EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 544 (August 2012) A&A, 544 (2012) A101 Full HTML
Free Access
Issue
A&A
Volume 544, August 2012
Article Number A101
Number of page(s) 32
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/201219312
Published online 07 August 2012

© ESO, 2012

1. Introduction

Nearly a decade has passed since the launch of the Galaxy Evolution Explorer (GALEX; Martin et al. 2005) in 2003. By carrying out the first all-sky survey at ultraviolet (UV) wavelengths, GALEX has opened a new parameter space for extragalactic studies, providing a direct estimate of the current star formation rate in relatively dust-free environments. Particularly powerful for our understanding of star formation in galaxies has been the combination of GALEX observations with multiwavelength datasets tracing dust-obscured star formation (e.g., Kennicutt et al. 2003) and the various components of the interstellar medium (ISM; e.g., Bigiel et al. 2008). Consequently, UV observations have now become a necessary ingredient of any multiwavelength survey focused on the study of the physical mechanisms regulating the star formation cycle in galaxies.

In this paper, we present GALEX observations of the galaxies in the Herschel Reference Survey (HRS, Boselli et al. 2010), a Herschel guaranteed time project focused on the study of the interplay between dust, gas and star formation in the local Universe. This dataset complements the Local Volume GALEX survey (Lee et al. 2011), since it includes more massive systems and it covers a wider range of environments. Thus, once combined, these two surveys provide us with a complete view of UV properties of galaxies within  ~25 Mpc from us.

Given the large apparent size of the galaxies in the HRS, GALEX observations are particularly suitable to investigate the UV morphology of nearby galaxies and their connection to internal galaxy properties and environment. Indeed, still very little is known about the UV structural properties of the local galaxy population.

The first systematic investigation of the GALEX UV morphology in nearby galaxies has been presented in the seminal work carried out by Gil de Paz et al. (2007). Although this analysis has been followed by several studies focused on colour gradients (e.g., Muñoz-Mateos et al. 2007), UV extended disks (e.g., Thilker et al. 2007; Lemonias et al. 2011), ellipticals (e.g., Jeong et al. 2009) and dwarf galaxies (e.g., Zhang et al. 2012), we are still missing an accurate quantification of the UV structural scaling relations of nearby galaxies. Firstly, it is still unknown whether structural scaling relations such as the stellar mass vs. size, stellar mass vs. surface brightness and size vs. surface brightness, which represent important constraints for theoretical models, hold also at UV wavelengths. Since UV is the ideal tracer of current star formation in the unobscured outer parts of galaxies, the UV scaling relations can provide us with strong constraints on the growth of the stellar disk and on the origin of the extended UV-disk phenomenon. Secondly, while several studies have highlighted how well UV traces the Hi content in the outer part of galaxies (Bigiel et al. 2010b,a; Lemonias et al. 2011), it still has to be proven that such a tight relation holds for the whole galaxy population. Thirdly, it is still unclear how the cluster environment affects the UV morphology. Although previous studies have shown that in Hi-deficient/cluster galaxies the extent of the Hα disk is significantly reduced compared to those in Hi-normal/field systems (Koopmann & Kenney 2004b,a; Koopmann et al. 2006; Boselli & Gavazzi 2006), it is still unclear if the UV disks are truncated as well and whether or not the Hi stripping affects the star formation in the central regions of stripped galaxies (Boselli & Gavazzi 2006).

For all these reasons, in this paper we investigate the UV structural properties of galaxies in the HRS in order to determine how they vary with galaxy properties, gas content and environment. As a by product of this analysis we present, in addition to the GALEX data, optical photometry and structural parameters in the SDSS g, r and i bands.

This work is part of our current effort to make publicly available to the astronomical community all the multiwavelength datasets collected for the HRS (e.g., Bendo et al. 2012; Boselli et al. 2012; Ciesla et al. 2012; Hughes et al. 2012), thus enhancing the legacy value of this survey in the years to come.

2. The data

2.1. Sample selection

The HRS is a volume-limited sample (i.e., 15 ≤ Dist. ≤ 25 Mpc) including late-type galaxies (Sa and later) with 2MASS (Skrutskie et al. 2006) K-band magnitude KStot ≤ 12 mag and early-type galaxies (S0a and earlier) with KStot ≤ 8.7 mag. Additional selection criteria are high galactic latitude (b >  +55°) and low Galactic extinction (AB < 0.2 mag, Schlegel et al. 1998), to minimize Galactic cirrus contamination. The original selection included 323 galaxies (261 late- and 62 early-types), although later one galaxy (HRS228) was excluded due to a wrong redshift reported in NED (see also Cortese et al. 2012).

2.2. GALEX observations

In order to obtain homogeneous near-ultraviolet (NUV; λ = 2316 Å: Δλ = 1069 Å) and far-ultraviolet (FUV; λ = 1539 Å: Δλ = 442 Å) data with an exposure time of at least  ~1.5 ks (corresponding to a surface brightness limit of  ~28.5 mag arcsec-2) for all the galaxies in the HRS observable by GALEX, we were awarded 112.5 ks as part of the legacy GI Cycle 6 proposal Completing the GALEX coverage of the Herschel Reference Survey (P.I. L. Cortese). Unfortunately, before the start of Cycle 6 observations, the FUV detector experienced an over-current condition and shut down, and GALEX officially moved to NUV-only operations. In addition, subsequent problems to the NUV detector and budget cuts on the mission did not allow GALEX to complete the GI and MIS surveys as planned.

For all these reasons, the GALEX coverage of the HRS remains heterogeneous and the observations here presented come from a combination of our GI proposal with data from the GALEX Ultraviolet Virgo Cluster Survey (GUViCS, Boselli et al. 2011) and archival observations publicly available as part of the GALEX GR6 data release. In detail, NUV observations are available for all HRS galaxies observable by GALEX (310 galaxies1): 285 galaxies have been observed with exposure time longer than 1 ks (82 from our proposal and the rest as part of the Nearby Galaxy Survey, Medium Imaging Survey and other Guest Investigator programs), while the remaining 26 galaxies have a typical integration time of  ~200 s and come mainly from the All Sky Imaging Survey (AIS). FUV observations are available for 302 galaxies: 167 with exposure times longer than 1 ks and the rest coming from the AIS. However, for 29 galaxies the AIS tiles were either too shallow to detect the target or the galaxy was on the edge of the field making impossible to perform reliable photometry. Thus, in this paper we present FUV magnitudes and structural parameters for just 273 galaxies (~85% of the HRS). All frames have been reduced using the current version of the GALEX pipeline (ops-v72). Details about the GALEX satellite and data reduction can be found in Martin et al. (2005) and Morrissey et al. (2007).

Table 6 lists some general properties of the HRS galaxies, the GALEX tiles and corresponding exposure times used in this work.

2.3. SDSS optical data

We combine the GALEX data with g, r and i band images for the 313 HRS galaxies included in the Sloan Digital Sky Survey DR7 (SDSS-DR7, Abazajian et al. 2009) footprint. For those cases where our target was present in more than one SDSS frame, we used the imcombine task in IRAF3 to create a mosaic and recover all the emission from the galaxy at least up to the optical radius.

thumbnail Fig. 1

Comparison of the FUV (left) and NUV (center) magnitudes and effective radii (right) presented here with the ones obtained by Gil de Paz et al. (2007, G07). The average differences (i.e., this work – G07) and standard deviations are shown in each panel. The dotted lines indicate the 1-to-1 relation.

3. Photometry

Both the GALEX and SDSS pipelines are not optimized for extended sources, thus we performed our own photometry starting from the reduced and calibrated frames. The SDSS and FUV images were registered to the NUV frame using the wcsmap and geotran tasks in IRAF and convolved to the NUV resolution (5.3′′, Morrissey et al. 2007). In the few cases for which GALEX images were not available, we just re-binned the SDSS frames to the same pixel size of GALEX data (1.5 arcsec) and convolved them to the NUV resolution.

