GC systems of the Leo II group

The SLUGGS survey00footnotemark: 0thanks: http://sluggs.swin.edu.au/: Exploring the globular cluster systems of the Leo II group and their global relationships

Sreeja S. Kartha , Duncan A. Forbes , Adebusola B. Alabi, Jean P. Brodie, Aaron J. Romanowsky, Jay Strader, Lee R. Spitler, Zachary G. Jennings, Joel C. Roediger
Centre for Astrophysics & Supercomputing, Swinburne University, Hawthorn VIC 3122, Australia
University of California Observatories, 1156 High St., Santa Cruz, CA 95064, USA
Department of Physics and Astronomy, San José State University, One Washington Square, San Jose, CA 95192, USA
Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824, USA
Macquarie University, Macquarie Park, Sydney, NSW 2113, Australia
Australian Astronomical Observatory, PO Box 915, North Ryde, NSW 1670, Australia
NRC Herzberg Astronomy & Astrophysics, Victoria, BC V9E 2E7, Canada
E-mail: skartha@astro.swin.edu.audforbes@astro.swin.edu.au
Released 2016 February 04
Abstract

We present an investigation of the globular cluster (GC) systems of NGC 3607 and NGC 3608 as part of the ongoing SLUGGS survey. We use wide-field imaging data from the Subaru telescope in the g, r, and i filters to analyse the radial density, colour and azimuthal distributions of both GC systems. With the complementary kinematic data obtained from the Keck II telescope, we measure the radial velocities of a total of 81 GCs.

Our results show that the GC systems of NGC 3607 and NGC 3608 have a detectable spatial extent of 15, and 13 galaxy effective radii, respectively. Both GC systems show a clear bimodal colour distribution. We detect a significant radial colour gradient for the GC subpopulations in both galaxies. NGC 3607 exhibits an overabundance of red GCs on the galaxy minor axis and NGC 3608 shows a misalignment in the GC subpopulation position angles with respect to the galaxy stellar component.

With the aid of literature data, we discuss several relationships between the properties of GC systems and their host galaxies. A one-to-one relation between the ellipticities of red GCs and the galaxy stellar light emphasises the evolutionary similarities between them. In our sample of four slowly rotating galaxies with kinematically decoupled cores, we observe a higher ellipticity for the blue GC subpopulation than their red counterparts. Also, we notice the flattening of negative colour gradients for the blue GC subpopulations with increasing galaxy stellar mass. Finally, we discuss the formation scenarios associated with the blue GC subpopulation.

keywords:
galaxies: elliptical and lenticular, cD - galaxies: star clusters: individual - galaxies: individual: NGC 3605, NGC 3607, NGC 3608
pagerange: The SLUGGS survey00footnotemark: 0thanks: http://sluggs.swin.edu.au/: Exploring the globular cluster systems of the Leo II group and their global relationshipsLABEL:lastpagepubyear: 2016

1 Introduction

To better understand the formation of early-type galaxies (ETGs), it is useful to study their oldest stellar components, such as globular clusters (GCs). GCs are mostly old (10 Gyr, Strader et al. 2005), luminous ( 10 L, Brodie et al. 2011) and compact (R 3pc) star clusters. Establishing connections between the properties of GC systems and their host galaxies can help in understanding the formation of GC systems and hence, their parent galaxies.

In almost all massive galaxies, GC systems are found to be bimodal in colour (Larsen et al., 2001; Peng et al., 2006; Kim et al., 2013; Hargis & Rhode, 2014). Transforming colours to metallicity connects this bimodality with two stages of GC formation. The colour/metallicity distribution peaks are represented by blue/metal-poor and red/metal-rich GC subpopulations (Brodie et al., 2012). In a recent work, Forbes et al. (2015) determined the mean ages of blue and red GC subpopulations as 12.2 12.8 and 11.5 Gyr respectively, suggesting that both subpopulations are very old. However, the two peaks differ in colour (e.g. 0.8 and 1.1 in (gi)) and metallicity (i.e. ([Z/H] peaks at 1.5 and 0.4 dex) values. Other properties of the two subpopulations differ such as azimuthal distribution, spatial distribution, radial colour distribution (Strader et al., 2011; Park & Lee, 2013) and also kinematics (Pota et al., 2013). Three scenarios have been suggested to explain the formation of these two distinct GC subpopulations: major-merger (Ashman & Zepf, 1992), multi-phase collapse (Forbes et al., 1997) and accretion (Côté et al., 1998, 2000). See Brodie & Strader (2006) and Harris (2010) for reviews. Many cosmological simulations of hierarchical galaxy formation have been used to investigate the characteristics of GC systems (physical, dynamical, chemical etc.) in ETGs (Beasley et al., 2002; Bekki et al., 2005, 2008; Muratov & Gnedin, 2010; Griffen et al., 2010; Tonini, 2013; Katz & Ricotti, 2013; Gnedin et al., 2014; Trenti et al., 2015).

In order to associate the formation of different GC subpopulations with galaxy formation events, we explore the radial and azimuthal distributions of both subpopulations and compare with galaxy stellar light. Generally, the radial distribution of blue GCs is found to be more extended than that of both the red GC subpopulation and the galaxy stellar light. The red GC subpopulation is similar in radial distribution to the galaxy light (Bassino et al., 2006b; Strader et al., 2011), whereas the profiles of the blue GC subpopulation show similarities with the X-ray surface brightness profiles (Forbes et al., 2012). The radially extended blue GC subpopulation residing in galaxy haloes suggests that they are very old stellar components formed early in an in-situ dissipative collapse galaxy formation scenario, or accreted later, into the galaxy outskirts, in galaxy build up by minor merger formation scenario. The similarity of red GCs with the galaxy stellar light supports coeval formation, but their origin (from the enriched gas of a parent galaxy or from accreted gas) is not clear.

Park & Lee (2013) studied the azimuthal distribution of GC systems in 23 ETGs using the data from the Advanced Camera for Surveys (ACS) mounted on the Hubble Space Telescope (HST). They found that the ellipticities of the red GC subpopulation match the galaxy stellar light ellipticities with a one-to-one correspondence. They also found that blue GC subpopulations show a similar but less tight relation. Wang et al. (2013) using the ETGs, from the ACS Virgo Cluster Survey (VCS), concluded that both red and blue GC subpopulations align in position angle with the galaxy stellar light, although in a weaker way for blue GC subpopulations. Several single galaxy studies concluded that the galaxy stellar light is mimicked by red GC subpopulation in position angle and ellipticity, but the blue GC subpopulation is differently distributed (e.g. NGC 720, NGC 1023: Kartha et al. 2014, NGC 5813: Hargis & Rhode 2014, NGC 4365: Blom et al. 2012).

The decreasing mean colour of GC subpopulations with increasing galactocentric radius is a key diagnostic observation (Bassino et al., 2006a; Harris, 2009; Arnold et al., 2011; Faifer et al., 2011; Forbes et al., 2011; Blom et al., 2012; Usher et al., 2013; Hargis & Rhode, 2014). More specifically, the degree of steepness allows us to distinguish between two different formation processes, dissipation and accretion/merger (Tortora et al., 2010). Recently, Forbes et al. (2011) studied the colour gradient for NGC 1407 GC subpopulations and found that both GC subpopulations have a steep negative gradient within 8.5 effective radii (R) and a constant colour to larger radii. They explained this colour trend as being compatible with two-phase galaxy formation (Oser et al., 2010). This implies that the inner GCs are formed during a dissipative collapse phase, whereas the outer GCs are acquired during late accretion/mergers. Thus, exploring the radial colour distribution can reveal clues about formation events that happened in the host galaxy’s history.

The layout of this paper is as follows. A brief literature review of the target galaxies is presented in Section 1.1. Section 2 describes the observations, data reduction techniques and initial analysis of imaging and spectroscopic data. Section 3 and 4 present the GC selection techniques and methods used to select the GC systems of individual galaxies. A detailed analysis of various GC system distributions (radial density, colour and azimuthal) for the selected GC systems is presented in Section 5. In Section 6, we discuss connections between the characteristics of galaxy stellar light and GC systems followed, in Section 7, by the conclusion.

1.1 NGC 3607 and NGC 3608 in the Leo II group

Here, we focus on the GC systems of the Leo II group. NGC 3607 and NGC 3608 are the brightest ETGs in the Leo II group. NGC 3607 is a near face-on lenticular galaxy while NGC 3608 is an E12 elliptical galaxy. In the same system there is a third galaxy, NGC 3605, which is a low mass galaxy of E45 morphology. Table 1 presents the main characteristics of the three galaxies with NGC 3607 as the central galaxy in the group. NGC 3608 and NGC 3605 are situated at a distance of 6 arcmin north-east and 2 arcmin south-west from NGC 3607. Kundu & Whitmore (2001a, b) investigated the GC systems of 57 ETGs including NGC 3607 and NGC 3608 using HST/Wide-Field Planetary Camera 2 (WFPC2) data in V and I filters. For these galaxies they detected 130 and 370 GCs, respectively, from single pointing imaging. They did not find a sign of a bimodal colour distribution in either galaxy.

Name RA Dec Type D V A M R PA Vel
(h:m:s) (::) (Mpc) (mag) (mag) (mag) (arcsec) () (km/s)
NGC 3605 11:16:46.6 +18:01:02 E45 20.1 12.15 0.07 19.36 17 19 0.40 661
NGC 3607 11:16:54.6 +18:03:06 S0 22.2 9.89 0.07 21.86 39 125 0.13 942
NGC 3608 11:16:58.9 +18:08:55 E12 22.3 10.76 0.07 20.98 30 82 0.20 1226
Table 1: Basic data for the target galaxies: NGC 3605, NGC 3607 and NGC 3608. Right ascension and declination (J2000) are taken from NASA/IPAC Extragalactic Database (NED). The galaxy distance, effective radius, heliocentric velocity, position angle and ellipticity, for NGC 3607 and NGC 3608, are from Brodie et al. (2014). For NGC 3605, the galaxy distance, effective radius and heliocentric velocity are taken from Cappellari et al. (2011) whereas the position angle and ellipticity are obtained from HyperLeda (Paturel et al., 2003). Total V-band magnitudes are obtained from de Vaucouleurs et al. (1991). The extinction correction for the V-band is calculated from Schlegel et al. (1998). The absolute magnitude is derived from the V-band magnitude, distance and the extinction correction.

With the same HST/WFPC2 data, Lauer et al. (2005) investigated the surface brightness profiles of NGC 3607 and NGC 3608. They mentioned that NGC 3607 contains a symmetric, old and tightly wrapped outer dusty disk to which a second disk is settling in a perpendicular direction. They explained this observation as an infall of gas directly to the centre of galaxy with no interaction with the outer disk. They also detected the remnants of a pre-existing dusty disk in NGC 3608. Terlevich & Forbes (2002) derived the ages of 150 galaxies using the spectral line indices and found 5.8, 3.6 and 10 Gyr ages for NGC 3605, NGC 3607 and NGC 3608 respectively. Later, Rickes et al. (2009) investigated the metallicity distribution, stellar population and ionised gas in NGC 3607 using long-slit spectroscopy. They found stellar components ranging in age from 1 to 13 Gyr between the centre and a 30 arcsec radius of the galaxy’s centre. As part of the ATLAS survey, McDermid et al. (2015) determined the mass-weighted ages for NGC 3605, NGC 3607 and NGC 3608 as 8.1 0.8, 13.5 0.7 and 13.0 0.7 Gyr respectively. Also, from the ATLAS survey Duc et al. (2015) studied these galaxies using the deep multi-band images from the Canada France Hawaii Telescope. They mentioned that NGC 3607 and NGC 3608 are interacting galaxies with the presence of weak dust lanes and ripples.

Based on the ROSAT data, two peaks were detected in diffuse hot X-rays on NGC 3607 and NGC 3608 (Mulchaey et al., 2003). They proposed that the two galaxies are undergoing a merger. Later, Forbes et al. (2006a) detected an extended diffuse X-ray emission around the Leo II group.

The GC systems of the ETGs at the centre of the Leo II group have not yet been studied using wide-field imaging data. As part of the SLUGGS (SAGES Legacy Unifying Globulars and GalaxieS) survey (Brodie et al., 2014), we obtained wide-field data in three optical filters covering the central region of the Leo II group using the Suprime-Cam instrument on the Subaru telescope. With the aid of the imaging and spectroscopic data, we aim to understand the properties of the GC systems associated with each galaxy.

Figure 1: A mosaic image showing the central 28 x 23 square arcmin area of the Leo II galaxy group. This Subaru/Suprime-Cam image is a combination of g, r and i filters. The target galaxies are labelled. The central galaxy, NGC 3607, is at a distance of 22.2 Mpc implying 1 arcsec = 0.107 kpc.

