), the CALIFA velocity dispersion map, and the radial velocity dispersion profile (extracted in circular annuli). DiskMass measurements are in gray while the CALIFA measurements are in color following the same color scheme of the maps (also indicated with the colorbar below). Empty (i.e., white) regions within some of the CALIFA maps are areas masked during our kinematic extraction. All velocity dispersion measurements are expressed in km s.

An even more stringent test is the direct comparison of our stellar velocity dispersion maps to those of other surveys. We found up to six objects in common with one of the reference IFU surveys today, which is ATLAS. We focus our test on the velocity dispersion maps, as the velocity maps (not shown here) are in good agreement. The results of this comparison are presented in Fig. 7. By construction, all the ATLAS galaxies are early-type systems, which are predominantly red objects with fairly high central velocity dispersions. The figure shows a color-composite SDSS image of each galaxy in common, as well as the dispersion maps of both surveys and radial velocity dispersion profiles (extracted in circular annuli). The overall agreement between the two surveys is very good, despite differences in S/N thresholds applied in each survey. While the ATLAS data was Voronoi binned to a S/N of 40, we deemed it necessary to adopt a threshold S/N of 20 to find a good balance between spatial resolution (i.e., Voronoi bin sizes) and spatial coverage. The bigger footprint of the PPak IFU allows us to reach well beyond 1 R for most of the sample (see Fig. 2), which is a significant improvement over ATLAS. The large bins are also responsible for the smoother trends observed in the CALIFA radial profiles.

The only major difference between the two datasets, however, is on the central dispersion values. In general, ATLAS values are larger. There are two main reasons that could explain this behavior. The PPak IFU is a fiber bundle made of 2.7″ wide fibers, as opposed to ATLAS with 1″ lenslets. While our dithering strategy during observations (see Sánchez et al. 2012 for details) allowed us to resample our final data cube to 1″ per spaxel, the original fiber size could result in lower velocity dispersion values due to beam smearing. We tested this scenario by comparing the central ATLAS values with those obtained by collapsing the ATLAS spectra within a 3″aperture (similar to a CALIFA fiber). Our results show that velocity dispersion values can decrease by up to 15%. This effect can therefore explain part of the discrepancy between the two surveys.

In addition, the effective PSF measured for the CALIFA survey (García-Benito et al. 2015) could also affect these values. While reported seeing conditions between the two surveys appear similar, if the CALIFA PSF was worse than the ATLAS PSF, this could also explain part of the decrease in the central velocity dispersion. Based on some simulations carried out in the context of another CALIFA paper (Méndez-Abreu et al. 2016, submitted), we estimated that the PSF can account for up to 5% difference in the observed values. On top of that, the level of Voronoi binning could play a similar role, although this seems unlikely in our case as the central spaxels remain mostly unbinned.

5 Reliability of velocity dispersion measurements below the instrumental resolution

An important aspect to consider when extracting stellar kinematics of galaxies is to understand the limiting velocity dispersions one can reach given the spectral resolution provided by the instrument used. The safest option is to use an instrumental setup where the spectral resolution is better than the expected values. Under certain circumstances, however, it is possible to push this limit and measure velocity dispersions below the nominal threshold imposed by the instrument. As shown in Ryś et al. (2013), but see also González (1993) and Pedraz et al. (2002), a combination of high signal-to-noise and spectral sampling of the line spread function (e.g., Koleva et al. 2009) makes it possible to overcome, to some extent, this limitation.

5.1 Comparison between DiskMass and CALIFA datasets

The nominal spectral dispersion of the CALIFA V1200 data is  72 km s. We determined the velocity dispersion limit of our data using as a reference three galaxies in the DiskMass survey (DMS; Bershady et al. 2010). This dataset was designed to measure velocity dispersions in face-on, disk galaxies and the spectral resolution of the instrument (FWHM=0.69Å) was chosen to safely reach values around  17 km s (Martinsson et al. 2013). The PPak was custom built for the DMS and subsequently employed in the CALIFA survey, which can help suppress systematic effects inherent in the analysis. The DMS team has kindly provided their data for three galaxies. One was already in common with the CALIFA survey (NGC 234). We observed two more, specifically for these tests, in 21–23 February 2014 (UGC 4256, UGC 4458), using the same V1200 instrumental configuration of the main survey.

Before carrying out our tests, and to account for potential systematic effects, we checked that neither the method (cross-correlation technique versus pPXF) used to measure the stellar kinematics had a strong impact on the resulting velocity dispersions. Our own extraction, using pPXF, of velocity dispersions from DMS data provided fully consistent results. The choice of templates, whether a single star (as the DMS team used) or a full stellar library (like in our case), did not cause any systematic difference in this particular exercise. Therefore template mismatch is not an issue in these tests. The successful comparison of the two methods using the same data was also reported by Westfall et al. (2011).

In addition to the difference in spectral resolution and template mismatch, there are some further differences with respect to the DMS team analysis that can cause systematic effects in the velocity dispersion values. Spatial binning is desirable in general to reach a threshold S/N, but it can also have the negative effect of artificially broadening the line-of-sight velocity distribution. This is more acute in the outer regions of galaxies, where the S/N drops quickly and the combination of a larger number of spectra is required. Despite this drawback, we chose to Voronoi bin the data to ensure a minimum quality of the spectra used to derive the velocity dispersion. The DMS team preferred to extract their values on single spaxels of 3″ diameter, and remove values with uncertainties larger than 8 km s(see § 7.3.2 of Martinsson et al. 2013).