Surface brightness photometry was performed using a modified version of the GALPHOT (Haynes et al. 1999) IRAF package, adapted in order to work on GALEX data. Sky background was determined in rectangular regions around the target, chosen independently in each band to avoid background/foreground sources, artifacts and the emission from the target. The mean sky value was then subtracted from each frame. Background/foreground sources and artifacts were then masked in each image independently and a final mask was created by merging all the pixels masked in the different bands. Isophotal ellipses were fitted to each sky-subtracted image by using the IRAF task ellipse. When available, the SDSS i-band frame was used to define the galaxy center, ellipticity and position angle. Otherwise, we used either the ellipticity and position angle listed in the RC3 catalogue (de Vaucouleurs et al. 1991) or determined it directly from the images available. Center, position angle and ellipticity were then kept fixed. The fitting always started with the most central ellipse having a major axis radius of 6 arcsec, increasing linearly by 3 arcsec for each step. Uncertainties on surface brightnesses and integrated magnitudes within each ellipse were determined following Gil de Paz et al. (2007) and Boselli et al. (2003, see also Muñoz-Mateos et al. 2009). Asymptotic magnitudes in each band have been estimated through a linear weighted fit of the growth curve, following the technique described by Gil de Paz et al. (2007, see also Cairós et al. 2001). In the rest of the paper, we will consider the asymptotic magnitudes as our best estimate for the total galaxy flux in each band. All magnitudes are given in the AB system.

It is important to note that the estimated uncertainties in the magnitudes are a combination of the error on the sky background determination, the Poisson error on the incident flux and the calibration error (0.05, 0.03, 0.03, 0.02, 0.03 mag in FUV, NUV, g, r, i, respectively; Morrissey et al. 2007; Abazajian et al. 2009). Thus, they do not take into account possible additional flat-field variations across the frame (affecting mainly extended sources) and the fact that shallow ( ≲ 200 s) GALEX images are not background- but source-limited. Indeed, by comparing independent GALEX observations of the same target, we find a standard deviation in the recovered asymptotic magnitude of  ~0.1–0.15 mag, i.e., larger than the typical errors obtained following the standard procedure described above (i.e.  ~0.06 in FUV and 0.04 NUV, see also Sect. 3.1).

From the surface brightness profiles, we also determined integrated magnitudes within the optical diameters given in Table 6, effective radii (Re, i.e. the radius containing 50% of the total light), effective surface brightnesses ( ⟨ μe ⟩ , i.e., the average surface brightness inside Re) and isophotal radii. The isophotal radii have been computed at surface brightness levels of 23.5, 24, 24.5, 28 and 28 mag arcsec-2 in i, r, g, NUV and FUV, respectively. These values roughly correspond to the average surface brightness at the optical diameter observed for the whole sample. Asymptotic magnitudes are on average  ~0.1 ± 0.1 mag brighter than those estimated up to the optical diameter. The typical uncertainty in the effective and isophotal radii varies between  ~20% in FUV to  ≲ 10% in the other bands. Similarly, the error on the effective surface brightness increases from  ≲ 0.1 mag for the SDSS and NUV bands to  ~0.20 mag in FUV. The larger uncertainty in the FUV structural parameters is simply due to the fact that nearly half of the FUV photometry has been performed on shallow AIS frames. This must be taken into account when a comparison between the scaling relations in the two GALEX bands is performed.

Finally, we note that we did not apply any corrections for inclination to our photometry and structural parameters, since they are usually highly uncertain (e.g., Giovanelli et al. 1994, 1995). Although our approach may artificially increase the scatter in some of the scaling relations investigated in the rest of the paper (in particular the ones involving radii), the main conclusions of our work are not affected.

The results of our photometry (not corrected for Galactic extinction) are presented in Tables 7 and 8. Notes on individual problematic or peculiar objects are given in Appendix A.

The results of our photometry as well as the GALEX data are publicly available on the Herschel Database in Marseille (HeDaM, http://hedam.oamp.fr/).

thumbnail Fig. 2

The basic UV and optical properties of the HRS. Top row: the NUV (left) and FUV (right) magnitude distribution. The solid line shows the local GALEX UV luminosity function presented by Wyder et al. (2005). Middle row: g − i (left) and NUV − i (right) colour vs. stellar mass relations. Bottom row: FUV − i (left) and FUV − NUV (right) colour vs. stellar mass relations. Colours are corrected for Galactic extinction only. Galaxies are colour-coded accordingly to their morphological type: red triangles are E-dE, purple squares S0-S0a, green pentagons Sa-Sab and black circles Sb and later types.

3.1. Comparison with the literature

In order to check the reliability of the GALEX measurements presented here, we compare our UV asymptotic magnitudes with the values obtained by Gil de Paz et al. (2007). This is the only UV catalogue currently available with significant overlap with the HRS: i.e., 62 and 52 galaxies in NUV and FUV, respectively. The results of this comparison are shown in Fig. 1. Overall there is good agreement between the two studies, with a typical scatter of  ~0.12 mag in NUV,  ~0.17 mag in FUV magnitudes and  ~0.08 dex in effective radius. However, we find a systematic offset between the two compilations, with our fluxes being  ~0.1 mag fainter in both NUV and FUV than Gil de Paz et al. (2007). This is most likely due to the change in flux calibration since the GALEX GR1 release (used by Gil de Paz et al. 2007), as also suggested by the fact that no systematic offset is seen when the effective radii are considered.

It is well known that the automatic SDSS photometry pipeline is not reliable for extended sources, such as the HRS sample. This is mainly due to problems in background subtraction and blending of large galaxies into multiple sources (Bernardi et al. 2007; West et al. 2010). Indeed, a comparison between our asymptotic magnitudes and the cmodel SDSS magnitudes given in NED shows an average difference of  ~−0.8 ± 1.0 mag, with our estimates being brighter. Luckily, some galaxies in our sample have already been remeasured as part of previous studies and we compared our asymptotic magnitudes with the published values. We find an average difference between our values and those published of  ~0.00 ± 0.05,  ~0.08 ± 0.12 and  −0.01 ± 0.08 mag for the samples of Muñoz-Mateos et al. (2009), McDonald et al. (2011) and Chen et al. (2010)4, respectively. These small differences are likely due to the different techniques used to determine total magnitudes. We can thus assume a typical uncertainty of  ~0.1 mag in the SDSS magnitudes presented in Table 7.

thumbnail Fig. 3

The effective surface brightness vs. effective radius (top row), effective surface brightness vs. stellar mass (middle row) and stellar mass vs. effective radius (bottom row) relations in i (left), NUV (center) and FUV (right), respectively. Symbols are as in Fig. 2.

4. Basic properties

Figure 2 provides a general overview of the UV and optical properties of the HRS. The top row shows the NUV and FUV luminosity distributions (corrected for Galactic extinction following Wyder et al. 2007) for all the galaxies for which GALEX observations are available. For comparison, the local UV GALEX luminosity function of Wyder et al. (2005) is presented. This has been arbitrarily normalized to match the bright-end of the luminosity distribution of the HRS. Our sample turns out to be a good representation of the UV luminosity distribution in the local Universe up to M(NUV)  ~ −15 and M(FUV)  ~ −16 mag, whereas at lower luminosities we under-sample the population of faint UV sources. In addition to the well known Malmquist bias, this result is due to the HRS being a K-band selected sample, thus missing low-mass star-forming galaxies.