2 Data

2.1 Observations and reduction techniques

Photometric data for the Leo II group was obtained using the Subaru Prime Focus Camera (Suprime-Cam; Miyazaki et al. 2002) mounted on the 8-meter Subaru telescope. The Suprime-Cam imager consists of ten CCDs with individual sizes of 2048 x 4096 pixels and a pixel scale of 0.202 arcsec, covering a maximum sky area of 34 x 27 square arcmin. Wide-field images were obtained during 2011 January 3 and 4. The sky conditions were good with an average seeing of 0.81, 0.80 and 0.80 for the g, r, i filters, respectively. Multiple exposures in a dithered pattern were taken to fill the gaps between individual CCDs. In g, r and i filters the total exposure times were 3743, 1560 and 1200 sec, respectively.

The individual exposures were reduced and combined using the Suprime-Cam Deep Field Reduction package 2 (SDFRED2; Ouchi et al. 2004) for each of the three filters. The pre-processing of images included flat fielding, distortion corrections and atmospheric dispersion corrections. The pipeline also features custom-made modifications to improve the sky subtraction and alignment between multiple exposures. We employed SExtractor (Bertin & Arnouts, 1996) 111http://www.astromatic.net/software/ and Montage 222http://montage.ipac.caltech.edu/index.html for the alignment process. All point sources three sigma above the background level are identified on each CCD image using SExtractor. The positions of these point sources are matched with a reference catalogue (here we use the Sloan Digital Sky Survey) to create an astrometric solution. The astrometric solution is used by the Montage program to align and combine the individual images, generating mosaic images in the three filters. A combination of g, r and i filter mosaic images is shown in Figure 1.

We also obtained a single pointing covering the central region of NGC 3607 from the Hubble Legacy Archive (HLA). This was taken in the F814W (I) filter using the ACS instrument. The Wide-Field Channel on the ACS consists of two 2048 x 4096 CCDs with a 0.049 arcsec pixel scale, and 3.37 x 3.37 square arcmin field of view. A custom-made pipeline (for detailed explanation see Spitler et al. 2006) is employed to reduce the ACS data. The pipeline provides source positions and half light radii for all the detected sources, which are utilised for a preliminary selection of GCs in the Subaru/Suprime-Cam imaging (see Section 3).

Complementary spectroscopic data were obtained using the DEep Imaging Multi-Object Spectrograph (DEIMOS, Faber et al. 2003) on the Keck II telescope. The field of NGC 3607 was targeted on five nights during 2013 January 10 – 12 and 2014 January 26 and 27 as part of the SLUGGS survey. We used five slit-masks for good azimuthal coverage and the seeing per night was 0.87 FWHM 1.15 arcsec with a total exposure time of 10 hours. DEIMOS was used with 1200 l/mm grating centered on 7800 Å, with slit widths of 1 arcsec. In this way, we have a wavelength coverage from 6500 – 8700 Å  and spectral resolution of Å. We reduced the raw spectra using the IDL SPEC2D reduction pipeline together with dome flats and arc lamp spectra. The pipeline produces sky-subtracted GC spectra that covers the CaT absorption lines in the near-infrared (8498, 8542, 8662 Å) and H line at 6563 Å(where possible).

We obtain the radial velocities from our science spectra using the FXCOR task in IRAF by measuring the doppler shift of the CaT lines, cross-correlating each Fourier transformed science spectrum with the Fourier transformed spectra of 13 Galactic template stars. In practice, we require that the strongest CaT lines (8542, 8662 Å) be present and where possible the H line as well. Where the lines are not properly defined, but the velocity is consistent with either galaxy, the GC is classified as marginal. Objects with velocities less than 350 km/s are classified as Galactic stars and those with velocities greater than 1800 km/s as background galaxies. Our final catalogue has 75 GCs and 7 ambiguous objects (see Table 8 in Appendix A). Here, ’ambiguous’ denotes that either the velocity or position has a mismatch with the target galaxies, but it has confirmed characteristics of a GC.

2.2 Photometry

Before carrying out any photometric analysis, the galaxy light was subtracted in each of the three mosaic images. The two large galaxies are individually modelled using IRAF task ELLIPSE with the center of the galaxy, the major axis position angle (PA) and the ellipticity () as free fitting parameters. During the fitting process the bright stars were masked before modelling the galaxy light. The best fit galaxy model produces radial profiles of surface brightness, position angle and ellipticity measurements for both the galaxies. We made use of galaxy light subtracted images to improve the source detection in the central regions of target galaxies.

We utilised SExtractor for source identification and photometry. We instructed SExtractor to identify a probable source only if it has a minimum of 5 adjacent pixels with a flux higher than three sigma above the local background. SExtractor estimates the total instrumental magnitudes for the detected sources using Kron radii (Kron, 1980) in the automatic aperture magnitude mode. It provides an output list of point sources with position and magnitude. As standard stars were not observed for zeropoint calibration, we exploited the bright stars (i 22) present in the galaxy field. A match between these bright stars and the Sloan Digital Sky Survey catalogue (data release 7 version) was used for the flux calibration in all three mosaic images. Photometric zeropoint magnitudes in three filters are derived from the best-fit linear relationship between the instrumental magnitudes of bright stars and calibrated magnitudes from the SDSS catalogue. Estimated zeropoints in g, r, i bands are 28.68 0.08, 28.92 0.12, 28.78 0.15 magnitudes, respectively. All magnitudes have had the zeropoint correction applied. The galaxy photometry is corrected for Galactic extinction using the dust extinction maps from Schlegel et al. (1998). Hereafter, all the magnitudes and colours cited are extinction corrected.

3 Globular cluster selection

The large galaxies, NGC 3607 and NGC 3608, are at an assumed distance of 22.2 0.1 Mpc (Brodie et al., 2014) and NGC 3605 taken to be 20.1 Mpc (Cappellari et al., 2011). For GC identification, a match of object positions between the three bands is carried out at first, in order to eliminate the false detections. Afterwards a separation between extended objects (galaxies) and point source objects (both GCs and stars) is incorporated. This separation is based on the surplus light detected beyond the extraction aperture. Objects showing large difference between the extraction aperture and an outer aperture are considered as extended sources and are removed (see Kartha et al. 2014 for details).

We employ a colour-colour selection as the next step to identify the GC candidates. To aid this selection, we used the position and half light radius of the sources from the HST/ACS data. An upper limit of 9 pc at the distance of NGC 3607, for GC candidature is applied, and the selected objects are visually verified. A positional match between the Subaru objects and the GCs selected on the HST/ACS image is carried out and then the half light radius is attached to the Subaru list for the common objects. Hence we create a list of probable GCs with their positions, three magnitudes from the Subaru/Suprime-Cam data, and half light radii from the HST/ACS data. From earlier studies, e.g., figure 6 in Blom et al. (2012) and figure 3 in Pota et al. (2013), it is evident that the GCs populate a particular region in the colour-colour diagram. With the above list we identify the locus of GCs in (ri) verses (gi) colour space, implementing similar procedures as explained in Spitler et al. (2008) and Blom et al. (2012). The GC candidates, along with neighbouring objects showing a 2 deviation from the selected region, are chosen as final GC candidates. The selected GCs range over 0.6 (gi) 1.4, which corresponds to a metallicity range of 1.94 [Z/H] 0.86 using the empirical relation given in Usher et al. (2012). The upper and lower cut off in i band magnitude are 20.4 and 24.4 magnitudes, respectively. At the distance of NGC 3607 objects brighter than 20.4 magnitude include ultra compact dwarfs (Brodie et al., 2011) while the lower limit is one magnitude fainter than the turnover magnitude for the GC system. This final list of GC candidates include 1000 objects from NGC 3605, NGC 3607 and NGC 3608.

4 Defining the GC systems of each galaxy

We derive the stellar mass of NGC 3605 as log(M) = 10.76 M from the galaxy V-band magnitude (see Table 1) and the mass to light ratio from Zepf & Ashman (1993). The extent of the GC system of NGC 3605 is calculated from the stellar mass in the empirical relation between GC system extent and the galaxy stellar mass (equation 7 in Kartha et al. 2014). A GC system extent of 40 arcsec is derived from the calculation and we assume a maximum of 1 arcmin extent for NGC 3605. We detect 10 objects in the 1 arcmin region around NGC 3605 and eliminate them from the following calculations. The surface density distribution of GCs around NGC 3605 has been investigated and we find a constant GC density, implying no contamination from NGC 3605 to the NGC 3607 or NGC 3608 GC systems.

The remaining GC candidates are a combination of objects from NGC 3607 and NGC 3608. In order to classify their individual GC systems, we invoke two methods, based on surface brightness and position angle of the host galaxies.

4.1 Surface brightness method

Figure 2: Surface brightness profiles for individual galaxies. The i- band profiles have been extracted using IRAF ELLIPSE task and extrapolated towards larger radii from the centres of NGC 3607 and NGC 3608. The negative to positive radius represents the declination axis centered on NGC 3608.

The galaxy light for both galaxies is modelled and extracted using the IRAF task ELLIPSE. The individual surface brightness profiles are fit with Sérsic profiles (Graham & Driver, 2005). We extrapolate these profiles to larger galactocentric radius ( 15 arcmin) and use these extrapolated profiles to represent the stellar light profiles of individual galaxies to large radius. Figure 2 shows the surface brightness profiles of NGC 3607 and NGC 3608. Based on the position of each GC, its membership probability is computed from the ratio of surface brightness of NGC 3607 to NGC 3608. Hereafter we refer to this as the surface brightness (SB) method. GCs with a probability greater than 55 percent are counted as members of NGC 3607, while less than 50 percent are classified as members of NGC 3608. The 6 R ellipses overlap around 55 percent SB probability (see Figure 3). We classify the GCs with probability between 55 and 50 percent as ambiguous objects.

Figure 3: Spectroscopically confirmed GCs of NGC 3607 and NGC 3608. The galaxy centres for NGC 3608 and NGC 3607 are, respectively, at co-ordinates (0,0) and (1.1,5.8). The magenta circles and green diamonds represent the GC members of NGC 3607 and NGC 3608, while open triangles and black square represent ambiguous GCs (with IDs denoted) and one extreme object (ID: S41879). The colour map in the background represents the membership probability from the surface brightness and the colour coding is shown to the right. The black ellipses represent six effective radii for the two galaxies with their respective ellipticity and position angle of the galaxy stellar light (refer to Table 1).

4.2 Major axis method

We employed a second method called the major axis (hereafter MA) method, to separate the GC systems of the two galaxies. In this method, we divided the GCs along the photometric major axis (125 and 82 degrees for NGC 3607 and NGC 3608, respectively) and selected the hemisphere pointing away from the other galaxy. Thus, the selection of GCs for NGC 3607 includes GCs in the position angles 125 to 305 degrees and for NGC 3608 GCs from 0 to 82 and 262 to 360 degrees. This method excludes the region of maximum tidal interaction between the two galaxies. Coccato et al. (2009) adopted a similar method for disentangling the planetary nebulae (PNe) of NGC 3608. To eliminate the contaminants from NGC 3607, they excluded the PNe on the southern side of NGC 3608, which is equivalent to the major axis method used here.

Figure 4: Velocity distribution of spectroscopically confirmed GCs as a function of radius with respect to NGC 3608. The NGC 3607 and NGC 3608 members are represented with magenta circles and green diamonds, while marginal GCs and one extreme object (ID: S41879) with open triangles and a filled square. The position of NGC 3605 is represented with a black star. The dot-dashed and the dashed horizontal lines represent the galaxy systemic velocities for NGC 3607 and NGC 3608, respectively. An average error of 14 km/s is shown at the lower left.

4.3 Analysis of kinematic data

We obtained the radial velocity measurements for 82 (confirmed plus marginal) GCs in the field of the Leo II group. The galaxy systemic velocities for NGC 3607 and NGC 3608 are 942 and 1226 km/s (Brodie et al., 2014), respectively. To assign the membership of GCs to individual galaxies, we performed a biweight estimator distribution (following Walker et al. 2006) based on the right ascension, declination and line of sight velocity of each GC. The GCs within 2 ( is the standard deviation calculated from the velocity distribution) from the central galaxy velocity are assigned membership to the corresponding galaxy, while keeping marginal members as velocities between 2 to 3. Figure 3 displays positions of spectroscopically confirmed GCs on a SB probability map. The background map shows the SB probability used in the separation of GCs (see Section 2). The positions of individual galaxy GCs (as determined using velocities) fall on the same region derived from the SB method, confirming the robustness of the SB probability method for classifying the GCs. The distribution gives 43 and 32 GCs, respectively, as NGC 3607 and NGC 3608 members.