Another important issue is the wavelength range used to derive the velocity dispersion. The DMS values rely on measurements in the short spectral range between 4980–5370 Å. Our CALIFA values are based on fits between 3750–4550 Å. While a longer baseline is in principle preferred, different spectral features may have slightly distinct intrinsic broadening (at the spectral resolutions we are considering here). We believe this may be the case in the CALIFA spectral range with the Ca H+K lines. We attempted to derive our stellar kinematics ignoring those lines, but results were noisier and uncertainties larger as the fits rely on a few weak spectral features, for example, Fe ( 4383 Å), H, and H. As shown in Kobulnicky & Gebhardt (2000), the Ca H+K lines are reliable features to obtain stellar kinematics in all kinds of galaxies, although their results appeared to be more uncertain for late-type systems. This may be the culprit of some of the differences we see with the DiskMass survey (see below). The detailed characterization of all these effects is a complex task, and even if we could measure the systematic deviations introduced by each effect, it is not totally obvious they would affect different kinds of galaxies in the same manner (e.g., emission-free early-type galaxies versus highly star-forming spiral disks).

Figure 9: Characterization of the biases and relative uncertainties in the velocity dispersions of the CALIFA survey. (Top panel) Ratio between the CALIFA and DMS/ATLAS3D measurements at the locations of the DMS/ATLAS3D values (see §5.2 for details). (Bottom panel) Relative uncertainties in the velocity dispersion values of the CALIFA survey using the Voronoi values and uncertainties for all the galaxies presented here. In both panels, the area delimited by the dotted lines indicates the 16% and 84% percentiles of the distribution of gray points. The solid lines and gray points indicate the median of the distributions.

Figure 8 shows the comparison of our own CALIFA data with the stellar velocity dispersions measured by the DMS team on the three galaxies in common. As in Fig. 7, we plot a SDSS color image of each galaxy, velocity dispersion maps of the two surveys, and also radial profiles (extracted in circular annuli) for a more direct comparison. When displaying the maps and radial profiles, we plot the individual spaxel measurements provided by the DMS team and our Voronoi binned values. The agreement between the two datasets is good overall. We do see discrepancies in some measurements (most noticeable in NGC 234 and UGC 4256). It appears that some of our CALIFA measurements are much larger than those reported by the DMS team at a given radius. We explored the reasons for these discrepant values and concluded that they occur in low surface brightness regions ( 22 mag arcsec) that are often affected by dust or unmasked foreground stars. They often have S/N slightly below the nominal S/N=20 threshold, which is permitted by the Voronoi binning routine within some tolerance (see Cappellari & Copin 2003). These values are also naturally associated with large Voronoi bins, which can also artificially help to increase the broadening. However, the pPXF fits in those regions are not particularly worse than in other areas with similar level of binning, S/N, or surface brightness levels. Given that there might be some physical insight as to why those values are high (e.g., dust obscuration, multiple kinematic components, and kinematic flaring in the outer parts of galaxies), we prefer to keep them in our data release and let the user, based on diagnostic parameters we provide, decide whether to include or exclude them depending on their science case. This effect is not evident in the DMS values owing to the partial field-of-view coverage of their data.

5.2 Limiting velocity dispersion and relative uncertainties

In order to establish the lowest reliable velocity dispersion we can measure, we directly compared the DMS and ATLAS3D values to our CALIFA measurements. This is shown in the top panel of Fig. 9, where we present the ratio of the CALIFA over the DMS and ATLAS3D dispersion values as a function of the DMS or ATLAS3D measurements. This exercise determines at which velocity dispersion values our CALIFA measurements depart systematically from the one-to-one relation. For a fair comparison, we used the Voronoi values of our CALIFA maps at the locations of DMS measurements. This is a better approach than interpolating our maps at those locations, which may produce artifacts. The drawback of this approach is that there is some instrinsic scatter produced by the sampling of our points in locations that could be far from the Voronoi centroids in our data. It is also sensitive to the different levels of scatter of the data points in the surveys (e.g., the scatter of ATLAS3D points is larger than CALIFA, see Fig. 7). While the number of points is not too large for the DMS survey ( 360 measurements), it is enough to compute some statistics. Besides the individual datapoints, we indicate the limiting 16% and 84% percentiles of the distribution with dotted lines. The median of the distribution is marked with a solid line. The panel shows that velocity dispersion values are consistent within the uncertainties down to  40 km s. Below that point, CALIFA measurements are systematically larger up to a factor  3 on average for values around 20 km s. On the high velocity dispersion end, values converge asymptotically to unity, as expected, except for the most massive systems where we suffer the PSF and beam smearing effects discussed in §4.3 for ATLAS3D.

In addition to the potential bias in our measurements, it is also interesting to determine the relative uncertainties of our measurements at different velocity dispersion regimes. This is presented in the bottom panel of Fig. 9. We produced this figure using all the individual Voronoi bin measurements and uncertainties for the 300 CALIFA galaxies presented here. The shaded region and lines as in the top panel. The figure shows that uncertainties are rather small around 5% for  150 km s. Below that value, relative uncertainties increase up to 50% for velocity dispersions  20 km s. The median uncertainty at  40 km s, where our measurements start deviating systematically from the DMS values, is  20%.

Figure 10: Stellar velocity dispersion profiles integrated within elliptical apertures with increasing semimajor radius. The profiles are normalized by the effective velocity dispersion ( within the effective radius (R). The galaxies were divided depending on the shape of their profile: (top panel) declining galaxies and (bottom panel) steadily increasing galaxies, which naturally correspond to early-type and late-type galaxies, respectively. For early-type galaxies, the red line is the average fit taking volume corrections into account. Dashed lines indicate the uncertainty of the fit. For late-type galaxies, dotted lines indicate average fits for different intervals of stellar mass, while solid lines indicate average fits for different intervals of absolute magnitude. For clarity, we did not include the lines with uncertainties in these cases. Averages for late types also take volume corrections into account.

6 Aperture profiles

The large number of galaxies across the Hubble sequence in our study allows us to estimate velocity dispersion aperture corrections for different groups of galaxies. These corrections are useful to homogenize dispersion values measured with fiber-fed spectrographs (e.g., SDSS) for galaxies at different distances, and they are particularly important for high-redshift studies.