The middle and bottom rows of Fig. 2 highlight the different behavior of our sample in different colour vs. stellar mass relations. As already shown by several works (e.g., Boselli et al. 2005; Gil de Paz et al. 2007; Wyder et al. 2007; Cortese & Hughes 2009; Cortese 2012), the UV-to-optical colours are much more powerful than the optical-only colours in separating the blue cloud from the red sequence. Only with UV magnitudes, a colour cut is almost as effective as a morphological classification in separating early- and late-type galaxies, even before any correction for internal dust attenuation. It is also interesting to note how the various morphological types behave when moving from an UV-to-optical to a FUV-NUV colour vs. stellar mass diagram. The blue cloud becomes almost a blue sequence, while for early-type galaxies the scatter significantly increases, and galaxies are dispersed across a range of more than 2 mag in colour. Finally, we remind the reader that the absence of red sequence galaxies for stellar masses lower than 1010   M⊙ is a consequence of the different K-band magnitude selection used for early- and late-type galaxies. This can be easily seen by comparing Fig. 2 with Fig. 1 of Hughes & Cortese (2009), who used the same K-band magnitude cut for all galaxies, regardless of their morphology.

5. Structural scaling relations

thumbnail Fig. 4

Same as Fig. 3, but for Hi-normal (filled circles) and Hi-deficient (empty circles) spirals (Sa and later types). The predictions of the dust-free models by Boissier & Prantzos (2000) for spin parameter 0.02 (dotted), 0.04 (solid) and 0.06 (dashed) are superposed in green. The red lines indicate the same predictions once the effect of dust attenuation is included.

The structural parameters presented in Table 8 allow us to investigate, for the first time, how the scaling relations between stellar mass (M∗), effective radius (Re) and effective surface brightness ( ⟨ μe ⟩ , corrected for Galactic extinction) behave when UV bands are considered here. The main results of this analysis are shown in Figs. 3 and 4, where the effective radii vs. stellar mass, effective surface brightness vs. stellar mass and effective radii vs. effective surface brightness relations in i, NUV and FUV are shown in the left, central and right column, respectively. Galaxies are separated accordingly to their morphological type in Fig. 3 and to their Hi content in Fig. 4. We computed the Hi-deficiency5 (DefHI) parameter for the HRS galaxies following Cortese et al. (2011). Atomic hydrogen masses have been estimated from Hi 21 cm line emission data (mainly single-dish), available from the literature (e.g., Springob et al. 2005; Giovanelli et al. 2007; Kent et al. 2008; Gavazzi et al. 2003, and the NASA/IPAC Extragalactic Database, NED). We adopted a threshold of DefHI = 0.5 to separate Hi-normal (filled blue circles) from Hi-deficient galaxies (empty circles). We assumed (1)where SHI is the integrated Hi line flux-density. In Table 4, we present the Pearson correlation and bisector6 best linear fit coefficients (Isobe et al. 1990) for all the three scaling relations considered here.

Table 4

Best bysector linear fitting, Pearson correlation coefficients and dispersion perpendicolar to the best-fit for the structural scaling relations presented in Sect. 5.

Starting from the bottom panels of Fig. 3, the M∗ vs. Re does not show any strong wavelength dependence and the effective radii always increase monotonically with stellar mass. Interestingly, at both optical and UV wavelengths, different morphological types follow slightly different relations, with early-type systems having smaller sizes (i.e., being more compact) than late type systems, at fixed stellar mass (e.g., Scodeggio et al. 2002; Shen et al. 2003). No offset is found between Hi-normal and Hi-deficient galaxies (see Fig. 4) in the optical, whereas in UV Hi-deficient galaxies seem to have smaller radii than Hi-normal systems. However, this difference is marginally significant. As already mentioned in Sect. 3, at this stage, it is impossible to determine whether the larger scatter observed for early-type galaxies in FUV is real or it is just a consequence of the larger errors in the estimate of the FUV structural parameters.

The most remarkable difference between UV and optical structural scaling relations is clearly seen in the M∗ vs.  ⟨ μe ⟩  relation. While in i-band the effective surface brightness becomes brighter at increasing stellar mass, the opposite trend is seen in UV. A similar result was found by Gavazzi et al. (1996), who investigated the relation between UV, B- and H-band surface brightness (normalized to the optical radius) and H-band luminosity for a large sample of cluster spirals. This is the first time that such trend is confirmed with GALEX UV data, and for a sample covering the whole range of morphologies. Our result implies that, at least in the local Universe, a UV-selected sample would preferentially be biased towards low-mass, actively star-forming galaxies, missing the more massive systems.

In order to determine the origin of such remarkable inversion in the M∗ vs.  ⟨ μe ⟩  relation, it is important to examine separately different morphological types. Indeed, while in spirals it is fair to assume that most of the UV photons come from young stellar populations, this is not the case for early-type galaxies where NUV and FUV fluxes are likely contaminated by more evolved stellar populations (e.g., O’Connell 1999; Boselli et al. 2005; Donas et al. 2007; Han et al. 2007). If we focus our attention on E and S0 only (red triangles and purple squares in Fig. 3), we no longer see an inversion in the M∗ vs.  ⟨ μe ⟩  relation. At all wavelengths, the two quantities are only weakly correlated (see also Table 4) and the only significant difference between the optical and UV relations is that early-type systems are the objects with the brightest optical and faintest UV surface brightness in our sample. This is just a natural consequence of the fact that the luminosity of early-type galaxies decreases by  ~3 orders of magnitude when moving from the optical to the UV regime, but the effective radius stays almost the same, explaining why the effective surface brightness decreases so remarkably.

Much more intriguing is the relation between M∗ and  ⟨ μe ⟩  for late-type galaxies. Here, we always find an inverse correlation between M∗ and  ⟨ μe ⟩  in optical, and a direct correlation in the UV, regardless of the criteria used to divide our sample (e.g., by morphology or gas content7, see Table 4 and Figs. 3 and 4). The fact that more massive disks have brighter effective surface brightness is a well known property of late-type spirals (Gavazzi et al. 1996; Scodeggio et al. 2002) and it is usually interpreted as a consequence of the fact that massive disks have built up their stellar mass at earlier epochs than smaller systems following an inside-out growth of the stellar disk (e.g., Dalcanton et al. 1997; Boissier & Prantzos 2000; Dutton 2009). Thus, massive galaxies have already consumed a significant fraction of their gas reservoir in the center and their star formation surface density is lower than in dwarf systems. In other words, this is just a consequence of the anti-correlation between specific star formation rate and stellar mass (e.g., Salim et al. 2007). Of course, it is important to remember that the surface brightnesses values shown in Figs. 3 and 4 are not corrected for internal dust attenuation, and thus part of this trend might just be a consequence of the fact that more massive star-forming systems are more affected by dust than dwarf galaxies. However, in our mass range, the dependence of extinction on stellar mass is quite weak, and it could introduce a spurious systematic trend of  ~1–1.5 mag (e.g., Cortese et al. 2006; Iglesias-Páramo et al. 2006): i.e., enough to flatten the relation, but insufficient to explain the reversal of the M∗ vs.  ⟨ μe ⟩  in UV.

In order to test this interpretation, we compared our observations with the predictions of the multi-zone chemical and spectrophotometric model of Boissier & Prantzos (2000), updated with an empirically determined star formation law (Boissier et al. 2003) relating the star formation rate to the total gas surface density. The only two free parameters in this model are the spin parameter λ (e.g., Mo et al. 1998), and the rotational velocity, VC. The star formation history of a galaxy depends on the infall timescale, which is a function of VC. Thus, VC controls the stellar mass accumulated during the history of the galaxy, and λ its radial distribution. We assumed an age of 13.5 Gyr, varied the spin parameter λ from 0.02 to 0.09 (with 0.01 step) and considered six different values for VC = 40, 80, 150, 220, 290, 360 km s-1. We adopted both the dust-free model and the reddened one obtained as described in Boissier & Prantzos (1999). Finally, the model stellar masses have been converted from a Kroupa et al. (1993) IMF, used in the model, to a Chabrier (2003) IMF by adding 0.06 dex (Bell et al. 2003; Gallazzi et al. 2008). This model is able to reproduce the integrated properties (Boissier & Prantzos 2000) as well as the surface brightness profiles (Muñoz-Mateos et al. 2011) of nearby late-type galaxies, thus it is an ideal tool for a comparison with our scaling relations.

thumbnail Fig. 5

The radius vs. stellar mass and radius vs. Hi mass relations in FUV (top row), NUV (middle row) and i-band (bottom row). The relations obtained for effective and isophotal radii are shown in the left and right panels, respectively. Hi-normal and Hi-deficient spirals are indicated by filled and empty circles, respectively. Purple squares are S0 and S0a and red triangles E and dE.