In addition, we classified the 7 ambiguous objects as 6 GCs and one extreme member. The extreme member S41879 has a velocity of 1822 22 km/s, but positionally it is projected near the centre of NGC 3607 (see Figure 3) in the 2D map. Assuming it lies at the distance of NGC 3607 (D = 22.2 Mpc), then it has M = 9.97 mag. From the line of sight velocity and H = 70 (km/s)/Mpc, we calculate the distance as 26 Mpc and hence the magnitude M = 10.31 mag. This suggests that it is a possible UCD (see Brodie et al. 2011). To confirm this, we checked the HST image for an estimation of its size. Unfortunately, this object is placed in the central gap region of the HST pointing. We examined the Subaru image and found that the object is very circular in shape. Another possibility is an intra-group GC, as it is blue (gi) = 0.623, circular in shape and lies in the projected region between NGC 3607 and NGC 3605. With the above information, we suggest that this extreme object might be a background UCD or an intra-group GC. Eliminating this extreme object, we have 81 spectroscopically confirmed GCs for NGC 3607 and NGC 3608.

Figure 5: GC subpopulations for the spectroscopically confirmed GC systems of NGC 3607 and NGC 3608. The centre of NGC 3605, NGC 3607 and NGC 3608 are denoted with star (3.1, 7.9), cross (1.1,5.8) and plus (0,0) symbols. The diamonds and circles (both open and filled) represent, respectively, the GCs of NGC 3608 and NGC 3607. The red and blue colours represent the blue and the red subpopulations for both galaxies.

Figure 4 shows the velocity distribution of GCs with galactocentric radius measured from the centre of NGC 3608. The six marginal GCs are labelled in Figures 3 and 4. Based on both these figures, we assign a membership for the marginal GCs. Note here that this manual membership assignment is unimportant for any broad conclusions of this study. S51178 is positionally close towards NGC 3607 with velocity > 1300 km/s. But according to the SB probability, this GC has > 80 percent probability to be associated with NGC 3607. Hence, considering these facts we assign it to NGC 3607 as GC44 (name given in Table 8). Based on the SB probability and velocity measurement, S53407 is assigned to NGC 3607 (GC45). The position of S64467 is close to NGC 3608 with 50 percent probability, but having a velocity of 807 km/s supports a membership with NGC 3607 (GC46). S60023 has a 70 percent probability with NGC 3608 and with a velocity of 1160 km/s. Hence, S60023 is a probable member of NGC 3608 (GC33). S55434 (GC34) and S57144 (GC35) are GCs with velocities 1281 and 1229 km/s, respectively. Both fall on the probability region of 60 percent for NGC 3607. However, a membership to NGC 3608 is allocated for these GCs based on the positional closeness and velocities. Hence, S60023, S55434, S57144 are NGC 3608 members and S51178, S64467, S53407 are NGC 3607 members. Finally, NGC 3608 and NGC 3607 have 35 and 46 spectroscopically confirmed GCs, respectively.

The mean velocities estimated from the GC systems of NGC 3607 and NGC 3608 are 963 and 1220 km/s, respectively, in good agreement with galaxy central velocities. Estimates of the GC system velocity dispersions for NGC 3607 and NGC 3608 are 167 and 147 km/s, respectively. Cappellari et al. (2013) found central velocity dispersions of 206.5 10 and 169.0 9 km/s from the galaxy stars, respectively, for NGC 3607 and NGC 3608.

4.3.1 GC subpopulations

Currently, we have 46 and 35 spectroscopically confirmed GCs, respectively, for NGC 3607 and NGC 3608. We have classified the GCs into blue and red subpopulations based on a constant colour division with galactocentric radius due to small number statistics. The GMM algorithm (explained in Section 5.1.2) gives a (gi) dividing colour of 0.87 for NGC 3607 and 0.93 mag for NGC 3608 (from photometric measurements). We used these colours to separate the blue and the red subpopulations of the two galaxies as shown in Figure 5. From the photometric analysis of the GC subpopulations, we obtained 62 and 38 percent blue and red subpopulations (see Section 5.2.2), respectively.

5 Analysis of photometric data

Below we describe the radial density, colour and azimuthal distributions of the NGC 3607 and NCG 3608 GC systems. Note here that the GC systems are selected from the colour-colour space discussed in Section 3.

Figure 6: Surface density distribution for the GC system of NGC 3607. The GCs are selected via the SB and the MA methods shown by filled and open circles. The solid and the dotted lines represent the Sérsic fits for the GCs selected from each method. The GC system reaches the background around a galactocentric radius of 9.5 0.6 arcmin, in agreement with the expected value using the galaxy stellar mass in the relation of Kartha et al. (2014).

5.1 GC system of NGC 3607

5.1.1 Radial density distribution

To derive the radial distribution of the GC system, we define radial bins up to a galactocentric radius of 16.9 arcmin. Then the effective area coverage is obtained for each radial annulus. The area is corrected for the presence of saturated stars and for any regions outside the detection area. The GC number in each annulus is then divided by the effective spatial area to determine the spatial density in that particular annulus. The errors are calculated using Poisson statistics.

We obtained the GC system surface density using two methods. In the SB method (refer Section 2), a correction is applied for the missing area due to NGC 3608 and NGC 3605. In the MA method (refer Section 4.2), the number density is doubled in each radial bin. The radial density distribution is fitted with a combination of Sérsic profile plus a background parameter to estimate the effective radius and the background value for the GC system. The fitted surface density profile is:

(1)

where N is the surface density of the GCs at the effective radius R, n is Sérsic index or the shape parameter for the profile, b is given by the term 1.9992n 0.3271 and bg represents the background parameter. Note that the radius R is the centre of each radial bin.

Figure 6 shows the density profile of the GC system for NGC 3607 only. The GCs brighter than the turnover magnitude, i = 23.5, only are considered. The plot displays the density values derived from the two different methods, i.e. SB and MA methods. Both are fitted with the profile given in Equation 1. In the density distribution plot for NGC 3607, the SB and MA methods used 1170 and 907 objects, respectively. It is evident from the figure that both methods yield consistent results and the profile reaches the background at a galactocentric radius of 9.5 0.6 arcmin (61 3 kpc).

Kartha et al. (2014) found an empirical relation between the galaxy stellar mass and the extent of its GC system. The relation is as follows:

(2)
Method R n bg GCS ext.
(arcmin) (arcmin) (arcmin)
SB 2.45 0.54 2.74 1.76 1.70 0.15 9.4 0.6
MA 1.99 0.29 1.97 1.19 1.68 0.08 9.6 0.5
Table 2: Fitted parameters for the surface density profile of the NGC 3607 GC system. The first column represents the method used for deriving the surface density profile. The effective radius, the Sérsic index and the background estimation are given in the following three columns. The last column presents the extent of the GC system as measured. The error values given are 1-sigma uncertainties.

NGC 3607, an S0 galaxy, with absolute V-band magnitude M = 21.87 and assumed mass to light ratio of 7.6 (given in Zepf & Ashman 1993) has a host galaxy mass, log(M/M) = 11.56. The GC system extent for NGC 3607 determined using the above equation is 57 3 kpc, in good agreement with the direct estimation using the wide-field Subaru/Suprime-Cam image (61 3 kpc).

Figure 7: Colour magnitude diagram for NGC 3607. The top panel represents the GCs brighter than M = 7.75 mag (0.5 fainter than the turnover magnitude) within the extent of NGC 3607 GC system. The dashed line represents the turnover magnitude in i filter, M = 8.23 mag. The bottom panel represents the colour histogram of NGC 3607 GC system. The open, shaded and dashed histograms represent the GCs which are brighter than the turnover magnitude, the estimated background contamination and the background corrected colour histograms.

5.1.2 GC bimodality

Figure 7 shows the colour magnitude diagram of NGC 3607 GCs. The GCs, brighter than M = 7.75 mag, within the GC system extent of NGC 3607 are shown in the diagram. The bottom panel contains the histogram of GCs which are brighter than the turnover magnitude (M = 8.23 mag) along with the background contamination. To estimate the background contamination, we made use of the detected objects beyond the GC system extent of the galaxy. For NGC 3607, the objects beyond 11 arcmin (as GC system extent is 9.5 0.6 arcmin) are considered as background contamination. We applied an areal correction, if needed. The background corrected colour histogram is also shown in Figure 7 and it includes 611 GCs.

To quantify the colour distribution of the GC system, we used the gaussian mixture modeling (GMM, Li & Gnedin 2014; Muratov & Gnedin 2010) algorithm on the GC system (gi) colour, after background correction. The algorithm tests for a multimodal colour distribution over unimodal. To be a significant multimodal GC system distribution, the following three statistics should be, 1. low values for the confidence level from the parametric bootstrap method, 2. the separation (D) between the means and the respective widths greater than 2, and 3. negative kurtosis for the input distribution.

Figure 8: Radial density distributions of GC subpopulations for NGC 3607. The density distributions for the blue and the red subpopulations (from the MA method) are represented with blue diamonds and red triangles, respectively. The best fit Sérsic profiles to the density distributions are shown as solid lines. The black solid line represents the best fit Sérsic profile for the total GC system. The dashed line represents the galaxy brightness profile in the i filter. The blue subpopulation is found to be more extended than the red subpopulation. However, the galaxy stellar light profile better matches with the density distribution of the red subpopulation than the blue subpopulation.

For NGC 3607, the GMM algorithm confirmed a bimodal colour distribution from the SB and MA method selected GCs, based on the following statistics : with less than 0.001 percent confidence level, D > 2.6 0.3 and negative kurtosis. The blue and red GC subpopulations peak in (gi) colour at 0.74 0.04 and 1.03 0.03, respectively. The (gi) colour of separation between the blue and the red subpopulations is at 0.87 0.02. The total GC system is classified into 45 9 and 55 8 percent, respectively, blue and red subpopulations.

NGC GC R n bg
(arcmin) (arcmin)
3607 Blue 1.59 0.94 4.14 2.32 0.36 0.12
Red 0.67 0.52 3.38 1.35 0.48 0.05
3608 Blue 1.42 0.31 1.03 0.89 0.50 0.05
Red 0.91 0.72 1.98 0.82 0.35 0.05
Table 3: Fitted parameters for the surface density profile of NGC 3607 and NGC 3608 GC subpopulations. The first and second columns represent the target galaxy and subpopulation category. The derived parameters, effective radius, the Sérsic index and the background estimation, after the Sérsic fit are given in the last three columns.
(a)
(b)
Figure 9: Azimuthal distribution for the GC system of NGC 3607. The black, blue and red histograms represent the azimuthal distribution of the total population and the blue and red subpopulations of GCs. The solid, dashed and dash-dotted lines represent the fitted profiles based on Equation 3. The vertical dashed line shows the position angle of the stellar major axis, 125 degrees. The left panel includes GCs selected on the basis of the SB method, whereas the right panel includes GCs based on the MA method. In both panels, the total GC system and the blue and red subpopulations are aligned in a position angle which is in good agreement with the stellar light. An overabundance of GCs (majority from the red subpopulation) along the minor axis (35 degrees) is seen in both panels.

The radial density distribution for both GC subpopulations (from the MA method) are estimated and plotted in Figure 8. Both subpopulation distributions are fitted with Sérsic profile given in Equation 1. The parameters derived from the Sérsic fit are tabulated in Table 3. The red subpopulation is centrally concentrated while the blue subpopulation is more extended. The red subpopulation appears to have higher number density for most galactocentric radii. The galaxy stellar light profile is in better agreement with the density distribution of red subpopulation than blue subpopulation. Also the effective radius of the galaxy stellar light (39 arcsec) matches more with the red subpopulation (40 29 arcsec) than the blue ones (95 50 arcsec).

5.1.3 Azimuthal distribution

To quantify the azimuthal distribution of GCs, they are initially folded along the North to South direction, then binned in equal angular intervals. The azimuthal distribution, , is then fitted with a profile (McLaughlin et al., 1994) of the form:

(3)

where is the power law index fitted to the surface density of GCs, bg is the background estimated from the Sérsic fits (see Section 5.1.1) and k is the normalization constant. The profile is iterated with the position angle of the GC system (PA) and the ellipticity () as free parameters. For the analysis, only the GCs within the extent of GC system (i.e., 9.5 arcmin) are included. The number of GCs in each angular bin is corrected for the missing area due to NGC 3608 in the SB method, and is doubled in the MA method. Here we used 980 and 564 GCs, respectively, in the SB and MA methods.

Figure (a)a shows the azimuthal distribution of GCs selected based on the SB method. The GCs are aligned to a position angle of 110 7 degrees, which is in reasonable agreement with the stellar light (125 degrees) of the galaxy. The alignment of GC system is more elliptical (0.39 0.08) than the stars (0.13). The GCs also show an enhancement along the minor axis (35 degrees), which is either a genuine feature or possibly a contamination from the GCs of NGC 3608 and NGC 3605 (both positioned around the minor axis of NGC 3607). We already found a constant surface density around NGC 3605 and hence, we assume that NGC 3605 is not contributing to the overabundance.