We studied the behavior of the integrated velocity dispersion profiles of our galaxies, extracted in elliptical apertures with a fixed position angle and ellipticity (as indicated in Table 1). We chose elliptical rather than circular apertures to properly account for inclination effects. We used the position angle and ellipticity measured in the outer parts of the galaxy (as listed in Table 1). While this choice ignores potential radial variations in these two parameters (e.g., due to bars), the velocity dispersion maps do not appear to be clearly influenced by those photometric deviations. This is true in particular for barred galaxies, as shown in Seidel et al. (2015) or Gadotti et al. (2015).

We found three types of radial profiles: (Class 1) those that decrease steadily, (Class 2) those decreasing up to a certain radius and then increasing again, and (Class 3) those that increase steadily with radius. We analyzed the type of galaxies conforming each class and found that class 1 objects are typically early types (e.g., ellipticals, lenticulars and Sa galaxies). Class 2 is made of a rather small set of objects (20) that are mostly early-type galaxies too. They show fairly high dispersions in the center but also reasonably high rotation in the outer parts, which drive the increase of integrated velocity dispersion at large radii. This effect is even more pronounced in class 3 objects, that are predominantly late-type systems (e.g., Sb, Sc, and Sd galaxies).

Given the small number of objects in class 2, we only provide aperture corrections for the other two groups (class 1 and 3). We followed previous works in the literature and fit the individual profiles of each galaxy in these two classes using a power-law function in the form

(1)

where the effective radius (R) is used as a normalization factor for both the radius and velocity dispersion. An important aspect to consider during the fitting process was the effect of the PSF in our measurements. We account for this effect by convolving our models for each galaxy with the CALIFA PSF during the fitting process. As illustrated in the comparison with the ATLAS survey data (see § 4.3), our velocity dispersions are probably smaller than they should at the very center of galaxies. Ignoring this effect artificially lowers the parameter in the power-law function.

Figure 10 shows the individual profiles for classes 1 and 3. Class 1, in the top panel, is made of predominantly early-type systems with an average stellar mass of M and absolute magnitude M mag. We determined the average profile for the class by weighting with the volume correction factor (V) of each galaxy. That provides a good representation of the average profile for early-type galaxies with those properties. The average fit and uncertainty is indicated with the solid red line and black dashed lines, respectively. The average value of is in good agreement with corrections reported in previous works (e.g., , Jorgensen et al. 1995; , Mehlert et al. 2003; , Cappellari et al. 2006), but see Ziegler & Bender 1997 for a steeper correction using a different prescription.

The family of late-type systems in class 3 is much more heterogeneous. We decided to divide the sample into three intervals of mass and absolute magnitude. As for early-type galaxies, PSF effects and volume corrections were taken into account for the fitting. As illustrated in Fig. 10 (bottom panel) there are significant differences in the slopes as a function of mass and magnitude. Our results indicate that low-mass and/or low-luminosity spiral galaxies display larger values than high-mass and/or bright systems.

7 Summary & conclusions

In this paper we present stellar kinematic maps for a sample of 300 galaxies that are part of the CALIFA survey. The sample covers a wide range of Hubble types, from ellipticals to late-spiral galaxies. This subset is a good representation of the CALIFA mother sample in terms of redshift, isophotal diameter, and absolute magnitude. The large footprint of the PPak IFU, together with the average distance of the survey, allow us to measure stellar kinematics well beyond 1.8 R for 50% of the galaxies, reaching out to 4–5 R in a few exceptional cases. The penalty, caused by the combination of spatial sampling and distance, is the inability to detect kinematically decoupled components at the centers of galaxies. Still our data is well suited for the study of large-scale kinematic twists or long-axis rotation, which occurs in a handful of objects.

The measurements presented in this paper are in good agreement with those of other well-known IFU surveys (e.g., ATLAS and DiskMass). The detailed comparison with the DiskMass survey allowed us to establish that we can measure reliable velocity dispersion values down to  40 km s (i.e.,  30 km s below the instrumental resolution). We also characterized the relative uncertainties of our measurements, which are around 5% for  150 km s. Below that value, relative uncertainties increase up to 50% for velocity dispersions all the way down to  20 km s.

We also took advantage of our large sample to compute integrated stellar velocity dispersion aperture corrections for different sets of galaxies across the Hubble sequence. These corrections are particularly useful to homogenize dispersion values of galaxies at different distances. We find two main classes of integrated aperture radial profiles: steadily decreasing profiles representative of early-type galaxies, and a second class of systematically increasing profiles typical of late-type spiral galaxies. We provide aperture corrections for each class for different stellar masses and absolute magnitudes.

The main properties of the sample and the stellar velocity and velocity dispersion maps introduced in this paper are available as part of the Online Material in Table 1 and Appendix A. The values of the maps themselves, together with many diagnostic parameters to assess the quality of the measurements, will be made available to the community at the CALIFA website (http://califa.caha.es).