The goal of this exercise is just to establish whether or not this simple model is consistent with our interpretation, not to determine the best set of parameters fitting our observations. Since the model is based on pure disk galaxies, in Fig. 4 we compare the theoretical predictions with our observations for late-type galaxies only. We also separate Hi-normal (filled circles) from Hi-deficient galaxies (empty circles), in order to note any environmentally-driven trend in our analysis. The green and red tracks show the dust-free and reddened model, respectively. Spin parameters λ = 0.02, 0.04 and 0.06 are indicated by the dotted, solid and dashed line, respectively. In general, the model is able to reproduce the same trends observed in the data. In particular, the reversal in the M∗ vs.  ⟨ μe ⟩  relation when moving from the optical to the UV is recovered. Moreover, as expected, although such change in slope is observed in both the dust-free and reddened model, the inclusion of the effects of dust makes it stronger. Finally, we note that the same scenario had been proposed by Gil de Paz et al. (2007) to explain the flattening of the UV surface brightness profiles in the inner parts of late-type spirals. We can thus conclude that the opposite trends observed in the optical and UV M∗ vs.  ⟨ μe ⟩  relations are a natural consequence of the inside-out growth of stellar disks. It would be really interesting to investigate how this relation evolves with redshift, and at which epoch this reversal starts to appear.

The same scenario invoked to explain the M∗ vs.  ⟨ μe ⟩  relation is valid for the  ⟨ μe ⟩  vs. Re relation (see Fig. 4, top row). Contrary to the previous case, here only a very weak correlation is observed in i-band, while in UV the surface brightness monotonically increases with effective radius. Moreover, in this case, early- and late-type galaxies show a similar trend although, at fixed radius, ellipticals and lenticulars are offset towards lower surface brightness than late-type systems. This is just a consequence of the different origin of the UV emission in the two morphological classes. Interestingly, the UV  ⟨ μe ⟩  vs. Re relation is the one where Hi-deficient and Hi-normal galaxies show the largest difference, with Hi-poor systems having smaller radii and fainter  ⟨ μe ⟩  than Hi-normal objects. As discussed in Sect. 7, this is consistent with a scenario in which the gas stripping is followed by the outside-in quenching of the star formation.

6. Effective vs. isophotal radii

In the previous section we have focused our attention on the main scaling relations involving stellar mass and effective quantities. However, several studies have shown that isophotal radii often provide better (i.e., less scattered) scaling relations than effective ones. Some examples are the velocity vs. size relation, the stellar mass/luminosity vs. size relation (Saintonge & Spekkens 2011) and the Hi mass vs. size relation, which is also usually adopted to calibrate the Hi deficiency parameter (Haynes & Giovanelli 1984; Solanes et al. 1996). Thus, in Fig. 5, we investigate whether isophotal radii provide less scattered relations than effective ones, by comparing the size vs. stellar mass and size vs. Hi mass relations in i, NUV and FUV bands. The properties of the best bisector linear fits are given in Table 5.

Remarkably, when isophotal radii are used, the scatter in the size vs. Hi mass relation decreases significantly when moving from the optical to the UV, while the opposite trend is seen in the isophotal size vs. stellar mass relation. Thus, for the stellar mass vs. optical radius and Hi mass vs. UV radius relations, isophotal radii provide the smallest scatters. The dispersion in the i-band size vs. stellar mass relation decreases from  ~0.11 to  ~0.07 dex for E+S0a and from 0.20 to 0.13 dex for Hi-normal spirals, when effective radii are replaced by isophotal measurements. Similarly, the scatter in the UV size vs. Hi mass relation for Hi-normal late-type galaxies decreases from  ~0.17 to  ~0.1 dex in both NUV and FUV.

Table 5

Best bysector linear fitting, Pearson correlation coefficients and dispersion perpendicolar to the best-fit for the radius vs. stellar mass and radius vs. Hi mass presented in Fig. 5.

Our findings are easily explained by the fact that, contrary to effective radii, isophotal sizes are not affected by the presence of a central light concentration (such a bulge or a bar), and thus they represent a better proxy for the extent of the optical/UV disk. However, it is important to note that such a significant difference between isophotal and effective radii is only found when we consider two quantities that are expected to be strongly correlated by default: i.e., stellar mass vs. size of the stellar disk and Hi mass vs. size of the UV disk. Indeed, as shown in Tables 4 and 5, no strong difference in the scatter of the i-band size vs. Hi mass relation or the UV size vs. stellar mass relation for late-type galaxies is found when isophotal radii are used.

Looking at Fig. 5, it is important to note the behavior of Hi-deficient galaxies in the radius vs. Hi mass relation. In i-band they are, by definition, systematically offset from the relation of Hi-normal galaxies, since the optical isophotal radius is indeed used to estimate the Hi-deficiency parameter. However, such offset gradually disappears when moving from optical to UV radii, suggesting that the UV is a very good tracer of atomic hydrogen (Donas et al. 1987; Bigiel et al. 2010a; Catinella et al. 2010; Cortese et al. 2011), regardless of the evolutionary history (e.g., secular or environmentally driven) of the galaxy.

The extremely tight optical RISO vs. stellar mass and UV RISO vs. Hi mass relations suggest that the extent of the star-forming (i.e., UV) disk normalized to the stellar mass (i.e., optical) one should be strongly correlated to the Hi gas fraction of a galaxy. Indeed, this is exactly what we find, as shown in Fig. 6. Intriguingly, all galaxies detected in Hi seem to follow the same relation, supporting the idea that the UV and Hi emission from galaxies are tightly linked, in particular in the outer (dust-free) star-forming disk (Bigiel et al. 2010a), and have similar variations with galaxy properties and environment (see also next section). This result suggests that the amount of Hi (per unit of stellar mass) directly regulates the inside-out growth of the disk in spiral galaxies. This supports the recent results of Wang et al. (2011) who showed that, at fixed stellar mass, the higher the Hi gas fraction of a galaxy the bluer and more actively star-forming its outer disk is.

thumbnail Fig. 6

The Hi gas fraction vs. the NUV-to-i (left) and FUV-to-i (right) isophotal radii ratios. Symbols are as in Fig. 5. Arrows indicates Hi non detections.

thumbnail Fig. 7

Top row: the g- (left), NUV- (middle), FUV-to-i (right) effective radius ratio vs. Hi deficiency. Bottom row: Same for the isophotal radius ratio. Symbols are as in Fig. 3.

thumbnail Fig. 8

The inner (left) and outer (right) NUV-i (top) and FUV-i (bottom) colour vs. stellar mass. Symbols are as in Fig. 4. The right-most panel shows the inner-outer NUV-i (top) and FUV-i (bottom) colour difference as a function of stellar mass. The large circles show the averages for each sub-sample in different bins of stellar mass.