The only other possible contributor for this minor axis overabundance is NGC 3608, situated in the NE direction. We have eliminated the maximum contamination from NGC 3608 in the MA method, as it counts only the hemisphere away from the other galaxy. Hence, if the enhancement of GCs is not genuine, then we should not observe the same in the MA method. Figure (b)b displays the azimuthal distribution of GCs selected in the MA method, including only the GCs from 125 to 305 degrees counted from North in counter-clockwise direction. It is evident from this plot that the enhancement along the minor axis is a genuine feature, with decreased strength which is consistent within error bars. The position angle of GCs from the MA method also aligns with the galaxy stellar light. Similarly, from the SB method, the GCs are found to be more elongated than the arrangement of stellar light. Table 4 summarises the best fit sinusoidal profile parameters.

Method NGC 3607 NGC 3608
GC PA PA
() ()
SB Total 110  7 0.39 0.09 104 15 0.20 0.09
Blue 112 14 0.37 0.11 106 11 0.31 0.10
Red 108 11 0.47 0.09  97 18 0.14 0.16
MA Total 109  8 0.42 0.07  66  7 0.39 0.10
Blue 108 10 0.45 0.11  67  8 0.45 0.09
Red 109  8 0.48 0.11  64 10 0.44 0.13
Table 4: Position angle and ellipticity for the GC systems of NGC 3607 and NGC 3608. The values are derived by fitting Equation 3 to the azimuthal distribution. The table gives the derived values for the total GC system, the blue and the red subpopulations. For comparison, the position angle and the ellipticity of the galaxy stellar light for NGC 3607 are 125 degrees and 0.13, respectively and for NGC 3608 are 82 degrees and 0.20, respectively.

Figure 9 also shows the azimuthal distribution of blue and red GC subpopulations from the two methods. The subpopulations are separated at a (gi) colour of 0.87, obtained from the GMM algorithm. Both subpopulations have similar position angles for the total GC system and are more elliptical than the galaxy stars.

Summarising, the total GC system and both subpopulations follow the galaxy stellar light in position angle. But the distribution of GCs is not as circular as the galaxy stellar component. The red GC subpopulation shows a more flattened distribution than the blue subpopulation for NGC 3607.

Figure 10: Radial colour distribution for the GC system of NGC 3607. The GCs are selected using the MA method, and are shown as small grey squares. The separation between the two subpopulations is obtained using a moving mean colour, and shown in black open circles. The average colours with errors for the blue and the red GC subpopulations are shown as blue and red filled circles, respectively. The solid lines represent the best fit lines for the blue and the red subpopulations in the central 6.5 arcmin, the projected separation between the two galaxies. For the blue and the red GC subpopulations, significant colour gradients (0.070 0.013 and 0.033 0.015 mag per dex for blue and red GC, respectively) are obtained in the central 6.5 arcmin radius.

5.1.4 Radial colour distribution

Figure 10 shows the radial distribution of GC colours from the centre of NGC 3607. The GCs brighter than the turnover magnitude in the MA method only are included. The GC subpopulations are divided with a moving colour with radius technique. In each radial bin, the average colour for both subpopulations are determined (keeping a constant number of GCs per radial bin). For NGC 3607, we used 350 GCs to plot the colour distribution with 45 GCs in each bin.

As seen from the plot, for the total extent of the GC system, the average colour for the blue subpopulation decreases with radius from the centre, while a flat colour gradient is seen for the red subpopulation. The colour distribution for the blue subpopulation is fitted with a logarithmic relation (following Forbes et al. 2011) as:

(4)

where R is the effective radius for NGC 3607 equal to 39 arcsec (Brodie et al., 2014), a and b are, respectively, intercept and slope of the fit. We obtained a best fit line using the bootstrap technique and derived the parameters for the blue subpopulation as a = 0.82 0.018 and b = 0.036 0.009 mag per dex. Maraston (2005) derived a relation between (gi) and [Z/H] over the metallicity range [Z/H] 0.2, using single stellar population models, of (gi)/[Z/H] = 0.21 0.05 mag per dex. Using this we obtained for the blue subpopulation a metallicity gradient of 0.17 0.04 dex per dex to the total extent of the GC system. But, we did not detect a significant colour gradient for the red subpopulation and the total population in the total extent of GC system (0.01 0.01 and 0.013 0.011 mag per dex for red and total GCs).

We also quantified the colour/metallicity gradient in the central ( 6.5 arcmin) region, only including the common galactocentric radii between the two galaxies. The colour gradient for the blue, red and the total population are 0.070 0.013, 0.033 0.015 and 0.039 0.018 mag per dex. In the inner 6.5 arcmin region, the blue subpopulation has a higher metallicity gradient (0.33 0.06 dex per dex) compared to the red subpopulation (0.16 0.07 dex per dex). Hence, we conclude that a significant colour/metallicity gradient is obtained for the blue and the red subpopulations of NGC 3607.

Figure 11: Surface density distribution for the GC system of NGC 3608. The radial density distribution of GCs based on the SB method and the MA method are represented with filled and open circles, respectively. The SB method detects GCs to a maximum galactocentric radius of 5.5 arcmin. The best fit Sérsic profiles are represented with solid and dotted lines for the two methods. The GC system reaches a background in the MA method at a galactocentric radius of 6.6 0.8 arcmin.

5.2 GC system of NGC 3608

5.2.1 Radial density distribution

Figure 11 displays the radial density of GCs selected with the SB and the MA methods for NGC 3608 fitted with the profile given in Equation 1 (fitted parameters are given in Table 5). In the SB method, the selection of GCs for NGC 3608 gives a maximum galactocentric radius of 5.5 arcmin (as seen from Section 2). But the MA method identifies objects to a distance of 12.8 arcmin from the galaxy centre (thus extends up to the edge of the detection area). In both methods, the GCs with i 23.5 mag (turnover magnitude) are counted for studying this distribution. In the density distribution plot, the SB and MA methods used 304 and 402 objects, respectively. The density distribution of GCs in radial annuli, after applying respective corrections for both methods, are shown in Figure 11. The GC system reaches a background level of 1.65 0.1 GCs per square arcmin to a galactocentric radius of 6.6 0.8 arcmin (43 5 kpc), from the MA method. But the density value for the final data point from the SB method is 1.82 0.36 GCs per square arcmin implying that the distribution has not reached the background level. The elimination of marginal GCs (SB probability between 50 and 55 percent) in the SB method might be the reason for this discrepancy in the extent of GC system. Another point from the figure is that the surface density values estimated from both methods are consistent within error bars, up to 5.5 arcmin.

Method R n bg GCS ext.
(arcmin) (arcmin) (arcmin)
SB 1.29 0.15 0.66 0.36 1.82 0.36 -
MA 1.50 0.15 0.93 0.56 1.65 0.10 6.6 0.8
Table 5: Fitted parameters for the surface density of NGC 3608 GC system. The first column represents the GC selection method. The following three columns give the derived values for the effective radius, the Sérsic index and background using the Sérsic fit. The extent of the GC system is given in the last column, which is not estimated for the SB method.
Figure 12: Colour magnitude diagram for NGC 3608. The top panel represents the GCs brighter than M = 7.75 mag within the extent of GC system. The dashed line represents the turnover magnitude in i filter, M = 8.23 mag. The colour histogram of the GC system of NGC 3608 is shown in the bottom panel, where the open, shaded and dashed histograms represent the GCs which are brighter than the turnover magnitude, the estimated background contamination and the background corrected colour histograms.

NGC 3608 is an E2 galaxy and M = 20.98 mag, assuming a mass to light ratio of 10 (Zepf & Ashman, 1993) has a stellar mass of log (M/M) = 11.32. Using Equation 2, the expected GC system extent is calculated to be 40 2 kpc, consistent with the GC system extent from the observational data (43 5 kpc).

5.2.2 GC bimodality

The colour magnitude diagram for the selected GCs of NGC 3608, within the GC system extent (43 kpc) and brighter than M = 7.75 mag, is shown in Figure 12. The figure displays 250 GCs. The background contamination in the GC system selection is quantified, as explained in Section 5.1.2, and are corrected for this contamination. The bottom right panel displays the colour histograms of GCs which are brighter than the turnover magnitude with and without background correction. The estimated background correction is also illustrated in the same figure.

The GMM algorithm fit to NGC 3608 GCs selected from the MA method gives a bimodal colour distribution with peaks at (gi) = 0.80 0.02 and 1.12 0.04. The total GC system contains 65 6 and 35 6 percent, respectively, blue and red subpopulations. The blue and red subpopulations are divided at (gi) = 0.93.

The radial surface densities (GCs from the MA method) are fitted with Sérsic profiles and are displayed in Figure 13. The parameters estimated from the Sérsic fit are tabulated in Table 3. For NGC 3608, the blue subpopulation shows a higher density than the red subpopulation throughout the extent of the GC system. The red subpopulation is found to be more centrally concentrated, and their density profile is in good agreement with the galaxy stellar light.

Figure 13: Radial density distributions of NGC 3608 GC subpopulations. The blue diamonds and the red triangles represent the surface density distributions of blue and red subpopulations respectively. The blue and the red solid lines demonstrate the best fit Sérsic profiles on the distributions, while the black solid line represents the Sérsic fit for the total GC system. The galaxy brightness profile in the i filter is shown as dashed line, in reasonable agreement with the density distribution of red subpopulation. Also, the red subpopulation is more centrally concentrated than the blue subpopulation for NGC 3608.
(a)
(b)
Figure 14: Azimuthal distribution for the GC system of NGC 3608. The colours and styles of histograms and lines are same as shown in Figure 9. The left panel shows the distribution of GCs selected from the SB method and the right panel shows the distribution from the MA method. The galaxy stellar light is aligned along the major axis (82 degrees), represented by the vertical dashed line. The total GC system and both the subpopulations are arranged along a different position angle ( 100 degrees) than the galaxy stellar light in the SB method. The right panel shows the distribution of GCs selected from the MA method. The total GC system and both the subpopulations are aligned at a position angle, 65 degrees in the MA method, slightly off from the galaxy stellar light. Also in both panels, a scarcity of GCs along the galaxy major axis is visible.

5.2.3 Azimuthal distribution

The range of galactocentric radii for the selected GCs in the SB method is from 0.5 to 5.5 arcmin. The selection of GCs in all position angles is complete up to 2.2 arcmin and hence, an areal correction is applied for the missing area outside that radius. Here we used 378 and 275 GCs, respectively, in the SB and MA methods. Figure (a)a shows the azimuthal density distribution of GCs from the SB method. The histograms are fitted with the sinusoidal profile given in Equation 3. Table 4 gives the position angles and ellipticities obtained from the sinusoidal fit. The galaxy stellar light has a major axis of 82 degrees and ellipticity of 0.20. As seen from Table 4, the total GC system and both subpopulations are arranged along a different position angle of 100 degrees for the SB method. When the distribution is examined over 0 to 360 degrees rather than 0 to 180 degrees (i.e., without folding along the North to South direction), an overabundance is evident in the position angles between 90 and 230 degrees. This is in the direction towards NGC 3607 and also the direction in which the area correction is largest. Hence, this overabundance is either due to contamination from NGC 3607 (or due to overestimation of missing area). Also a scarcity of GCs is observed in both major axis position angles (82 and 262 degrees). The ellipticity for the total GC system is 0.20 0.09, matching with the galaxy stellar light.

Figure (b)b shows the azimuthal density distribution of GCs selected in the MA method, for which GCs in the position angles 80 to 260 degrees are under abundant. The GCs within the extent of GC system (6.6 arcmin) are included in the azimuthal distribution. As seen in Table 4, the best fit sinusoidal profile gives a position angle of 66 7 degrees for the total GC system and an ellipticity of 0.39 0.10. The GCs selected in the MA method includes GCs of NGC 3608 placed at a position angle pointing away from NGC 3607, implying minimum contamination. The arrangement of GCs in the MA method is along the position angle matching the galaxy stars, but the distribution is more elliptical. Since we observed an overabundance in GCs for both galaxies, in the region towards each other, an interaction may be occurring between the two.

Figure 15: Radial colour distribution for the GC system of NGC 3608 (from the MA method). The individual GCs are represented as small grey squares. The average colours with error for the blue and red GC subpopulations are denoted as blue and red filled circles, respectively. The separation colours for the subpopulations in each radial bin is calculated using a moving mean colour method and is denoted with black open circles. The blue subpopulation shows a colour gradient of 0.052 0.011 mag per dex ([Z/H] = 0.25 0.05 dex per dex) for the total extent of the GC system, but we did not detect any colour gradient for the red subpopulation.