Acknowledgements.
We would like to thank the anonymous referee for constructive comments that helped improve some aspects of the original manuscript. We are also grateful to the DiskMass survey team for sharing their data with us for the spectral resolution tests, and to Marc Verheijen and Kyle Westfall in particular for in-depth discussions on the topic. This study makes use of the data provided by the Calar Alto Legacy Integral Field Area (CALIFA) survey (http://califa.caha.es). Based on observations collected at the Centro Astronómico Hispano Alemán (CAHA) at Calar Alto, operated jointly by the Max-Planck-Institut für Astronomie and the Instituto de Astrofísica de Andalucía (CSIC). CALIFA is the first legacy survey being performed at Calar Alto. The CALIFA collaboration would like to thank the IAA-CSIC and MPIA-MPG as major partners of the observatory, and CAHA itself, for the unique access to telescope time and support in manpower and infrastructures. The CALIFA collaboration thanks also the CAHA staff for the dedication to this project.
Funding and financial support acknowledgements: J. F-B. from grant AYA2013-48226-C3-1-P from the Spanish Ministry of Economy and Competitiveness (MINECO). J. F-B. and GvdV from the FP7 Marie Curie Actions of the European Commission, via the Initial Training Network DAGAL under REA grant agreement number 289313. J. M-A. and V. W. acknowledge support from the European Research Council Starting Grant (SEDMorph P.I. V. Wild). P.S-B acknowledge financial support from the BASAL CATA Center for Astrophysics and Associated Technologies through grant PFB-06. R. M. GD. from grant AYA2014-57490-P. R. G-B, R. M. GD. and EP acknowledge support from the project JA-FQM-2828. C. J. W. acknowledges support through the Marie Curie Career Integration Grant 303912. L. G. from the Ministry of Economy, Development, and Tourism’s Millennium Science Initiative through grant IC120009 awarded to The Millennium Institute of Astrophysics (MAS), and CONICYT through FONDECYT grant 3140566. I. M. from grant AYA2013-42227-P.
Galname ID PA Type M M R R
(deg) (10 M) (mag) (arcsec) (arcsec)
IC0480 159 0.015 167 0.77 Sc 1.41 -20.43 24 38
IC0540 274 0.007 170 0.63 Sab 0.75 -19.27 14 27
IC0674 381 0.025 117 0.63 Sab 8.09 -22.07 9 36
IC0944 663 0.023 105 0.65 Sab 18.20 -22.37 19 37
IC1079 781 0.029 82 0.51 E4 21.23 -23.21 37 26
IC1151 817 0.007 28 0.63 Scd 0.70 -20.26 22 37
IC1199 824 0.016 157 0.57 Sb 4.67 -21.46 20 28
IC1256 856 0.016 89 0.36 Sb 2.00 -21.18 17 25
IC1528 005 0.013 75 0.56 Sbc 1.39 -20.95 23 41
IC1652 037 0.017 171 0.72 S0a 4.09 -21.20 11 26
IC1683 043 0.016 15 0.35 Sb 3.88 -21.11 13 26
IC1755 070 0.026 155 0.75 Sb 8.43 -21.69 11 31
IC2101 144 0.015 144 0.72 Scd 1.74 -20.81 25 44
IC2247 186 0.014 148 0.79 Sab 3.24 -20.75 21 41
IC2487 273 0.015 162 0.79 Sc 2.48 -21.05 25 40
IC4566 807 0.019 161 0.41 Sb 8.99 -21.96 15 27
IC5309 906 0.014 25 0.55 Sc 1.89 -20.61 17 24
IC5376 001 0.017 3 0.69 Sb 4.52 -21.10 16 36
MCG-01-54-016 878 0.010 32 0.78 Scd 0.11 -18.77 24 38
MCG-02-02-030 013 0.012 171 0.56 Sb 2.34 -20.88 19 38
MCG-02-02-040 016 0.012 53 0.47 Scd 0.99 -20.19 20 34
MCG-02-03-015 032 0.019 22 0.74 Sab 4.24 -21.41 12 37
MCG-02-51-004 868 0.019 159 0.64 Sb 4.79 -21.69 17 34
NGC0001 008 0.015 107 0.32 Sbc 6.31 -21.73 12 30
NGC0023 009 0.015 177 0.30 Sb 10.96 -22.47 17 26
NGC0036 010 0.020 24 0.48 Sb 7.87 -22.33 21 33
NGC0155 018 0.021 167 0.14 E1 15.00 -22.41 15 25
NGC0160 020 0.018 49 0.47 Sa 10.72 -22.18 22 35
NGC0169 022 0.015 90 0.47 Sab 39.90 -21.87 34 34
NGC0171 023 0.013 32 0.05 Sb 5.26 -21.84 26 32
NGC0177 024 0.013 8 0.42 Sab 2.34 -20.70 13 37
NGC0180 025 0.018 167 0.34 Sb 8.36 -22.31 28 41
NGC0192 026 0.014 168 0.57 Sab 7.05 -21.59 22 39
NGC0214 028 0.015 50 0.26 Sbc 6.65 -22.16 18 31
NGC0216 027 0.005 25 0.71 Sd 0.19 -18.99 20 35
NGC0217 029 0.013 112 0.74 Sa 12.50 -21.90 23 41
NGC0234 031 0.015 64 0.20 Sc 4.50 -21.91 20 34
NGC0237 030 0.014 175 0.32 Sc 2.04 -21.14 15 35
NGC0257 033 0.018 88 0.36 Sc 6.22 -22.15 21 40
NGC0364 035 0.017 35 0.28 E7 9.16 -21.56 15 24
NGC0429 036 0.019 15 0.78 Sa 6.22 -21.26 6 29
NGC0444 039 0.016 158 0.74 Scd 0.74 -20.23 23 32