7. The effect of the Virgo cluster on the UV morphology

The results presented in the previous section provide direct evidence of the strong connection between Hi content and extent of the UV star-forming disk. Since Hi removal is one of the most dramatic effects of the environment on cluster spirals (Cayatte et al. 1990; Cortese et al. 2008, 2011; Chung et al. 2009), it is interesting to investigate what happens to the extent of the UV disk when the Hi is gradually removed from the galaxy. Figure 6 already suggests that the UV disk should shrink in Hi-poor galaxies, and this is clearly visible in Fig. 7, where we plot the ratio of the g-to-i, NUV-to-i and FUV-to-i effective (top row) and isophotal radii (bottom row) as a function of Hi-deficiency. The quantity in the y-axis is sometimes referred to as the truncation parameter (i.e., the ratio of the truncation radius to the radius of the old stellar population, Boselli & Gavazzi 2006) and it has been used to investigate the effect of the environment on the Hi (Cayatte et al. 1990; Chung et al. 2009), Hα (Boselli & Gavazzi 2006; Rose et al. 2010) and dust (Cortese et al. 2010) disks in Hi-deficient galaxies. This is the first time that such technique is applied to UV and optical data.

If we focus on the ratio of isophotal radii, we clearly find that the extent of the NUV and FUV disks monotonically decreases with increasing Hi-deficiency (r ~ −0.6 and  − 0.7). However, this trend is strong only for late-type galaxies (i.e., Sa and later), whereas it becomes weaker for ellipticals and lenticulars (r ~ −0.35). This is not extremely surprising. Firstly, as already mentioned, in early type galaxies the UV emission may not trace young stellar populations, making it more difficult to justify a physical link between UV emission and Hi content. Secondly, the Hi deficiency is not well calibrated for early-type galaxies and it is not even clear whether a concept of Hi-deficiency is justified (Cortese et al. 2011). This suggests that the truncation parameter may not be a good indicator of environmental effects for early-type galaxies.

In the case of late-type galaxies, we find a clear change of slope in the relation between the truncation parameter and Hi-deficiency when moving from the FUV to the g-band. In particular, no trend is observed in the optical, suggesting that the truncation process has been quite recent and it has significantly affected only the UV morphology of the galaxy. This is entirely consistent with the predictions for a ram pressure stripping scenario (Boselli et al. 2006, 2008).

By comparing the top and bottom rows of Fig. 7, it is clear that such a clean result is not obtained if effective radii are used to estimate the truncation parameter. Although a mild trend is still visible in the UV, it is more scattered and less significant. Indeed, the Pearson correlation coefficient for late-type galaxies decreases to  ~−0.3. This is just a natural consequence of the fact that the Hi stripping, the quenching of the star formation and the truncation of the UV disk start from the outskirts of a galaxy and gradually reach its inner parts. Thus, the effective radii are significantly less affected than the isophotal ones.

In order to find additional support to this conclusion, we can investigate how much the colour (and thus the specific star formation rate, e.g., Schiminovich et al. 2007) in the inner regions of Hi-deficient galaxies are affected by the Virgo cluster environment. Thus, in Fig. 8 we compare the inner and outer NUV − i (top panel) and FUV − i (bottom panel) colour vs. stellar mass relation for Hi-normal and Hi-deficient spiral galaxies. Here, with inner and outer colours we refer to the colour inside and outside the i-band effective radius. We clearly find two different behaviors inside and outside the effective radius. In the inner parts, at fixed stellar mass, the colour of Hi-deficient galaxies is just  ~0.5–1 mag redder than the one observed in Hi-normal galaxies, and it follows a colour vs. stellar mass relation very similar to the one observed in Fig. 2. Conversely, in the outer parts, Hi-deficient systems are significantly redder (~1.5–2 mag) than Hi-normal galaxies and it is unclear if they still follow a colour vs. stellar mass relation. This automatically implies that, at fixed stellar mass, the shape of the colour gradients in Hi-deficient galaxies is altered (right panel in Fig. 8) and, in the more extreme cases, its slope could even be inverted, i.e., showing inner parts bluer than the outer disk (see also Boselli et al. 2006).

This result does not only confirm that the inner parts of the UV disk are less affected during the stripping phase, but it also shows that no increase in the unobscured star formation activity follows the truncation of the star-forming disk. This is a very important result for our understanding of the effects of the environment on star formation. Indeed, previous studies of the Hα morphology in nearby clusters have proposed an alternative scenario to ram pressure to explain the reduced extent of the star-forming disk in Hi-deficient galaxies (e.g., Moss et al. 1998; Moss & Whittle 2000). They suggested that the gravitational interaction could drive the flow of a significant fraction of gas into the central regions of a galaxy, triggering a starburst and thus altering the spatial distribution of the star-forming regions. Our findings seem to definitely rule out such a scenario, at least in environments similar to the Virgo cluster. Of course, our analysis cannot exclude the presence of a completely dust obscured starburst phase, and we will investigate this issue in a future paper. However, given the fact that ultra-luminous infrared galaxies are usually not observed in nearby clusters (Boselli & Gavazzi 2006), we consider this possibility quite unlikely.

8. Summary and conclusion

In this paper we have presented UV and optical photometry and structural parameters for the HRS, a magnitude-, volume-limited sample of nearby galaxies in different environments. We used this new dataset to investigate, for the first time, the UV scaling relations and the effects of the environment on the UV morphology of nearby galaxies. Our results can be summarized as follows:

  • We find a clear change of slope in the stellar mass vs. effectivesurface brightness relation when moving from the optical to theUV. In other words, massive galaxies have higher optical andlower UV surface brightnesses than less massive systems. Bycomparing our observations with the prediction of a simplemulti-zone chemical model, we show that this is a directconsequence of the inside-out growth of the galactic diskcombined with the fact that more massive systems have growntheir disk earlier than dwarf galaxies.

  • We show that isophotal radii almost always provide the tightest correlations with stellar and Hi masses than effective sizes. Particularly remarkable is the very low scatter in the correlation between UV isophotal radii and Hi mass, suggesting that the extent of the star-forming disk is directly linked to the amount of Hi in a galaxy. This conclusion is further confirmed by the fact that the ratio of UV-to-optical radius strongly correlates with the Hi gas fraction.

  • We show that the tight connection between Hi content and size of the UV disk is also valid when environmentally perturbed Hi-deficient galaxies are included. In particular, we find a strong correlation between the size of the UV disk (normalized to the optical radius) and Hi-deficiency. Moreover, we show that, while the UV colour of the outer disk of Hi-deficient galaxies is significantly redder than that in Hi-normal galaxies, the galaxy center is less affected by the removal of the Hi. This is consistent with a simple truncation of the star-forming disk without any significant enhancement of the star formation in the inner parts.

In conclusion, all our results are consistent with a steady inside-out growth of the UV disk in nearby galaxies via consumption of their Hi reservoir. Such growth can be stopped and even reversed only if the atomic hydrogen is removed via some kind of environmental effect.

1

13 galaxies cannot be observed because too close to bright stars exceeding the counts rate allowed by the NUV detector.

3

IRAF is distributed by the National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy (AURA) under cooperative agreement with the National Science Foundation.

4

In this case, we considered the values obtained from the growth curve analysis since much more similar to the technique adopted here.

5

The Hi-deficiency (DefHI) is defined as the difference, in logarithmic units, between the expected Hi mass for an isolated galaxy with the same morphological type and optical diameter of the target and the observed value (Haynes & Giovanelli 1984).

6

We decided to use the bisector fit in order to provide a direct comparison with theoretical models, but our conclusions do not qualitatively change if other linear regression techniques are adopted. We remind the reader that, by construction, the bisector best-fit must not be used to predict one quantity from the other (Isobe et al. 1990).

7

Even in this case, at fixed stellar mass, Hi-deficient galaxies seem to show marginally fainter  ⟨ μe ⟩  compared to Hi-normal galaxies.