The total GC system is separated into subpopulations at (gi) = 0.93 (obtained from the GMM algorithm). Regarding the azimuthal distribution of GC subpopulations, both subpopulations are aligned along the position angle of the total GC system in the two methods. Also the ellipticity of both subpopulations matches with the total GC system. In the MA method, the total and both subpopulations are more elliptically aligned than the galaxy stellar light.

5.2.4 Radial colour distribution

Figure 15 shows the radial (gi) colour distribution of GC system of NGC 3608 selected on the MA method. To study this distribution, GCs (from the MA method) brighter than the turnover magnitude are selected and is 215 GCs. The total GC system and the red subpopulation show a null gradient, while the blue subpopulation shows a strong gradient along the total radial extent of the GC system. The colour distribution of the blue subpopulation is fitted with the logarithmic relation given in Equation 4, where R = 30 arcsec (Brodie et al., 2014). The parameters, a = 0.823 0.019 mag and b = 0.052 0.011 mag per dex, are derived from the best fit profile using the bootstrap technique (shown in Figure 15). The colour gradient, when converted to a metallicity gradient, gives [Z/H] = 0.25 0.05 dex per dex.

6 Discussion

6.1 GC system distribution and galaxy effective radius

In this study of two group galaxies (NGC 3607 and NGC 3608 situated within a projected distance of 39 kpc), we introduce two methods, the Surface Brightness and the Major Axis methods, to separate the individual GC systems. For NGC 3607, the radial GC system extent determined from both methods are consistent with each other and in good agreement with the empirical relation for GC system extent (Equation 2), initially presented in Kartha et al. (2014). From the radial surface density distribution, the red subpopulation is more centrally concentrated than the blue subpopulation. The galaxy surface brightness distribution is in agreement with the density distribution profile of the red subpopulation than the blue subpopulation (Figure 8). Also, the effective radius of the galaxy stars (39 arcsec) is consistent with that of the red GC subpopulation (40 29 arcsec), while for the blue GC subpopulation it is 95 50 arcsec. Both the spatial distribution and the effective radius measurements support the idea that the red GC subpopulation has evolutionary similarities with the galaxy stellar component (Forbes & Forte, 2001; Larsen et al., 2001; Brodie & Strader, 2006; Spitler, 2010; Forbes et al., 2012).

For NGC 3608, the blue GC subpopulation is more extended than the red GC subpopulation. It is evident from Figure 13 that the density distribution of the red GC subpopulation follows the galaxy stellar light distribution. However, the effective radius of galaxy light (30 arcsec) is half of the red subpopulation (59 40 arcsec) and one third of the blue subpopulation (85 18 arcsec). The effective radius of the red GC subpopulation is therefore not consistent with the stellar light component. Even so the resemblance of the density distribution profile with the galaxy stellar light might imply a significant association.

Figure 16: GC system effective radius versus galaxy effective radius. The plot displays NGC 3607, NGC 3608, NGC 4406, NGC 4472, NGC 4594 and NGC 5813 (represented by green diamonds) along with other six galaxies (filled black circles) from the earlier study of Kartha et al. (2014). The GC system effective radius is derived from the Sérsic profile fitted to the radial surface density distribution of GCs. The data in the plot are fitted with a linear relation using the bootstrap technique shown by a black line. The GC system effective radius is 6 times the galaxy stellar light.
Galaxy Effective radius Ref.
NGC GC system (kpc) Stellar light (kpc)
3607 14.22 4.21 1, 2
3608 9.11 3.20.7 1, 2
4406 28.21 7.60.5 3, 4
4472 58.48 7.90.8 3, 4
4594 16.81 3.20.7 3, 4
5813 36.63 8.80.8 3, 4

References: 1 - This work; 2 - Brodie et al. (2014); 3 - Hargis & Rhode (2014); 4 - Cappellari et al. (2011)

Table 6: Effective radii for the GC systems and respective stellar surface brightness, from the two galaxies in this paper (NGC 3607 and NGC 3608) and other four galaxies. The last column provides the references for the GC system and galaxy effective radii, respectively.

Figure 16 displays the total GC system effective radius versus the galaxy effective radius and is an updated version (with the addition of six galaxies) of figure 20 in Kartha et al. (2014). In this figure, the GC system effective radii are determined from Sérsic profile fits to the density distribution, which is currently available for twelve galaxies. The positions of the newly added galaxies, tabulated in Table 6, are compatible with the existing linear relation (R = [(5.2 3.7) R] (8.5 6.5)). The updated relation for the twelve galaxies is as follows:

(5)

where both Rs are measured in kpc. When compared with the relation in Kartha et al. (2014), the above relation has a similar slope within error bars. The effective radii for both GC subpopulations are determined only for six galaxies. With the available data, we could not detect any significant relation between the effective radius of GC subpopulations and the host galaxy stellar light.

From Equation 5, we can infer that the GC system effective radius is 6 times the galaxy effective radius, which confirms that the GC system of a galaxy extends further out than the bulk of its stellar component (Harris et al., 2000; Forbes et al., 2006b; Brodie & Strader, 2006; Alamo-Martínez et al., 2012; Cantiello et al., 2015). A byproduct from the above relation is that we can estimate the GC system effective radius by knowing the galaxy effective radius.

6.2 GC system ellipticity and galaxy ellipticity

To further address the association of galaxy stellar light with GC subpopulations, we need to study the two dimensional spatial distribution of these systems. Different studies of two dimensional distributions (position angle and ellipticity) have confirmed an association of both subpopulations with galaxy stellar light (e.g. NGC 2768 by Kartha et al. 2014, NGC 4636 by Dirsch et al. 2005). Park & Lee (2013) analysed the two dimensional shape parameters of 23 early-type galaxies using the HST/ACSVCS. They found that the arrangement of both subpopulations is aligned with the photometric major axis of galaxies. Also, the red GC subpopulations show a tight relation in ellipticity with galaxy stellar light, while the blue GC subpopulations show a less tight relation. Concurrently, Kartha et al. (2014) obtained a similar relation for the red subpopulations from a sample of six galaxies using wide-field imaging.

Figure 17: GC ellipticity versus galaxy stellar light ellipticity. The top and the bottom panels show the relation between ellipticities of blue GCs (open circles) and red GCs (filled diamonds) versus galaxy stellar light, respectively. Each panel includes an additional four galaxies (in red or blue colours) with the six galaxies (in black colour) from Kartha et al. (2014). A linear fit to the red GCs (red and black diamonds) is drawn as a solid line and a one-to-one relation is shown as a dashed line. The red GC subpopulation confirms a one-to-one relation with galaxy stellar light, whereas only a weak relation for the blue GC subpopulation is present.

Figure 17 demonstrates the relation between GC subpopulation ellipticity and galaxy stellar light ellipticity for ten early-type galaxies. The plot is an updated version of figure 22 in Kartha et al. (2014), with the addition of four galaxies (NGC 3607, NGC 3608, NGC 4406 and NGC 5813). Data for NGC 4406 and NGC 5813 are taken from Hargis & Rhode (2014). For NGC 4406, the stellar, blue and red subpopulation ellipticities are 0.4 0.03, 0.39 0.06 and 0.36 0.07, respectively. Similarly, the ellipticities for stellar, blue and red GC subpopulations of NGC 5813 are 0.3 0.03, 0.52 0.15 and 0.36 0.11, respectively. With the addition of four galaxies, we observe a tight one-to-one relation between red GC ellipticity and galaxy stellar light. The relation is

(6)

The intrinsic scatter in the above relation is estimated as 0.10. The one-to-one relation signifies that the red subpopulations are affiliated with the stellar light of the parent galaxies (Park & Lee, 2013). In other words, both the red subpopulation and galaxy stellar light might have a similar origin. In contrast to Park & Lee (2013), we notice a poor association of blue GC subpopulation ellipticity with galaxy stellar light ellipticity. We explain this as a consequence of our wide-field imaging, as the ACSVCS data used by Park & Lee (2013) does not reach far out enough to detect the whole blue GC subpopulation for the most extended galaxies (Peng et al., 2006).

In this small sample of ten galaxies, NGC 3607 shows the lowest ellipticity (nearly circular at = 0.13) for the galaxy stellar component. For NGC 3607, the ellipticities of both GC subpopulations show a deviation from the galaxy stellar light, although both are arranged along the photometric major axis of the galaxy. So, NGC 3607 supports the idea that galaxies with low ellipticities might have randomly arranged GC subpopulations (Wang et al., 2013). The difference in spatial distribution of GC subpopulations from the galaxy stellar component suggests that a major fraction of both GC subpopulations might have formed separately from the galactic stars and later settled in the host galaxies. In the case of NGC 3608, both GC subpopulations show deviations from the galaxy stellar light in position angle. In addition, the blue GC subpopulation shows a more elongated distribution than the red GC subpopulation.

In addition to NGC 3608, three other galaxies - NGC 4365, NGC 4406 and NGC 5813 - also have blue GC subpopulations more elongated in shape than the red GC subpopulations. The elongated shape of the blue GC subpopulation suggests that it shows spatial distribution similarities with the red subpopulation that mostly follows the distribution of galaxy stellar component. If the distribution of blue GCs is not spherical, Wang et al. (2013) suggest that it may not have been built from accretions that were equally distributed in all directions. Instead they might formed through local filamentary structures in particular directions. This points out that directional dependent accretion or minor mergers might have occurred in these galaxies, altering the shape of blue GC subpopulations.

In addition, these four elliptical galaxies are all slow rotators with kinematically distinct cores, KDCs, (Emsellem et al., 2011; Krajnović et al., 2011). Naab et al. (2014) carried out hydrodynamical simulations to kinematically study the centres of early-type galaxies. They suggested that KDCs were generally formed in slow rotators that had experienced multiple gas-poor minor mergers. They proposed that their recent mass assembly histories are devoid of any major mergers and are expected to have older stellar populations. Few, if any GCs, are expected to have formed from such mergers. It is unclear whether blue GCs from the accreted galaxies would form a more elongated distribution than the host galaxy starlight as we observe.

In summary, the ellipticities of red GC subpopulations have a one-to-one relation with the galaxy stellar light ellipticities, whereas only a weak relation is seen for the blue GC subpopulation. Additionally, slowly rotating galaxies with a KDC have larger values for blue GC subpopulation ellipticities than their red GC counterparts. The elongated shape of the blue GC subpopulations may be due to recent minor mergers that were asymmetric in direction (Tempel et al., 2015).

6.3 GC metallicity gradients and galaxy stellar mass

Colour gradients are important observational features for exploring the formation history of GC subpopulations and are clues to galaxy mass assembly. A negative colour gradient (GCs are redder at the centre of the galaxy than the outskirts) represents either the presence of younger (or more metal-rich) GCs at the galaxy centre or older (more metal-poor) GCs at the outskirts. As GCs are observed to be mostly old ( 10 Gyr, Strader et al. 2005; Dotter et al. 2010; Forbes et al. 2015), the colour gradients are basically caused by metallicity gradients rather than age gradients.

The observed gradients in GC subpopulations help discriminate between the different galaxy formation processes e.g., a negative gradient is predicted when the GCs are formed from a dissipative collapse (Pipino et al., 2010), while a gas-poor major merger will wash away any existing gradient (Di Matteo et al., 2009), a gas-rich major merger may remake a new gradient different from the original one (Hopkins et al., 2009), etc. Also, minor mergers (accretions) can deposit GCs in the outskirts of galaxies (Hirschmann et al., 2015; Pastorello et al., 2015) which will alter the existing gradient, perhaps resulting in an inner negative gradient and a flat outer gradient (Oser et al., 2010; Forbes et al., 2011).

The first detection of a radial colour gradient in a GC system was by Geisler et al. (1996) in NGC 4472. With ground based data, GC colour gradients have been detected in other massive galaxies (NGC 4486: Harris 2009, NGC 1407: Forbes et al. 2011, NGC 4365: Blom et al. 2012), while only seen in a handful of intermediate mass galaxies (NGC 3115: Arnold et al. 2011, NGC 4564: Hargis & Rhode 2014) to date.

In NGC 3607, another intermediate mass galaxy, the mean colours of both the blue and the red GC subpopulations reveal a significant colour gradient in the inner 6.5 arcmin (10 R). The colour gradient for the blue subpopulation is steeper than the red subpopulation. Within the total extent of the GC system (beyond 10 R), only the blue subpopulation has a significant colour gradient. We detect a significant colour gradient only for the blue GC subpopulation of NGC 3608.