NGC0447 038 0.019 74 0.13 Sa 13.52 -22.40 28 31
NGC0477 042 0.020 128 0.50 Sbc 3.14 -21.69 21 45
NGC0496 045 0.020 32 0.46 Scd 2.59 -21.40 19 35
NGC0499 044 0.015 72 0.33 E5 25.18 -22.48 21 31
NGC0504 046 0.014 44 0.60 S0 2.95 -20.76 8 30
NGC0517 047 0.014 24 0.49 S0 6.64 -21.35 10 34
NGC0528 050 0.016 57 0.52 S0 7.48 -21.68 12 28
NGC0529 051 0.016 13 0.09 E4 12.25 -22.27 12 37
NGC0551 052 0.017 137 0.56 Sbc 4.38 -21.52 19 44
NGC0681 061 0.006 65 0.33 Sa 3.10 -20.71 30 37
NGC0741 068 0.019 85 0.22 E1 32.89 -23.47 35 32
NGC0755 069 0.005 49 0.61 Scd 0.24 -19.43 28 39
NGC0768 071 0.023 33 0.61 Sc 3.48 -21.78 15 34
NGC0774 072 0.015 164 0.18 S0 8.39 -21.55 12 26
NGC0776 073 0.016 41 0.10 Sb 4.94 -21.82 19 32
NGC0781 074 0.012 11 0.70 Sa 2.96 -20.80 8 32
NGC0810 076 0.026 27 0.34 E5 35.65 -22.84 17 20
NGC0825 077 0.011 50 0.51 Sa 2.64 -20.70 12 34
NGC0932 087 0.014 65 0.08 S0a 9.20 -22.10 18 33
NGC1056 100 0.005 153 0.32 Sa 1.05 -19.94 14 37
NGC1060 101 0.017 75 0.18 E3 70.15 -23.62 27 26
NGC1093 108 0.018 99 0.39 Sbc 3.25 -21.49 13 36
NGC1167 119 0.016 62 0.23 S0 49.20 -22.98 24 30
NGC1349 127 0.022 50 0.12 E6 8.47 -22.44 17 21
NGC1542 131 0.012 131 0.59 Sab 2.74 -20.74 15 23
NGC1645 134 0.016 84 0.57 S0a 6.78 -21.81 13 39
NGC1677 143 0.009 137 0.71 Scd 0.38 -19.46 12 29
NGC2253 147 0.012 109 0.32 Sbc 3.34 -21.55 15 36
NGC2347 149 0.015 9 0.36 Sbc 8.71 -22.12 18 42
NGC2410 151 0.016 34 0.68 Sb 7.62 -21.86 21 37
NGC2449 156 0.016 135 0.52 Sab 7.28 -21.68 16 33
NGC2476 160 0.012 136 0.29 E6 6.32 -21.58 9 22
NGC2480 161 0.008 167 0.43 Sdm 0.83 -19.75 35 25
NGC2481 162 0.007 6 0.16 S0 4.83 -20.80 9 34
NGC2486 163 0.015 92 0.44 Sab 3.96 -21.30 15 29
NGC2487 164 0.016 132 0.15 Sb 5.90 -22.19 28 35
NGC2513 171 0.016 174 0.27 E2 34.59 -22.86 26 32
NGC2540 183 0.021 131 0.39 Sbc 3.32 -21.62 14 33
NGC2553 188 0.016 67 0.50 Sb 6.89 -21.30 9 19
NGC2554 189 0.014 160 0.19 S0a 16.33 -22.59 19 38
NGC2592 201 0.007 45 0.22 E4 4.15 -20.72 9 28
NGC2604 209 0.007 48 0.12 Sd 0.46 -20.24 26 36
NGC2639 219 0.011 130 0.35 Sa 14.72 -22.33 17 38
NGC2730 232 0.013 80 0.12 Scd 1.31 -20.94 24 39
NGC2880 272 0.005 142 0.36 E7 4.69 -21.10 18 36
NGC2906 275 0.007 82 0.44 Sbc 2.46 -20.79 19 33
NGC2916 277 0.012 19 0.36 Sbc 5.66 -22.09 26 40
NGC2918 279 0.023 75 0.31 E6 27.73 -22.78 12 28
NGC3057 312 0.005 23 0.27 Sdm 0.12 -19.17 32 34
NGC3106 311 0.021 116 0.10 Sab 16.29 -22.79 21 32
NGC3158 318 0.023 165 0.19 E3 54.70 -23.70 32 32
NGC3160 319 0.023 140 0.76 Sab 8.99 -21.51 15 36
NGC3300 339 0.010 173 0.46 S0a 5.78 -21.41 13 32
NGC3303 340 0.020 159 0.51 S0a 11.51 -22.33 15 21
NGC3381 353 0.005 43 0.14 Sd 0.48 -20.08 24 42
NGC3615 387 0.022 42 0.42 E5 24.15 -22.98 15 18
NGC3687 414 0.008 151 0.06 Sb 1.88 -20.97 17 30
NGC3811 436 0.010 171 0.23 Sbc 2.65 -21.40 21 39
NGC3815 437 0.012 67 0.50 Sbc 2.25 -21.05 14 34
NGC3994 476 0.010 8 0.49 Sbc 2.65 -21.22 9 26
NGC4003 479 0.022 168 0.28 S0a 11.83 -22.00 14 22
NGC4047 489 0.011 97 0.26 Sbc 4.86 -21.90 16 33
NGC4149 502 0.010 85 0.60 Sa 2.30 -20.63 18 36
NGC4185 515 0.013 164 0.33 Sbc 4.69 -21.88 30 38
NGC4210 518 0.009 97 0.24 Sb 1.93 -20.98 21 36
NGC4470 548 0.008 179 0.32 Sc 0.98 -20.72 15 33
NGC4644 569 0.016 57 0.71 Sb 2.82 -21.03 12 29
NGC4676A 577 0.022 2 0.85 Sdm 6.50 -22.17 38 31
NGC4676B 2999 0.022 43 0.44 S0 7.18 -22.09 15 23
NGC4711 580 0.014 41 0.47 Sbc 2.05 -21.05 17 33
NGC4816 588 0.023 80 0.31 E1 32.06 -23.03 30 30
NGC4841A 589 0.023 42 0.11 E3 35.16 -22.83 20 25
NGC4874 592 0.024 46 0.23 E0 49.54 -24.11 55 29
NGC4956 602 0.016 39 0.17 E1 9.68 -22.38 9 21
NGC4961 603 0.009 100 0.31 Scd 0.48 -20.25 15 33
NGC5000 608 0.019 1 0.24 Sbc 5.37 -21.81 16 30
NGC5016 611 0.009 57 0.23 Sbc 1.72 -21.06 17 35
NGC5029 612 0.029 149 0.40 E6 31.77 -23.28 25 28
NGC5056 614 0.019 3 0.44 Sc 3.02 -21.82 15 38
NGC5205 630 0.006 169 0.35 Sbc 0.73 -20.12 19 41
NGC5216 633 0.010 33 0.32 E0 3.20 -21.07 20 26
NGC5218 634 0.010 101 0.14 Sab 4.49 -21.43 18 36
NGC5378 676 0.010 86 0.22 Sb 3.83 -21.27 24 34
NGC5406 684 0.018 111 0.29 Sb 18.75 -22.57 20 40
NGC5480 707 0.006 41 0.18 Scd 1.38 -20.76 25 41
NGC5485 708 0.006 174 0.32 E5 10.57 -21.95 31 38
NGC5520 715 0.006 63 0.49 Sbc 0.73 -20.18 12 34