Acknowledgments

We wish to thank the GALEX Team for their help and assistance during the planning of the observations and the GALEX TAC for allocating time to the HRS and GUViCS surveys. L.C. thanks Peppo Gavazzi for inspiring part of this work and Barbara Catinella, Peppo Gavazzi and Elysse Voyer for useful comments. We thank the referee for useful suggestions that improved the clarity of this work. GALEX (Galaxy Evolution Explorer) is a NASA Small Explorer, launched in April 2003. We gratefully acknowledge NASA’s support for construction, operation, and science analysis for the GALEX mission, developed in cooperation with the Centre National d’Etudes Spatiales (CNES) of France and the Korean Ministry of Science and Technology. Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the US Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. The SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions. The Participating Institutions are the American Museum of Natural History, Astrophysical Institute Potsdam, University of Basel, University of Cambridge, Case Western Reserve University, University of Chicago, Drexel University, Fermilab, the Institute for Advanced Study, the Japan Participation Group, Johns Hopkins University, the Joint Institute for Nuclear Astrophysics, the Kavli Institute for Particle Astrophysics and Cosmology, the Korean Scientist Group, the Chinese Academy of Sciences (LAMOST), Los Alamos National Laboratory, the Max-Planck-Institute for Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico State University, Ohio State University, University of Pittsburgh, University of Portsmouth, Princeton University, the United States Naval Observatory, and the University of Washington. This research has made use of the NASA/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; and of the GOLDMine database (Gavazzi et al. 2003). The research leading to these results has received funding from the European Community’s Seventh Framework Programme (/FP7/2007-2013/) under grant agreement No. 229517.