Galaxy log(M) Metallicity Gradient Ref.
NGC Blue GCs Red GCs
(M) (dex dex) (dex dex)
1399 11.660 0.120.05 0.100.05  1, 14
1399 11.660 0.210.04  2, 11
1407 11.892 0.220.04 0.240.07  3, 10
3115 11.249 0.170.03 0.240.06  4, 10
3115 11.239 0.270.06 0.110.10  5,  5
3607 11.677 0.330.06 0.160.07  6, 10
3608 11.205 0.250.05  6, 10
3923 11.796 0.180.07 0.170.08  5,  5
4278 11.290 0.230.10 0.230.12  7, 14
4365 11.843 0.190.01 0.220.03  8, 14
4472 12.046 0.080.04 0.100.05  1, 14
4472 12.046 0.130.03 0.100.05  9, 11
4486 11.953 0.120.02 0.120.03  1, 14
4486 11.953 0.090.01 0.120.01 10, 10
4486 11.953 0.170.07 0.170.05 11, 11
4594 11.653 0.170.04 0.170.04 12, 12
4649 11.867 0.000.04 0.050.02  5,  5
4649 11.867 0.210.05 13, 14

References: 1 - Liu et al. (2011); 2 - Bassino et al. (2006a); 3 - Forbes et al. (2011); 4 - Arnold et al. (2011); 5 - Faifer et al. (2011); 6 - This paper; 7 - Usher et al. (2013); 8 - Blom et al. (2012); 9 - Geisler et al. (1996); 10 - Harris (2009); 11 - Forte et al. (2012); 12 - Hargis & Rhode (2014); 13 - Strader et al. (2012); 14 - Usher et al. (2013)

Table 7: List of twelve galaxies observed with metallicity gradients for GC subpopulations. The metallicity gradients ([Z/H]) given below are obtained from the colour gradients. Galaxy name, logarithmic galaxy stellar mass, metallicity gradients for blue and red GC subpopulations with errors and the corresponding references (for colour gradient followed by the transformation equation used) are given.
Figure 18: Metallicity gradients of GC subpopulations versus galaxy stellar mass. The metallicity gradients (converted from colour gradients) for the blue (blue open circles) and the red (red filled diamonds) GC subpopulations for 12 galaxies are plotted against host galaxy stellar mass. The blue solid line and the red dashed line represent the linear fits to the metallicity gradients of blue and red GCs, respectively. It is evident from the plot that for the blue GC subpopulation, the metallicity gradients become shallower with increasing galaxy stellar mass. Note that multiple measurements of metallicity gradients for the same galaxy are included.

Table 7 compiles the list of galaxies in which colour gradients are detected for the GC subpopulations. The colour gradients are detected in different colour filters. For a uniform comparison, the different colour gradients are converted to metallicity gradients ([Z/H]) using colour-metallicity transformation equations (references are given in Table 7). The converted metallicity gradients are given in Table 7. The list includes the gradients obtained only from wide-field imaging data and hence we exclude galaxies with single pointing HST/ACS imaging. This criterion excludes most galaxies from Liu et al. (2011), for which only the central regions of target galaxies were covered. The sample of galaxies in the Table includes two galaxies from this paper plus another ten galaxies. The galaxy stellar masses are also tabulated. In order to calculate the galaxy stellar masses, we used their distance and visual magnitude from NED and the mass to light ratios of Zepf & Ashman (1993). In the sample of twelve galaxies, half have multiple measurements of their metallicity gradients. We include all measured GC gradients and their quoted uncertainties for the 12 galaxies. This comprises 18 measurements of blue GC gradients and 15 measurements of red GC gradients for 12 galaxies.

In Table 7, multiple measurements are given for five galaxies. For the same galaxy, the observed gradients are not always consistent among different works. For example, in the case of NGC 4649 the gradients for the blue subpopulation are 0.00 0.04 (Faifer et al., 2011) and 0.21 0.05 (Strader et al., 2011). Both studies extended to 7 R. In another example, the gradient for the red subpopulation of NGC 3115 is quoted in Arnold et al. (2011) as 0.240.06, while Faifer et al. (2011) quoted 0.110.1. But in the case of NGC 4472, Geisler et al. (1996) and Liu et al. (2011) find consistent gradients for the red subpopulation.

Figure 18 shows the metallicity gradients for blue/red GC subpopulations versus the galaxy stellar mass. We plot multiple measurements for individual galaxies. Linear fits are carried out separately for the blue and the red GCs with uncertainties estimated from the bootstrap technique. The technique uses the errors associated with individual gradients. Best fit relations are:

(7)
(8)

The galaxies in our sample have a mass range 11.0 < log(M) < 12.0 M. From the above relations, we find that the metallicity gradient for the blue subpopulation has a significant correlation with stellar mass; the negative gradients flattens with increasing stellar mass. As more massive galaxies are expected to accrete more satellites (Oser et al., 2010), we expect more GC accretion to have taken place in these galaxies. This addition of mostly blue GCs at different galactocentric radii may make the initial gradient of the blue GCs shallower. In addition, Hirschmann et al. (2015) found that gradients resulting from major mergers are shallower in more massive galaxies due to radial mixing of GCs. From the spectroscopic metallicities of GC subpopulations in twelve ETGs, Pastorello et al. (2015) observe a similar trend of decreasing metallicity gradient with increasing galaxy stellar mass.

For the red GC subpopulation, we are unable to find a significant relation between the metallicity gradient and galaxy stellar mass. In comparison with the blue GC subpopulations, the metallicity gradients for the red GC subpopulations have higher errors and also a lower number of data points. In Table 7, the least significant gradient measurement is for the red subpopulation of NGC 3115 (Faifer et al., 2011). Hence, we carried out another fitting for the red GCs without that measurement and the best fitted relation is

(9)

From the above relation, we infer that the gradients for the red GC subpopulation show a very weak dependence on galaxy stellar mass.

The galaxy stellar mass is derived from the M/L ratios that are given in Zepf & Ashman (1993). We appreciate that the Zepf & Ashman (1993) values are an approximation but have chosen to use them as this is the approach we took in Kartha et al. (2014), which follows from the same approach as used by Rhode et al. (2010) and Spitler et al. (2008). So in order to match the results with the above mentioned publications, we use the same method. Bell et al. (2003) derived the relationships to estimate the stellar mass from galaxy colours (see Appendix B for details). We used their relationship to derive the galaxy stellar mass from (B - V) colour. We find that the equations 7 – 9 are statistically unchanged when using Bell et al. (2003) to derive galaxy stellar masses.

In summary, we suggest that the subsequent addition of GCs from minor mergers may weaken any pre-existing gradients in metallicity (from an early dissipative formation event) both for the blue and the red GC subpopulations.

6.4 Ratio of blue to red GCs with environment density and galaxy mass

Figure 19: Ratio of blue to red GCs versus density of environment. Spirals, lenticular galaxies and elliptical galaxies are represented by spirals, hexagons and stars respectively. The galaxies from the SLUGGS survey are shown in double symbols. This is an updated version of figure 21 in Kartha et al. (2014) with the addition of NGC 3607 and NGC 3608. The position coordinates for NGC 3607 and NGC 3608 are respectively, (0.34, 0.79) and (0.56, 1.85). NGC 3607 is consistent with the correlation shown by lenticular galaxies.
Figure 20: Ratio of blue to red GCs versus galaxy stellar mass. The representation of symbols is same as in Figure 19. For all types of galaxies, there is no correlation between the ratio of blue to red GCs and galaxy stellar mass. They have a mean ratio of N/N 1.7 for the total sample. An inset plot of ratio of blue to red GCs versus galaxy absolute B- band magnitude is given for the same sample. The mean ratio of blue to red GCs is 1.7 for the galaxy range 18.5 M 22 mag.

Figure 19 shows the ratio of blue to red GCs versus the environment density for a sample of 42 galaxies (two from this paper and forty from Kartha et al. 2014). We note that the blue to red GC ratio is largely insensitive to any GC magnitude incompleteness. With this sample of galaxies, it is evident that neither spiral nor elliptical galaxies show any particular trend in the ratio of blue to red GCs with environment density. NGC 3608, with N/N = 1.85 and density = 0.56 Mpc (Tully, 1988), is consistent with the other elliptical galaxies.

Kartha et al. (2014) found that the fraction of blue to red GCs in lenticular galaxies decreases with local density of environment. This suggests that lenticular galaxies residing in high density environments accommodate a higher fraction of red GCs. NGC 3607 is a lenticular galaxy with a relatively high fraction of red GCs (N/N = 0.79 and density = 0.34 Mpc from Tully 1988). The position of NGC 3607 is consistent with the trend of decreasing fraction of blue to red GCs with increasing environment density.

In Figure 20, the ratio of blue to red GCs is plotted against the host galaxy stellar mass for the above sample of 42 galaxies. There is no obvious correlation between the ratio of blue to red GCs with the galaxy stellar mass. We divide the galaxies into three mass bins of size 0.5 M and derive the mean value for the ratio of blue to red GCs. The mean ratio of blue to red GCs in the low (log(M)< 11 M), intermediate (11 < log(M) < 11.5 M) and high mass (log(M) > 11.5 M) bins are respectively, 1.7 0.8, 2.0 1.2 and 1.6 0.4. The mean ratio of blue to red GCs for the total sample of forty two galaxies is 1.76.

Using cosmological simulations, Bekki et al. (2008) investigated the structural, kinematical and chemical properties of GC systems in different Hubble type galaxies. They estimated the ratio of blue to red GCs, in the host galaxy luminosity range 14 < M < 22 to vary from 50 – 0.25, with an average of 1.5. Using the ACSVCS, Peng et al. (2006) also investigated the ratio of blue to red GCs in a similar luminosity range and determined that the fraction varies from 5.6 to 0.67 percent from low to high luminosity galaxies, suggesting an average ratio of 1.5 blue to red GCs over the total luminosity range.

There is a decreasing trend in the ratio of blue to red GCs with host galaxy luminosity, both observationally (Peng et al., 2006) and theoretically (Bekki et al., 2008). We observe a nearly constant ratio of blue to red GCs in our sample of 42 galaxies because our luminosity range is much more restricted. As seen in the inset of Figure 20, our sample of forty two galaxies lie in the galaxy luminosity range 18.5 M 22 mag, whereas the faint end extends to M = 14 mag for both Peng et al. (2006) and Bekki et al. (2008).

6.5 Formation of GC systems

6.5.1 NGC 3607 and NGC 3608

In the Leo II group, NGC 3607 is the massive central galaxy and has a red GC subpopulation fraction higher than the blue, while the neighbouring galaxy NGC 3608 is less massive and has a higher fraction of blue GCs. An overabundance of red GCs is observed along the minor axis of NGC 3607 (even after removing the GCs in the direction towards NGC 3608). From the azimuthal distribution of GCs of NGC 3608, it is found that both GC subpopulations are aligned in position angle and that angle is different from the position angle of the galaxy stellar light. These results (overabundance and misalignment) suggest a possible interaction between the galaxies in the group.

Using HST data, Lauer et al. (2005) carried out an imaging study of 77 early-type galaxies, including NGC 3607. They detected an additional gas disk settling in NGC 3607 perpendicular to the existing dusty disk. They commented that the dusty disk is in a transition phase merging with the gas disk. They explained this process as gas infalling directly onto the centre of NGC 3607 without disturbing the dusty disk and without any obvious features of interaction.

Later, Annibali et al. (2007) studied the stellar population properties of 66 early-type galaxies. They estimated the age, metallicity and alpha enhancement using the Lick indices with updated simple stellar population models (including the non-solar element abundance patterns). They estimated a very young age, 3.1 0.5 Gyr, for NGC 3607 and suggested it had experienced a recent episode of star formation. Rickes et al. (2009) carried out long slit spectroscopy, out to galactocentric radii of 30.5 arcsec, and claimed that NGC 3607 has undergone a minimum of three star formation episodes with ages ranging from 1 to 13 Gyr. The young age for the stellar population of NGC 3607 and the detection of a central gas disk indicate that NGC 3607 has experienced a recent star formation episode and the overabundance of red GCs may be due to GCs formed in that episode.

From the ATLAS survey, McDermid et al. (2015) estimated the mass-weighted ages for NGC 3605, NGC 3607 and NGC 3608 as 8.1 0.8, 13.5 0.7 and 13.0 0.7 Gyr respectively. They utilised the spectra within 1R to fit the single stellar population models and hence derive the mass-weighted ages, metallicity and star formation histories of 260 ETGs. Using the Lick indices, they estimated the age for NGC 3607 as 7.3 1.3 Gyr that contradicts the young age determined by Annibali et al. (2007).