NGC5614 740 0.013 128 0.19 Sa 19.86 -22.77 18 35
NGC5630 749 0.009 93 0.70 Sdm 0.47 -20.37 22 38
NGC5631 744 0.007 30 0.06 S0 8.47 -21.74 19 34
NGC5633 748 0.008 16 0.26 Sbc 1.82 -20.94 13 35
NGC5657 754 0.013 164 0.63 Sbc 1.92 -20.98 10 39
NGC5682 758 0.008 125 0.76 Scd 0.25 -19.39 26 38
NGC5720 764 0.026 131 0.44 Sbc 7.05 -22.29 16 27
NGC5732 768 0.013 43 0.48 Sbc 0.85 -20.46 14 32
NGC5784 778 0.018 19 0.13 S0 16.44 -22.61 13 29
NGC5797 780 0.013 130 0.45 E7 7.01 -22.13 18 31
NGC5876 787 0.011 51 0.59 S0a 7.96 -21.41 12 31
NGC5888 789 0.029 150 0.38 Sb 16.07 -22.74 16 31
NGC5908 791 0.011 154 0.36 Sa 16.71 -22.17 34 42
NGC5930 795 0.009 161 0.54 Sab 4.30 -21.36 16 37
NGC5934 796 0.019 24 0.59 Sb 8.75 -21.80 13 36
NGC5947 4034 0.020 61 0.15 Sbc 3.48 -21.56 13 32
NGC5953 801 0.007 43 0.10 Sa 3.01 -21.09 10 34
NGC5966 806 0.015 83 0.39 E4 10.21 -22.08 18 34
NGC5971 804 0.011 132 0.56 Sb 2.07 -20.80 12 27
NGC5980 810 0.014 11 0.60 Sbc 5.25 -21.81 17 40
NGC5987 809 0.010 62 0.65 Sa 16.22 -22.15 33 37
NGC6004 813 0.013 91 0.20 Sbc 4.86 -21.86 22 37
NGC6020 815 0.014 133 0.31 E4 10.02 -22.08 19 25
NGC6021 816 0.016 157 0.27 E5 10.14 -21.88 9 27
NGC6032 820 0.014 0 0.38 Sbc 3.37 -21.30 27 39
NGC6060 821 0.015 102 0.57 Sb 8.59 -22.23 28 35
NGC6063 823 0.010 156 0.44 Sbc 1.38 -20.55 20 36
NGC6081 826 0.017 128 0.59 S0a 13.12 -21.95 12 30
NGC6125 829 0.015 4 0.04 E1 24.21 -22.86 21 28
NGC6132 831 0.017 125 0.64 Sbc 1.63 -21.04 14 31
NGC6146 832 0.029 73 0.24 E5 42.56 -23.48 15 26
NGC6150 835 0.029 58 0.45 E7 26.67 -22.65 11 29
NGC6168 841 0.009 110 0.77 Sc 0.73 -20.00 26 34
NGC6173 840 0.029 144 0.37 E6 53.09 -23.85 38 32
NGC6186 842 0.010 49 0.23 Sb 3.71 -21.24 20 35
NGC6278 844 0.009 126 0.42 S0a 8.30 -21.49 11 33
NGC6301 849 0.028 108 0.40 Sbc 10.42 -22.76 24 39
NGC6310 848 0.011 69 0.72 Sb 3.64 -20.99 23 33
NGC6314 850 0.022 173 0.47 Sab 16.26 -22.46 12 37
NGC6338 851 0.027 15 0.38 E5 49.09 -23.48 28 26
NGC6394 857 0.028 42 0.64 Sbc 7.87 -21.87 14 24
NGC6411 859 0.012 65 0.35 E4 12.08 -22.42 34 33
NGC6427 860 0.011 34 0.57 S0 5.64 -21.37 8 34
NGC6478 862 0.023 34 0.63 Sc 10.33 -22.57 23 38
NGC6497 863 0.010 112 0.51 Sab 10.89 -22.09 13 34
NGC6515 864 0.023 12 0.35 E3 15.60 -22.73 19 28
NGC6762 867 0.010 119 0.72 Sab 2.42 -20.46 9 31
NGC6941 869 0.021 131 0.26 Sb 8.77 -22.39 20 32
NGC6945 870 0.013 127 0.36 S0 24.49 -21.91 13 31
NGC6978 871 0.020 126 0.57 Sb 10.79 -22.15 18 34
NGC7025 874 0.017 39 0.32 S0a 33.65 -22.73 13 31
NGC7047 876 0.019 107 0.45 Sbc 6.18 -21.83 18 29
NGC7194 881 0.027 18 0.30 E3 27.86 -23.05 17 22
NGC7311 886 0.015 9 0.47 Sa 11.72 -22.45 12 37
NGC7321 887 0.024 14 0.32 Sbc 8.53 -22.48 15 32
NGC7364 889 0.016 65 0.32 Sab 7.62 -22.04 12 32
NGC7436B 893 0.025 41 0.15 E2 82.04 -23.50 27 27
NGC7466 896 0.025 25 0.62 Sbc 5.60 -21.86 13 31
NGC7489 898 0.021 160 0.47 Sbc 3.17 -22.07 20 39
NGC7549 901 0.016 16 0.60 Sbc 3.97 -21.75 20 34
NGC7550 900 0.017 154 0.09 E4 27.04 -22.89 24 25
NGC7562 903 0.012 83 0.32 E4 17.66 -22.54 20 36
NGC7563 902 0.014 149 0.47 Sa 9.18 -21.54 9 31
NGC7591 904 0.017 150 0.46 Sbc 5.75 -21.91 16 33
NGC7608 907 0.012 18 0.73 Sbc 1.24 -20.00 20 33
NGC7611 908 0.011 134 0.55 S0 7.93 -21.32 11 21
NGC7619 911 0.013 50 0.17 E3 8.79 -22.69 35 34
NGC7623 912 0.012 7 0.30 S0 9.57 -21.47 10 31
NGC7625 913 0.005 10 0.04 Sa 1.33 -20.26 14 34
NGC7631 914 0.013 76 0.62 Sb 3.38 -21.10 17 33
NGC7653 915 0.014 -11 0.18 Sb 3.16 -21.58 12 38
NGC7671 916 0.013 133 0.37 S0 9.04 -21.76 11 26
NGC7683 917 0.012 138 0.48 S0 10.45 -21.74 14 33
NGC7684 919 0.017 22 0.66 S0 9.68 -21.69 9 38
NGC7691 920 0.013 171 0.21 Sbc 1.64 -21.34 28 34
NGC7711 923 0.014 92 0.55 E7 11.30 -22.02 15 42
NGC7716 924 0.009 31 0.19 Sb 2.45 -21.04 21 38
NGC7722 925 0.013 148 0.27 Sab 17.58 -22.05 21 24
NGC7738 927 0.023 34 0.59 Sb 12.00 -22.23 14 37
NGC7783NED01 932 0.026 120 0.54 Sa 28.51 -22.59 15 31
NGC7787 933 0.022 104 0.71 Sab 4.18 -21.17 11 23
NGC7800 937 0.006 44 0.61 Ir 0.19 -19.56 32 37
NGC7819 003 0.017 105 0.41 Sc 2.45 -21.06 23 37
NGC7824 006 0.020 143 0.37 Sab 17.62 -22.26 11 38
UGC00005 002 0.024 44 0.53 Sbc 6.78 -22.09 16 33
UGC00029 004 0.029 173 0.30 E1 10.86 -22.66 17 13