References

  1. Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., et al. 2009, ApJS, 182, 543 [NASA ADS] [CrossRef] [Google Scholar]
  2. Arrigoni Battaia, F., Gavazzi, G., Fumagalli, M., et al. 2012, A&A, 543, A112 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  3. Bell, E. F., McIntosh, D. H., Katz, N., & Weinberg, M. D. 2003, ApJS, 149, 289 [NASA ADS] [CrossRef] [Google Scholar]
  4. Bendo, G. J., Galliano, F., & Madden, S. C. 2012, MNRAS, 423, 197 [NASA ADS] [CrossRef] [Google Scholar]
  5. Bernardi, M., Hyde, J. B., Sheth, R. K., Miller, C. J., & Nichol, R. C. 2007, AJ, 133, 1741 [NASA ADS] [CrossRef] [Google Scholar]
  6. Bettoni, D., Buson, L. M., & Galletta, G. 2010, A&A, 519, A72 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  7. Bigiel, F., Leroy, A., Walter, F., et al. 2008, AJ, 136, 2846 [NASA ADS] [CrossRef] [Google Scholar]
  8. Bigiel, F., Leroy, A., Seibert, M., et al. 2010a, ApJ, 720, L31 [NASA ADS] [CrossRef] [Google Scholar]
  9. Bigiel, F., Leroy, A., Walter, F., et al. 2010b, AJ, 140, 1194 [NASA ADS] [CrossRef] [Google Scholar]
  10. Binggeli, B., Sandage, A., & Tammann, G. A. 1985, AJ, 90, 1681 [NASA ADS] [CrossRef] [Google Scholar]
  11. Boissier, S., & Prantzos, N. 1999, MNRAS, 307, 857 [NASA ADS] [CrossRef] [Google Scholar]
  12. Boissier, S., & Prantzos, N. 2000, MNRAS, 312, 398 [NASA ADS] [CrossRef] [Google Scholar]
  13. Boissier, S., Prantzos, N., Boselli, A., & Gavazzi, G. 2003, MNRAS, 346, 1215 [NASA ADS] [CrossRef] [Google Scholar]
  14. Boselli, A., & Gavazzi, G. 2006, PASP, 118, 517 [NASA ADS] [CrossRef] [Google Scholar]
  15. Boselli, A., Sauvage, M., Lequeux, J., Donati, A., & Gavazzi, G. 2003, A&A, 406, 867 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  16. Boselli, A., Cortese, L., Deharveng, J. M., et al. 2005, ApJ, 629, L29 [NASA ADS] [CrossRef] [Google Scholar]
  17. Boselli, A., Boissier, S., Cortese, L., et al. 2006, ApJ, 651, 811 [NASA ADS] [CrossRef] [Google Scholar]
  18. Boselli, A., Boissier, S., Cortese, L., & Gavazzi, G. 2008, ApJ, 674, 742 [NASA ADS] [CrossRef] [Google Scholar]
  19. Boselli, A., Eales, S., Cortese, L., et al. 2010, PASP, 122, 261 [NASA ADS] [CrossRef] [Google Scholar]
  20. Boselli, A., Boissier, S., Heinis, S., et al. 2011, A&A, 528, A107 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  21. Boselli, A., Hughes, T. M., Cortese, L., & Gavazzi, G. 2012, A&A, submitted [Google Scholar]
  22. Cairós, L. M., Caon, N., Vílchez, J. M., González-Pérez, J. N., & Muñoz-Tuñón, C. 2001, ApJS, 136, 393 [NASA ADS] [CrossRef] [Google Scholar]
  23. Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245 [NASA ADS] [CrossRef] [Google Scholar]
  24. Catinella, B., Schiminovich, D., Kauffmann, G., et al. 2010, MNRAS, 403, 683 [NASA ADS] [CrossRef] [Google Scholar]
  25. Cayatte, V., van Gorkom, J. H., Balkowski, C., & Kotanyi, C. 1990, AJ, 100, 604 [NASA ADS] [CrossRef] [Google Scholar]
  26. Chabrier, G. 2003, PASP, 115, 763 [NASA ADS] [CrossRef] [Google Scholar]
  27. Chen, C.-W., Côté, P., West, A. A., Peng, E. W., & Ferrarese, L. 2010, ApJS, 191, 1 [NASA ADS] [CrossRef] [Google Scholar]
  28. Chung, A., van Gorkom, J. H., Kenney, J. D. P., Crowl, H., & Vollmer, B. 2009, AJ, 138, 1741 [NASA ADS] [CrossRef] [Google Scholar]
  29. Ciesla, L., Boselli, A., Smith, M. W. L., et al. 2012, A&A, 543, A161 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  30. Cortese, L. 2012, A&A, 543, A132 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  31. Cortese, L., & Hughes, T. M. 2009, MNRAS, 400, 1225 [NASA ADS] [CrossRef] [Google Scholar]
  32. Cortese, L., Boselli, A., Buat, V., et al. 2006, ApJ, 637, 242 [NASA ADS] [CrossRef] [Google Scholar]
  33. Cortese, L., Minchin, R. F., Auld, R. R., et al. 2008, MNRAS, 383, 1519 [NASA ADS] [CrossRef] [Google Scholar]
  34. Cortese, L., Davies, J. I., Pohlen, M., et al. 2010, A&A, 518, L49 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  35. Cortese, L., Catinella, B., Boissier, S., Boselli, A., & Heinis, S. 2011, MNRAS, 415, 1797 [NASA ADS] [CrossRef] [Google Scholar]
  36. Cortese, L., Ciesla, L., Boselli, A., et al. 2012, A&A, 540, A52 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  37. Dalcanton, J. J., Spergel, D. N., & Summers, F. J. 1997, ApJ, 482, 659 [NASA ADS] [CrossRef] [Google Scholar]
  38. de Vaucouleurs, G., de Vaucouleurs, A., Corwin, Jr., H. G., et al. 1991, Third Reference Catalogue of Bright Galaxies, ed. E. A. de Vaucouleurs [Google Scholar]
  39. Donas, J., Deharveng, J. M., Laget, M., Milliard, B., & Huguenin, D. 1987, A&A, 180, 12 [NASA ADS] [Google Scholar]
  40. Donas, J., Deharveng, J.-M., Rich, R. M., et al. 2007, ApJS, 173, 597 [NASA ADS] [CrossRef] [Google Scholar]
  41. Dreyer, J. L. E. 1888, MmRAS, 49, 1 [Google Scholar]
  42. Dreyer, J. L. E. 1895, MmRAS, 51, 185 [NASA ADS] [Google Scholar]
  43. Dutton, A. A. 2009, MNRAS, 396, 121 [NASA ADS] [CrossRef] [Google Scholar]
  44. Gallazzi, A., Brinchmann, J., Charlot, S., & White, S. D. M. 2008, MNRAS, 383, 1439 [NASA ADS] [CrossRef] [Google Scholar]
  45. Gavazzi, G., Pierini, D., & Boselli, A. 1996, A&A, 312, 397 [NASA ADS] [Google Scholar]
  46. Gavazzi, G., Boselli, A., Scodeggio, M., Pierini, D., & Belsole, E. 1999, MNRAS, 304, 595 [NASA ADS] [CrossRef] [Google Scholar]
  47. Gavazzi, G., Boselli, A., Donati, A., Franzetti, P., & Scodeggio, M. 2003, A&A, 400, 451 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  48. Gil de Paz, A., Boissier, S., Madore, B. F., et al. 2007, ApJS, 173, 185 [NASA ADS] [CrossRef] [Google Scholar]
  49. Giovanelli, R., Haynes, M. P., Salzer, J. J., et al. 1994, AJ, 107, 2036 [NASA ADS] [CrossRef] [Google Scholar]
  50. Giovanelli, R., Haynes, M. P., Salzer, J. J., et al. 1995, AJ, 110, 1059 [NASA ADS] [CrossRef] [Google Scholar]
  51. Giovanelli, R., Haynes, M. P., Kent, B. R., et al. 2007, AJ, 133, 2569 [Google Scholar]
  52. Han, Z., Podsiadlowski, P., & Lynas-Gray, A. E. 2007, MNRAS, 380, 1098 [NASA ADS] [CrossRef] [Google Scholar]
  53. Haynes, M. P., & Giovanelli, R. 1984, AJ, 89, 758 [NASA ADS] [CrossRef] [Google Scholar]
  54. Haynes, M. P., Giovanelli, R., Salzer, J. J., et al. 1999, AJ, 117, 1668 [NASA ADS] [CrossRef] [Google Scholar]
  55. Hughes, T. M., & Cortese, L. 2009, MNRAS, 396, L41 [NASA ADS] [CrossRef] [Google Scholar]
  56. Hughes, T. M., Cortese, L., Boselli, A., Gavazzi, G., & Davies, J. I. 2012, A&A, in press, DOI: 10.1051/0004-6361/201218822 [Google Scholar]
  57. Iglesias-Páramo, J., Buat, V., Takeuchi, T. T., et al. 2006, ApJS, 164, 38 [NASA ADS] [CrossRef] [Google Scholar]
  58. Isobe, T., Feigelson, E. D., Akritas, M. G., & Babu, G. J. 1990, ApJ, 364, 104 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. Jeong, H., Yi, S. K., Bureau, M., et al. 2009, MNRAS, 398, 2028 [NASA ADS] [CrossRef] [Google Scholar]
  60. Kennicutt, Jr., R. C., Armus, L., Bendo, G., et al. 2003, PASP, 115, 928 [NASA ADS] [CrossRef] [Google Scholar]
  61. Kent, B. R., Giovanelli, R., Haynes, M. P., et al. 2008, AJ, 136, 713 [Google Scholar]
  62. Koopmann, R. A., & Kenney, J. D. P. 2004a, ApJ, 613, 866 [NASA ADS] [CrossRef] [Google Scholar]
  63. Koopmann, R. A., & Kenney, J. D. P. 2004b, ApJ, 613, 851 [NASA ADS] [CrossRef] [Google Scholar]
  64. Koopmann, R. A., Haynes, M. P., & Catinella, B. 2006, AJ, 131, 716 [NASA ADS] [CrossRef] [Google Scholar]
  65. Kroupa, P., Tout, C. A., & Gilmore, G. 1993, MNRAS, 262, 545 [NASA ADS] [CrossRef] [Google Scholar]
  66. Lee, J. C., Gil de Paz, A., Kennicutt, Jr., R. C., et al. 2011, ApJS, 192, 6 [NASA ADS] [CrossRef] [Google Scholar]
  67. Lemonias, J. J., Schiminovich, D., Thilker, D., et al. 2011, ApJ, 733, 74 [NASA ADS] [CrossRef] [Google Scholar]
  68. Martin, D. C., Fanson, J., Schiminovich, D., et al. 2005, ApJ, 619, L1 [Google Scholar]
  69. McDonald, M., Courteau, S., Tully, R. B., & Roediger, J. 2011, MNRAS, 414, 2055 [NASA ADS] [CrossRef] [Google Scholar]
  70. Mo, H. J., Mao, S., & White, S. D. M. 1998, MNRAS, 295, 319 [Google Scholar]
  71. Morrissey, P., Conrow, T., Barlow, T. A., et al. 2007, ApJS, 173, 682 [NASA ADS] [CrossRef] [Google Scholar]
  72. Moss, C., & Whittle, M. 2000, MNRAS, 317, 667 [NASA ADS] [CrossRef] [Google Scholar]
  73. Moss, C., Whittle, M., & Pesce, J. E. 1998, MNRAS, 300, 205 [NASA ADS] [CrossRef] [Google Scholar]
  74. Muñoz-Mateos, J. C., Gil de Paz, A., Boissier, S., et al. 2007, ApJ, 658, 1006 [NASA ADS] [CrossRef] [Google Scholar]
  75. Muñoz-Mateos, J. C., Gil de Paz, A., Zamorano, J., et al. 2009, ApJ, 703, 1569 [NASA ADS] [CrossRef] [Google Scholar]
  76. Muñoz-Mateos, J. C., Boissier, S., Gil de Paz, A., et al. 2011, ApJ, 731, 10 [NASA ADS] [CrossRef] [Google Scholar]
  77. Nilson, P. 1973, Nova Acta Regiae Soc. Sci. Upsaliensis Ser. V, 0 [Google Scholar]
  78. O’Connell, R. W. 1999, ARA&A, 37, 603 [NASA ADS] [CrossRef] [Google Scholar]
  79. Rose, J. A., Robertson, P., Miner, J., & Levy, L. 2010, AJ, 139, 765 [NASA ADS] [CrossRef] [Google Scholar]
  80. Saintonge, A., & Spekkens, K. 2011, ApJ, 726, 77 [NASA ADS] [CrossRef] [Google Scholar]
  81. Salim, S., Rich, R. M., Charlot, S., et al. 2007, ApJS, 173, 267 [NASA ADS] [CrossRef] [Google Scholar]
  82. Schiminovich, D., Wyder, T. K., Martin, D. C., et al. 2007, ApJS, 173, 315 [NASA ADS] [CrossRef] [Google Scholar]
  83. Schlegel, D. J., Finkbeiner, D. P., & Davis, M. 1998, ApJ, 500, 525 [NASA ADS] [CrossRef] [Google Scholar]
  84. Scodeggio, M., Gavazzi, G., Franzetti, P., et al. 2002, A&A, 384, 812 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  85. Shen, S., Mo, H. J., White, S. D. M., et al. 2003, MNRAS, 343, 978 [NASA ADS] [CrossRef] [Google Scholar]
  86. Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163 [NASA ADS] [CrossRef] [Google Scholar]
  87. Solanes, J. M., Giovanelli, R., & Haynes, M. P. 1996, ApJ, 461, 609 [NASA ADS] [CrossRef] [Google Scholar]
  88. Springob, C. M., Haynes, M. P., & Giovanelli, R. 2005, ApJ, 621, 215 [NASA ADS] [CrossRef] [Google Scholar]
  89. Thilker, D. A., Bianchi, L., Meurer, G., et al. 2007, ApJS, 173, 538 [NASA ADS] [CrossRef] [Google Scholar]
  90. Wang, J., Kauffmann, G., Overzier, R., et al. 2011, MNRAS, 412, 1081 [Google Scholar]
  91. West, A. A., Garcia-Appadoo, D. A., Dalcanton, J. J., et al. 2010, AJ, 139, 315 [NASA ADS] [CrossRef] [Google Scholar]
  92. Wyder, T. K., Treyer, M. A., Milliard, B., et al. 2005, ApJ, 619, L15 [Google Scholar]
  93. Wyder, T. K., Martin, D. C., Schiminovich, D., et al. 2007, ApJS, 173, 293 [NASA ADS] [CrossRef] [Google Scholar]
  94. Zhang, H.-X., Hunter, D. A., Elmegreen, B. G., Gao, Y., & Schruba, A. 2012, AJ, 143, 47 [NASA ADS] [CrossRef] [Google Scholar]
  95. Zibetti, S., Charlot, S., & Rix, H. 2009, MNRAS, 400, 1181 [NASA ADS] [CrossRef] [Google Scholar]
  96. Zwicky, F., Herzog, E., & Wild, P. 1961, Catalogue of galaxies and of clusters of galaxies (Pasadena: California Institute of Technology (CIT)) [Google Scholar]