Forbes et al. (2006a) carried out a multi-wavelength (X-ray, optical and Hi imaging) study of 60 galaxy groups, including the Leo II group. They investigated the evolutionary connections between different groups and the influence of group environment. In their study, they detected extended X-ray emission associated with the Leo II group but did not resolve individual galaxies. Recently, using Chandra X-ray data, Jang et al. (2014) observed X-ray emission from the central AGN in NGC 3607 and diffuse emission around NGC 3608. The detection of extended X-ray emissions confirms the presence of hot intergalactic gas.

The misalignment in the position angles of the GCs relative to the galaxy in NGC 3608 might be another sign of interaction with NGC 3607. Additionally, each galaxy shows an overabundance of GCs in the direct of the other, again suggesting a possible interaction between them. Jedrzejewski & Schechter (1988) proposed a close encounter between these two galaxies. They studied the absorption line kinematics for the stellar component of NGC 3608 and found a change in direction of the rotation curve between the core and outside region. They proposed that the reversal might be due to an interaction with the nearby NGC 3607.

We conclude that our results also support a possible interaction between the two galaxies. To confirm this proposition, deep surface photometric and detailed kinematic studies are needed.

6.5.2 Formation scenarios

As described in the introduction, three ’classic’ formation scenarios were proposed to explain bimodality in globular cluster systems. In the major merger model (Ashman & Zepf, 1992), the blue GCs already exist in the merging galaxies, while the red GCs form during the merging process. In the multi-phase collapse scenario (Forbes et al., 1997), the blue GCs are formed early, followed by a quiescent phase. After a few Gyrs, star formation is restarted with the formation of red GCs, which can be followed by accretion of additional blue GCs. According to Côté et al. (1998, 2000), the red GCs are inherent to the parent galaxies and the blue GCs are purely accreted from dwarf galaxies.

The three classic scenarios were explored in cosmological simulations which addressed a variety of GC system properties: structural and kinematical (Bekki et al., 2005), dynamical and chemical (Bekki et al., 2008), colour and metallicity bimodality (Muratov & Gnedin, 2010; Tonini, 2013), as well as physical relationships with the host galaxies (Beasley et al., 2002). Recently, Trenti et al. (2015) proposed another scenario for GC formation from the merging of multiple gas rich mini halos.

In all the classic formation scenarios, there is a strong association between red GC subpopulations and the parent galaxy. This relationship is established from different observations such as the strong relation between red GC peak colour and galaxy luminosity (Peng et al., 2006; Strader et al., 2006; Faifer et al., 2011), position angle arrangement of red GCs and the galaxy stellar component (Wang et al., 2013), connection between rotation velocity for red GCs and field stars (Pota et al., 2013) etc. On the other hand, the association between blue GC subpopulations and parent galaxy stars is weak. Peacock et al. (2015) found that the blue GC subpopulations of NGC 3115 are consistent with the stellar halo in metallicity and spatial distributions. However, the origin of the blue GC subpopulation is quite controversial. Côté et al. (1998, 2000) and Tonini (2013) proposed a dissipationless accretion origin whereas dissipational in-situ formation (Forbes et al., 1997; Beasley et al., 2003) is suggested for the formation of blue GCs in the inner regions. This distinction in region (inner or outer) is mentioned since accretion of blue GCs to the galaxy outskirts in the later phase is also included in the multi-phase scenario (Forbes et al., 1997).

Strader et al. (2004, 2005) investigated the feasibility of the above formation scenarios using observational data for massive elliptical galaxies. From the GC colour-galaxy luminosity relation and the age-metallicity relation, they proposed an in-situ plus accretion model for the formation of inner blue GCs which were then truncated by reionization, whereas the red GCs formed along with the bulk of field stars. They suggested that dwarf galaxies residing in overdense regions collapse before dwarfs in less dense regions, and then accrete more enriched gas from nearby star forming regions. These dwarf galaxies, along with their blue GCs, are later accreted into the halo of a massive galaxy forming part of the main system. This implies an in-situ+accretion origin for blue GCs. Hence, the origin of blue GCs in the inner regions could be due to one of three proposed processes, i.e. completely in-situ, fully accreted or in-situ+accretion.

In the following paragraphs, we try to differentiate between these three formation processes for blue GCs based on their global properties. In particular, we measure radial density, radial colour and azimuthal distributions in relation to their parent galaxies.

From the azimuthal distribution of GC subpopulations, both blue and red GCs have a positional arrangement in common with the galaxy stellar light component (Wang et al., 2013). This suggests that the blue GC subpopulation and galaxy stellar component have similar evolutionary histories. For galaxies in which the blue GCs and stars accreted from satellite dwarfs, this similarity is expected (Côté et al., 2001). From the derived ellipticities it is seen that red GC subpopulations have a one-to-one relation with the galaxy stellar component whereas the relation is not tight for blue GC subpopulations (see Figure 17). If the galaxy has accreted its blue GCs recently, then a complete one-to-one correlation with host galaxy properties is not expected. Park & Lee (2013) also investigated this relationship for 23 early-type galaxies using ACSVCS data and found an approximate one-to-one relation between blue GC ellipticity and the galaxy stellar component. As is well-known, the ACS field of view does not provide anywhere near complete coverage for massive nearby galaxies (Peng et al., 2006). That means a nearly one-to-one relation between inner blue GCs and galaxy stellar component suggests a common origin for both and hence, supports the in-situ formation scenario.

Another diagnostic trend is the GC subpopulation peak colour versus galaxy luminosity. The peak colour of the red GC subpopulation gets redder with increasing galaxy luminosity. Perhaps a weaker correlation exists for the blue GC subpopulation. Liu et al. (2011) found that projection effects tend to flatten GC radial trends, particularly for the blue subpopulation because if its extended nature. Hence, the slope of the relation between the blue GC subpopulation peak colour and galaxy luminosity is reduced to half of the earlier value (0.0126 0.0025: Peng et al. 2006), making the relation between peak colour of the blue GC subpopulation and galaxy luminosity insignificant. This result weakens the idea that the formation of blue GCs is via in-situ processes.

Radial colour gradients may also reveal the origin of blue GCs. The colour gradients for blue GCs formed in-situ are expected to be steeper than for a subpopulation formed from in-situ+accretion or completely accreted processes. We expect this because the addition of GCs through accretion can dilute (in the case of in-situ+accretion) the existing colour gradient for the blue GC subpopulation. In the case of complete accretion, we assume zero colour gradient for the blue subpopulation. Hence, to disentangle the origin of blue GCs, the steepness of the gradient needs to be quantified with large samples of galaxies where the colour gradients are measured with maximum accuracy. Our present work is limited by a small sample of 10 galaxies collected from the literature (Geisler et al., 1996; Bassino et al., 2006a; Harris, 2009; Forbes et al., 2011; Faifer et al., 2011; Arnold et al., 2011; Blom et al., 2012; Usher et al., 2013; Hargis & Rhode, 2014) and two from this work. Liu et al. (2011) carried out an analysis of the colour gradients for 76 early-type galaxies using ACSVCS and ACS Fornax Cluster Survey (FCS). Even though the sample size is impressive, only three galaxies have more than one pointing and we have included them in the above sample. Hence, significant color gradients are detected in a total of 12 galaxies, five of which have multiple measurement. Gradient values are provided in Table 7.

Figure 18 show this sample of GC metallicity gradients plotted against host galaxy stellar mass. The blue GC subpopulation shows a trend of decreasing gradient with increasing galaxy stellar mass. This implies that high mass galaxies have shallower gradients, whereas low mass (log(M) 11.0 M) galaxies have steeper gradients. As the metallicity gradients show a dependency on galaxy stellar mass, both the GC subpopulations are expected to have some formational similarities with the galaxy stellar component. This means that a completely accreted origin (Côté et al., 1998, 2000; Tonini, 2013) may not be the best scenario to explain the formation of blue GCs. Also, we notice that both GC subpopulation gradients show a dependence on galaxy stellar mass. Thus, a common, or in-situ origin (Forbes et al., 1997; Beasley et al., 2003), is probably involved in the formation of blue and red GC subpopulations (Pastorello et al., 2015). However, we note that large red (early-type) galaxies tend to preferentially accrete red satellite galaxies (Hearin et al., 2014; Hudson et al., 2015). Thus GC system metallicity gradients may also reflect the gradients of the accreted satellites, if they are preserved in the accretion process. In the in-situ+accretion formation scenario (Strader et al., 2004, 2005) for the blue GCs, we expect the gradient to be shallower than for the blue GCs formed completely in-situ, but a reference scale is not yet established by models.

To summarise, from the present study it is difficult to ascribe either a completely in-situ or an in-situ+accretion origin for the blue GC subpopulations. A homogeneous large sample with accurate GC properties is needed to address this issue in depth.

7 Conclusions

We present wide-field imaging data from the Subaru telescope with which we carry out an investigation of the GC systems in the Leo II group to large galactocentric radii ( 120 kpc). Using the multi-band wide-field images in g, r and i filters, we analysed the radial density, radial colour and azimuthal distributions of GC systems in the two brightest galaxies of the group, NGC 3607 and NGC 3608. Our study is complemented with spectroscopic data obtained from DEIMOS on the Keck II telescope. We present the main conclusions here.

  1. The GC systems of NGC 3607 and NGC 3808 are found to have radial extents of 9.5 0.6 arcmin (equivalent to 61 5 kpc or 4.4 R) and 6.6 0.8 arcmin (equivalent to 43 5 kpc or 4.7 R), respectively. The derived values are in agreement with estimates obtained from the empirical relation between the effective radius of the GC system and galaxy stellar light given in Kartha et al. (2014).

  2. The GC system colours of both galaxies are fitted with the GMM algorithm and we detect a bimodal distribution with confidence level greater than 99.99 percent. NGC 3607 is observed to have 45 9 and 55 8 percent of blue and red GC subpopulations, while for NGC 3608 the blue and red GC subpopulations contribute 65 6 and 35 6 percent to the total GC system.

  3. From the radial velocity measurements, we detect 81 GCs in the field of the Leo II group. We assign 46 and 35 GCs, respectively, to NGC 3607 and NGC 3608. We estimate a mean velocity of 963 and 1220 km/s for NGC 3607 and NGC 3608, respectively. Also, the mean GC velocity dispersions for the respective galaxies are 167 and 147 km/s.

  4. From the radial density distributions of the GC subpopulations of NGC 3607, the red subpopulation is more centrally located while the blue subpopulation is more extended. Also, the effective radius of the red GC subpopulation (40 29 arcsec) and the galaxy stellar light (39 arcsec) are in good agreement, compared to the blue subpopulation (95 50 arcsec).

  5. For NGC 3608, the blue subpopulation is more extended in radius than the centrally concentrated red subpopulation. The red subpopulation distribution shows similarities with the galaxy surface brightness distribution. However, the effective radius of the red subpopulation (59 40 arcsec) is larger than the galaxy stellar light (30 arcsec).

  6. The azimuthal distribution of the NGC 3607 GC system reveals that both subpopulations are aligned along a position angle ( 110 degrees), which is in reasonable agreement with the galaxy stellar light (125 degrees). However, the distribution of the GC system is more elliptical in comparison with the circular distribution of galaxy stellar light. The red subpopulation shows a more elliptical distribution when compared with the blue subpopulation.

  7. For NGC 3608, the GCs are arranged along position angles that are different from the galaxy stellar population. Using two different methods of GC selection, the position angles for the total GC system are found to be along 104 15 and 67 7 degrees, while the galaxy major axis is at 82 degrees. One method of GC selection suggests that the GCs have an ellipticity = 0.20 0.09, while the other shows an ellipticity of 0.39 0.10. By comparison, the stellar light ellipticity is 0.20. In NGC 3608, the blue subpopulation has a more elliptical arrangement than the red subpopulation.

  8. The total GC system, and both subpopulations of NGC 3607, become bluer in colour with increasing galactocentric radius; a significant metallicity gradient is observed for both subpopulations. We find that the blue subpopulation has a steeper gradient than the red subpopulation. We also detect a strong colour gradient only for the blue subpopulation of NGC 3608. The colour gradient for the blue subpopulation in NGC 3608 is steeper than that in NGC 3607.

We compare different global properties of the GC systems and their parent galaxies. We reconfirm that the extent of the GC system is a function of galaxy size and the effective radius of a GC system is nearly 6 times the effective radius of parent galaxy. We obtain a one-to-one relation between the parent red GC ellipticities and galaxy stellar light ellipticities. Also, the blue GC ellipticities of slow rotators with kinematically decoupled cores are more elongated than their red GC subpopulation ellipticities. We propose that they might have experienced recent minor mergers from anisotropic directions (Tempel et al., 2015).