UGC00036 007 0.021 18 0.61 Sab 10.05 -21.69 10 20
UGC00148 012 0.014 96 0.75 Sc 1.29 -20.75 20 36
UGC00312 014 0.014 7 0.46 Sd 0.60 -20.69 20 38
UGC00335NED02 017 0.018 149 0.49 E4 6.07 -21.39 18 24
UGC00809 040 0.014 23 0.81 Scd 0.49 -19.72 20 36
UGC00841 041 0.019 54 0.77 Sbc 1.03 -20.26 17 31
UGC00987 049 0.016 30 0.64 Sa 4.11 -21.21 12 34
UGC01057 053 0.021 152 0.69 Sc 1.27 -20.81 14 27
UGC01271 059 0.017 99 0.47 S0a 6.71 -21.42 9 29
UGC02222 103 0.017 96 0.57 S0a 5.53 -21.42 10 23
UGC02229 104 0.024 177 0.52 S0a 7.76 -22.03 19 25
UGC02403 115 0.014 153 0.59 Sb 3.19 -20.80 19 26
UGC03151 135 0.015 93 0.73 Sa 5.71 -21.41 20 30
UGC03253 146 0.014 87 0.47 Sb 2.69 -21.16 15 33
UGC03539 148 0.011 117 0.69 Sc 0.70 -19.69 20 38
UGC03899 150 0.013 44 0.70 Sd 0.17 -19.23 9 30
UGC03944 152 0.013 120 0.57 Sbc 0.99 -20.42 17 33
UGC03969 153 0.027 134 0.78 Sb 4.78 -21.19 15 29
UGC03995 155 0.016 90 0.56 Sb 8.36 -22.12 25 39
UGC04029 157 0.015 63 0.79 Sc 2.14 -20.75 26 37
UGC04132 165 0.017 27 0.69 Sbc 5.82 -21.81 22 35
UGC04145 167 0.016 138 0.53 Sa 9.10 -21.43 9 29
UGC04197 174 0.015 130 0.79 Sab 5.15 -20.92 18 41
UGC04280 185 0.012 3 0.68 Sb 1.37 -20.29 11 36
UGC04308 187 0.012 113 0.14 Sc 1.84 -21.29 24 33
UGC04722 231 0.006 31 0.79 Sdm 0.05 -18.18 32 38
UGC05108 278 0.027 138 0.60 Sb 7.74 -22.12 9 19
UGC05113 281 0.023 41 0.74 S0a 12.62 -21.76 8 22
UGC05498NED01 314 0.021 61 0.79 Sa 6.38 -21.36 13 31
UGC05598 326 0.019 35 0.74 Sb 1.71 -20.75 15 27
UGC05771 341 0.025 60 0.33 E6 20.75 -22.35 12 27
UGC05990 361 0.005 15 0.74 Sc 0.16 -18.32 12 33
UGC06036 364 0.022 100 0.73 Sa 14.86 -21.93 11 38
UGC06312 386 0.021 49 0.64 Sab 10.74 -21.91 13 29
UGC07012 486 0.010 12 0.51 Scd 0.28 -19.91 14 30
UGC07145 500 0.022 151 0.63 Sbc 2.26 -21.14 16 32
UGC08107 593 0.028 53 0.68 Sa 11.64 -22.56 16 33
UGC08231 606 0.008 73 0.66 Sd 0.14 -19.28 19 33
UGC08234 607 0.027 133 0.45 S0 13.65 -22.76 8 24
UGC08733 657 0.008 21 0.44 Sdm 0.26 -19.75 30 40
UGC08778 664 0.011 116 0.70 Sb 1.76 -20.30 15 27
UGC08781 665 0.025 160 0.40 Sb 11.38 -22.37 15 29
UGC09067 714 0.026 12 0.54 Sbc 3.82 -21.85 14 28
UGC09476 769 0.011 132 0.34 Sbc 1.61 -20.95 21 40
UGC09537 774 0.029 140 0.79 Sb 16.60 -22.64 20 40
UGC09542 775 0.018 34 0.70 Sc 2.07 -20.96 21 37
UGC09665 783 0.009 138 0.73 Sb 0.99 -19.99 18 33
UGC09873 797 0.019 126 0.75 Sb 1.25 -20.38 21 33
UGC09892 798 0.019 101 0.69 Sbc 1.98 -20.71 16 26
UGC10097 814 0.020 114 0.18 E5 28.71 -22.73 14 27
UGC10123 818 0.013 53 0.77 Sab 3.32 -20.55 18 31
UGC10205 822 0.022 133 0.38 S0a 9.93 -22.32 19 35
UGC10257 825 0.013 162 0.78 Sbc 1.21 -20.47 20 38
UGC10297 827 0.008 179 0.83 Sc 0.29 -19.11 18 40
UGC10331 828 0.015 140 0.76 Sc 0.77 -20.43 19 41
UGC10337 830 0.029 63 0.72 Sb 10.79 -22.17 17 26
UGC10380 834 0.029 108 0.79 Sb 10.21 -21.85 12 35
UGC10384 837 0.017 92 0.73 Sb 1.87 -20.73 11 35
UGC10388 838 0.015 128 0.70 Sa 6.56 -21.19 11 28
UGC10650 843 0.010 22 0.78 Scd 0.20 -19.32 23 43
UGC10693 845 0.028 103 0.37 E7 32.14 -23.39 22 31
UGC10695 846 0.028 110 0.35 E5 19.95 -22.70 24 27
UGC10710 847 0.028 147 0.65 Sb 9.68 -22.12 20 36
UGC10796 852 0.010 59 0.42 Scd 0.28 -19.56 20 32
UGC10811 854 0.029 91 0.66 Sb 7.48 -21.92 12 29
UGC10905 858 0.027 173 0.56 S0a 40.46 -22.92 15 25
UGC10972 861 0.016 54 0.78 Sbc 2.66 -21.22 24 34
UGC11228 865 0.019 178 0.33 S0 12.39 -22.10 12 33
UGC11649 872 0.013 63 0.22 Sab 3.70 -21.38 19 32
UGC11680NED01 873 0.026 57 0.46 Sb 12.39 -22.56 16 28
UGC11717 877 0.021 37 0.61 Sab 6.95 -21.84 17 39
UGC12054 885 0.007 47 0.74 Sc 0.10 -18.41 15 33
UGC12127 888 0.027 0 0.11 E1 23.39 -23.47 36 25
UGC12185 890 0.022 159 0.56 Sb 4.68 -21.56 12 33
UGC12274 894 0.026 143 0.68 Sa 14.19 -22.08 17 27
UGC12308 895 0.008 118 0.79 Scd 0.11 -18.88 27 38
UGC12494 905 0.014 37 0.67 Sd 0.28 -19.67 20 43
UGC12518 910 0.009 23 0.64 Sb 1.80 -19.45 17 34
UGC12519 909 0.015 157 0.70 Sc 1.09 -20.56 21 34
UGC12723 926 0.018 75 0.82 Sc 0.76 -19.77 17 27
UGC12810 929 0.027 56 0.61 Sbc 5.43 -22.01 20 35
UGC12816 930 0.018 140 0.50 Sc 0.66 -20.63 16 34
UGC12857 934 0.008 35 0.72 Sbc 0.56 -19.49 19 36
UGC12864 935 0.016 110 0.61 Sc 1.13 -20.69 27 38
VV488NED02 892 0.016 70 0.77 Sb 2.32 -20.96 23 33