Online material

Table 6

General properties of the HRS sample and of the GALEX data used in the paper.

Table 7

Integrated FUV, NUV, g, r and i magnitudes and stellar masses for the HRS.

Table 8

FUV, NUV, g, r and i effective and isophotal radii and effective surface brigthnesses for the HRS.

Appendix A: Notes on individual objects

  • HRS2. Our photometry in the SDSS bands may be affected by the presence of a bright star  ~1 arcmin south-west from the target.

  • HRS3 & HRS4. Interacting system (Arp 94). Photometry is uncertain due to the overlap between the two objects.

  • HRS20. Interacting system (Arp 270). Photometry is uncertain due to the overlap with the companion galaxy.

  • HRS42. The point spread function (PSF) of the NUV image is significantly asymmetric and elongated towards North-West. However, this does not affect our integrated photometry and should not significantly influence the estimate of the structural parameters.

  • HRS55. The i-band frame could not be used due to the presence of large artifacts created by a nearby foreground bright star.

  • HRS60. The i-band photometry is mildly affected by the presence of a satellite track.

  • HRS68. The effective radius for this object is significantly smaller than the spatial resolution adopted in this analysis (~6 arcsec). Thus, no effective radius and surface brightness are provided.

  • HRS74. The PSF of the NUV image is significantly asymmetric and elongated towards North-East. However, this does not affect our integrated photometry and should not significantly influence the estimate of the structural parameters.

  • HRS81. The GALEX images suggest the presence of very low surface brightness loops/tidal features associated to this object, not clearly visible in SDSS. However, the data currently available are not deep enough to determine if these features are real.

  • HRS105. This galaxy has an extended UV ring, with ellipticity and position angle (0.5, +34 deg) significantly different from the ones determined from the i-band images (see also Cortese & Hughes 2009; Bettoni et al. 2010). However, the integrated magnitudes do not significantly change if these values are used for the profile fitting.

  • HRS177. The GALEX NUV image suggests the presence of a tail of star-forming knots departing from the galaxy towards the north (see also Arrigoni Battaia et al. 2012).

  • HRS202. This galaxy has a typical FUV surface brightness fainter than 28 mag arcsec-2, making impossible to estimate its isophotal radius.

  • HRS211. The galaxy center is saturated in the i-band, affecting our photometry.

  • HRS213. This edge-on galaxy has a significant bulge component in the optical, which is not visible in the GALEX images. Thus the ellipticity adopted here is not a fair representation of the UV light distribution and could affect our estimate of the UV structural parameters.

  • HRS215 & HRS216. Interacting system. Photometry is uncertain due to the overlap between the two objects.

  • HRS244 & HRS245. Interacting system. Photometry is uncertain due to the overlap between the two objects.

  • HRS265. The PSF of the NUV image is significantly asymmetric and elongated towards North-West. However, this does not affect our integrated photometry and should not significantly influence the estimate of the structural parameters.

All Tables

Table 4

Best bysector linear fitting, Pearson correlation coefficients and dispersion perpendicolar to the best-fit for the structural scaling relations presented in Sect. 5.

In the text
Table 5

Best bysector linear fitting, Pearson correlation coefficients and dispersion perpendicolar to the best-fit for the radius vs. stellar mass and radius vs. Hi mass presented in Fig. 5.

In the text
Table 6

General properties of the HRS sample and of the GALEX data used in the paper.

In the text
Table 7

Integrated FUV, NUV, g, r and i magnitudes and stellar masses for the HRS.

In the text
Table 8

FUV, NUV, g, r and i effective and isophotal radii and effective surface brigthnesses for the HRS.

In the text

All Figures

thumbnail Fig. 1

Comparison of the FUV (left) and NUV (center) magnitudes and effective radii (right) presented here with the ones obtained by Gil de Paz et al. (2007, G07). The average differences (i.e., this work – G07) and standard deviations are shown in each panel. The dotted lines indicate the 1-to-1 relation.
In the text
thumbnail Fig. 2

The basic UV and optical properties of the HRS. Top row: the NUV (left) and FUV (right) magnitude distribution. The solid line shows the local GALEX UV luminosity function presented by Wyder et al. (2005). Middle row: g − i (left) and NUV − i (right) colour vs. stellar mass relations. Bottom row: FUV − i (left) and FUV − NUV (right) colour vs. stellar mass relations. Colours are corrected for Galactic extinction only. Galaxies are colour-coded accordingly to their morphological type: red triangles are E-dE, purple squares S0-S0a, green pentagons Sa-Sab and black circles Sb and later types.
In the text
thumbnail Fig. 3

The effective surface brightness vs. effective radius (top row), effective surface brightness vs. stellar mass (middle row) and stellar mass vs. effective radius (bottom row) relations in i (left), NUV (center) and FUV (right), respectively. Symbols are as in Fig. 2.
In the text
thumbnail Fig. 4

Same as Fig. 3, but for Hi-normal (filled circles) and Hi-deficient (empty circles) spirals (Sa and later types). The predictions of the dust-free models by Boissier & Prantzos (2000) for spin parameter 0.02 (dotted), 0.04 (solid) and 0.06 (dashed) are superposed in green. The red lines indicate the same predictions once the effect of dust attenuation is included.

In the text
thumbnail Fig. 5

The radius vs. stellar mass and radius vs. Hi mass relations in FUV (top row), NUV (middle row) and i-band (bottom row). The relations obtained for effective and isophotal radii are shown in the left and right panels, respectively. Hi-normal and Hi-deficient spirals are indicated by filled and empty circles, respectively. Purple squares are S0 and S0a and red triangles E and dE.
In the text
thumbnail Fig. 6

The Hi gas fraction vs. the NUV-to-i (left) and FUV-to-i (right) isophotal radii ratios. Symbols are as in Fig. 5. Arrows indicates Hi non detections.
In the text
thumbnail Fig. 7

Top row: the g- (left), NUV- (middle), FUV-to-i (right) effective radius ratio vs. Hi deficiency. Bottom row: Same for the isophotal radius ratio. Symbols are as in Fig. 3.
In the text
thumbnail Fig. 8

The inner (left) and outer (right) NUV-i (top) and FUV-i (bottom) colour vs. stellar mass. Symbols are as in Fig. 4. The right-most panel shows the inner-outer NUV-i (top) and FUV-i (bottom) colour difference as a function of stellar mass. The large circles show the averages for each sub-sample in different bins of stellar mass.
In the text

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.