Note. – Col. 1: galaxy name. Col. 2: CALIFA identification number for each galaxy. Col. 3 redshift of the galaxy from SDSS (Abazajian et al. 2009). Col. 4: position angle of the galaxy measured in the outer parts, using SDSS images. Col. 5: average ellipticity measured in the outer parts of the galaxy, using SDSS images. Col. 6: Hubble type of the galaxy from Walcher et al. (2014). Col. 7: total stellar mass of the galaxy, measured as described in Walcher et al. (2014). Col. 8: total absolute magnitude in band from SDSS (Abazajian et al. 2009). Col. 9: effective radii (in arcsec) of the galaxy, measured as described in Walcher et al. (2014). Col. 10: maximum radial extent of our kinematic maps (in arcsec).

Table 1: Basic properties of the CALIFA stellar kinematics sample

Appendix A Stellar kinematic maps

This online material presents all the stellar velocity (Figs. A1–A17) and velocity dispersion (Figs. A18–A34) maps extracted from the V1200 grating used in this paper. The complete sample comprises 300 galaxies of Hubble morphological types ranging from ellipticals to late-type spirals. Velocity maps are in km s and use a fixed range in the interval [,150] km s. Velocity dispersion maps are also expressed in km s and use a fixed range from 20 to 300 km s. Color schemes as in Fig. 3. Overlaid contours come from SDSS band images and have been limited to the isophote reaching 2 R. All panels cover an area of 80″100″.

\forloop

ct10¡18

Figure 11: Stellar velocity maps from the CALIFA V1200 dataset.
\forloop

ct10¡18

Figure 12: Stellar velocity dispersion maps from the CALIFA V1200 dataset.

Footnotes

  1. Extended sample galaxies, two objects in this study, have CALIFA IDs larger than 1000. See Table 1 and Sánchez et al. (2016) as well.
  2. Total absolute magnitudes are used throughout this paper, except in Fig. 2 where petrosian magnitudes are employed instead for consistency with Walcher et al. (2014).
  3. We define our S/N as the average within the spectral range used in the fitting process.

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