From a sample of twelve galaxies, we investigate the relationship between the metallicity gradients and host galaxy stellar mass. We found that the gradients of both GC subpopulations become shallower with increasing stellar mass. The average ratio of blue to red GCs in galaxies in the mass range 11.0 log(M) 12.0 M is nearly 1.7. These findings agree with the predictions from the simulations of Bekki et al. (2008) and also with the findings from other observations (Peng et al., 2006). We also carried out a study to disentangle the formation of blue GC subpopulations (i.e. completely in-situ versus in-situ+accretion versus completely accreted), which have not given conclusive results and need to be followed up with a homogeneous, large sample.

Acknowledgments

We thank the anonymous referee for his/her careful read- ing of the manuscript and the valuable feedbacks. The authors extend their gratitude to Jacob A. Arnold and Kristin A. Woodley regarding their help in observations. We thank Blesson Mathew and Nicola Pastorello for the careful reading of the manuscript. We also acknowledge the members of SAGES group, especially Christopher Usher, Vincenzo Pota and Joachim Janz, for the support and enlightening discussions. SSK thanks the Swinburne University for the SUPRA fellowship. DAF thanks the ARC for support via DP-130100388. This work was supported by NSF grant AST-1211995. This paper was based in part on data collected at Subaru Telescope, which is operated by the National Astronomical Observatory of Japan. This paper uses data products produced by the OIR Telescope Data Center, supported by the Smithsonian Astrophysical Observatory. Based on observations made with the NASA/ESA Hubble Space Telescope, and obtained from the Hubble Legacy Archive, which is a collaboration between the Space Telescope Science Institute (STScI/NASA), the Space Telescope European Coordinating Facility (ST-ECF/ESA) and the Canadian Astronomy Data Centre (CADC/NRC/CSA). Some of the data presented herein were obtained at the W. M. Keck Observatory, operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration, and made possible by the generous financial support of the W. M. Keck Foundation. The authors wish to recognize and acknowledge the very significant cultural role and reverence that the summit of Mauna Kea has always had within the indigenous Hawaiian community. We are most fortunate to have the opportunity to conduct observations from this mountain. This research has made use of the NASA/IPAC Extragalactic Data base (NED) operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. The analysis pipeline used to reduce the DEIMOS data was developed at UC Berkeley with support from NSF grant AST-0071048. We acknowledge the usage of the HyperLeda database (http://leda.univ-lyon1.fr).

Appendix A List of spectroscopically confirmed objects around the Leo II group

Table 8 presents the photometric magnitudes g, r and i and the radial velocities for GCs, Galactic stars and background galaxies detected around NGC 3607 and NGC 3608 in the Leo II group.

ID RA Dec g g r r i i V V
(degree) (degree) (mag) (mag) (mag) (mag) (mag) (mag) (km/s) (km/s)
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)
NGC3607_GC1 169.217229 18.003300 22.320 0.002 21.863 0.003 21.653 0.003  958 14
NGC3607_GC2 169.231425 18.022400 22.782 0.003 22.019 0.003 21.622 0.003  924 9
NGC3607_GC3 169.298829 18.025774 22.706 0.004 22.228 0.003 22.027 0.004 1255 13
NGC3607_GC4 169.268788 18.026516 22.646 0.003 21.898 0.002 21.533 0.003  910 12
NGC3607_GC5 169.247763 18.029331 21.396 0.001 20.756 0.001 20.492 0.001  904 5
NGC3607_GC6 169.239792 18.027800 22.370 0.002 21.734 0.002 21.477 0.003  825 15
NGC3607_GC7 169.201883 18.032566 23.010 0.003 22.235 0.003 21.837 0.003  961 17
NGC3607_GC8 169.251525 18.034105 22.218 0.002 21.596 0.002 21.361 0.002  732 13
NGC3607_GC9 169.208850 18.035141 20.924 0.001 20.340 0.001 20.115 0.001  950 4
NGC3607_GC10 169.230150 18.037170 22.053 0.002 21.321 0.002 21.027 0.002  924 15
NGC3607_GC11 169.199067 18.037264 23.225 0.004 22.434 0.004 22.066 0.004 1136 15
NGC3607_GC12 169.159492 18.033494 24.264 0.011 23.764 0.014 23.499 0.014 1052 12
NGC3607_GC13 169.236067 18.038172 22.161 0.002 21.525 0.002 21.279 0.002  792 13
NGC3607_GC14 169.099217 18.039568 22.358 0.002 21.732 0.002 21.482 0.002 1048 15
NGC3607_GC15 169.207533 18.043100 23.017 0.004 22.368 0.004 22.065 0.004  764 17
NGC3607_GC16 169.213671 18.044300 23.698 0.005 23.089 0.006 22.770 0.007 1060 14
NGC3607_GC17 169.237692 18.047800 21.814 0.001 21.057 0.001 20.719 0.001  920 12
NGC3607_GC18 169.238979 18.049000 23.307 0.007 22.484 0.007 21.903 0.006 1092 13
NGC3607_GC19 169.184475 18.049648 22.226 0.002 21.579 0.003 21.377 0.002  966 13
NGC3607_GC20 169.194458 18.052082 22.753 0.003 22.098 0.003 21.713 0.004  974 14
NGC3607_GC21 169.240721 18.054500 21.360 0.001 20.738 0.001 20.463 0.001  598 9
NGC3607_GC22 169.220738 18.047815 21.831 0.001 21.313 0.001 21.092 0.002 1303 19
NGC3607_GC23 169.242683 18.055283 21.524 0.001 20.757 0.001 20.448 0.001  840 9
NGC3607_GC24 169.265371 18.060005 22.899 0.003 22.149 0.003 21.811 0.003 1027 11
NGC3607_GC25 169.232196 18.058500 23.611 0.006 22.948 0.006 22.492 0.006  987 13
NGC3607_GC26 169.271792 18.065065 23.112 0.004 22.420 0.004 22.110 0.004 1025 12
NGC3607_GC27 169.186392 18.065200 21.930 0.002 21.293 0.003 21.060 0.003  848 16
NGC3607_GC28 169.219979 18.068300 21.646 0.001 20.972 0.001 20.655 0.001  835 6
NGC3607_GC29 169.237104 18.074398 22.956 0.004 22.211 0.004 21.830 0.004  958 16
NGC3607_GC30 169.213671 18.076300 24.242 0.009 23.568 0.009 23.240 0.01 1012 12
NGC3607_GC31 169.308429 18.075178 23.142 0.004 22.514 0.004 22.248 0.005  949 14
NGC3607_GC32 169.229092 18.086800 22.753 0.003 22.003 0.003 21.650 0.003  815 9
NGC3607_GC33 169.212542 18.087000 21.839 0.001 21.158 0.001 20.890 0.002  985 7
NGC3607_GC34 169.160442 18.106018 22.294 0.002 21.748 0.002 21.521 0.003  694 9
NGC3607_GC35 169.106842 18.140724 23.123 0.004 22.499 0.004 22.267 0.005 1138 18
NGC3607_GC36 169.376407 18.206726 22.467 0.002 21.941 0.003 21.741 0.003 1106 14
NGC3607_GC37 169.189700 18.011189 23.796 0.005 23.047 0.005 22.757 0.006  723 17
NGC3607_GC38 169.211025 18.018753 22.581 0.002 22.030 0.003 21.810 0.003  940 18
NGC3607_GC39 169.222354 18.045021 21.901 0.001 21.198 0.001 20.898 0.001 1145 23
NGC3607_GC40 169.203471 18.038366 22.804 0.004 22.081 0.004 21.699 0.004  775 14
NGC3607_GC41 169.233100 18.040434 22.867 0.003 22.145 0.003 21.782 0.003  873 26
NGC3607_GC42 169.242008 18.074684 24.662 0.013 24.083 0.016 23.961 0.018 1188 19
NGC3607_GC43 169.107099 18.244194 22.618 0.003 22.043 0.003 21.819 0.003 1279 16
NGC3608_GC1 169.231942 18.129700 25.547 0.021 24.951 0.023 24.783 0.028 1039 13
NGC3608_GC2 169.246717 18.131090 22.657 0.003 22.041 0.003 21.806 0.003 1293 13
NGC3608_GC3 169.261129 18.135875 21.721 0.001 21.065 0.001 20.849 0.001 1091 6
NGC3608_GC4 169.227192 18.138500 21.595 0.001 20.916 0.001 20.648 0.001 1272 8
NGC3608_GC5 169.298158 18.138886 22.732 0.003 22.140 0.003 21.918 0.003 1242 15
NGC3608_GC6 169.245833 18.146111 23.950 0.007 23.105 0.006 22.668 0.006 1176 29
NGC3608_GC7 169.269692 18.139126 22.568 0.002 21.890 0.003 21.623 0.003 1458 9
NGC3608_GC8 169.263733 18.142671 24.392 0.009 23.568 0.009 23.227 0.009 1141 15
NGC3608_GC9 169.224058 18.145254 22.229 0.002 21.650 0.002 21.418 0.002 1493 14
NGC3608_GC10 169.241266 18.14431 21.213 0.001 20.639 0.001 20.371 0.001 1203 12
NGC3608_GC11 169.275825 18.151506 24.155 0.011 23.506 0.011 23.181 0.013 1467 19
NGC3608_GC12 169.298200 18.149405 22.834 0.003 22.243 0.003 22.044 0.004 1061 12
NGC3608_GC13 169.222383 18.155500 22.851 0.003 22.260 0.003 22.029 0.004  957 15
NGC3608_GC14 169.243342 18.156600 22.166 0.002 21.581 0.002 21.258 0.002 1335 12
NGC3608_GC15 169.229208 18.167700 23.038 0.003 22.479 0.003 22.272 0.004 1229 17
NGC3608_GC16 169.324208 18.163569 22.591 0.002 21.995 0.003 21.763 0.003 1268 9
NGC3608_GC17 169.260221 18.165211 22.266 0.002 21.670 0.002 21.487 0.002 1283 12
NGC3608_GC18 169.273496 18.165411 23.057 0.005 22.558 0.005 22.414 0.005 1038 13
NGC3608_GC19 169.292936 18.166916 21.915 0.002 21.332 0.002 21.130 0.002 1176 7
NGC3608_GC20 169.229207 18.167725 22.623 0.002 21.976 0.002 21.741 0.003 1233 14
NGC3608_GC21 169.252583 18.168700 22.874 0.003 22.248 0.004 22.035 0.004 1247 19
NGC3608_GC22 169.320831 18.169830 22.265 0.002 21.576 0.002 21.289 0.002 1383 7
NGC3608_GC23 169.337812 18.171459 22.885 0.003 22.267 0.004 22.031 0.004 1294 11
NGC3608_GC24 169.220495 18.173113 23.444 0.006 22.925 0.005 22.703 0.006 1118 17
NGC3608_GC25 169.259806 18.179447 22.292 0.003 21.759 0.003 21.582 0.003 1385 8
NGC3608_GC26 169.237946 18.138157 23.728 0.005 22.946 0.005 22.619 0.005 1031 26
NGC3608_GC27 169.271321 18.159868 23.265 0.004 22.567 0.004 22.266 0.004  808 22
NGC3608_GC28 169.228779 18.133675 22.476 0.002 21.936 0.002 21.736 0.003 1358 17
NGC3608_GC29 169.266946 18.158815 23.398 0.004 22.629 0.004 22.304 0.004 1076 18
NGC3608_GC30 169.213571 18.159685 23.121 0.004 22.496 0.004 22.268 0.004 1238 25
NGC3608_GC31 169.254055 18.160824 23.176 0.004 22.574 0.004 22.354 0.005 1328 12
NGC3608_GC32 169.256616 18.169390 23.532 0.005 22.965 0.006 22.689 0.006 1180 19
NGC3607_GC44 169.303350 18.082405 22.710 0.003 22.061 0.003 21.800 0.003 1318 11
NGC3607_GC45 169.245171 18.095700 22.927 0.003 22.233 0.003 21.962 0.004 1089 14
NGC3607_GC46 169.184189 18.164358 22.793 0.003 21.974 0.003 21.599 0.003  807 9
NGC3608_GC33 169.203088 18.135864 22.218 0.002 21.563 0.002 21.314 0.002 1160 10
NGC3608_GC34 169.217304 18.109472 22.905 0.004 22.333 0.004 22.084 0.005 1281 23
NGC3608_GC35 169.192558 18.121163 23.079 0.004 22.484 0.004 22.250 0.005 1229 18
NGC3608_ext1 169.197333 18.036472 22.384 0.0020 21.893 0.0020 21.761 0.0030 1822 22
NGC3608_star1 169.181300 18.000490 22.633 0.002 22.119 0.003 21.950 0.003  113 11
NGC3608_star2 169.189558 18.004114 21.235 0.001 20.761 0.001 20.625 0.001 106 7
NGC3608_star3 169.223729 18.025229 25.096 0.017 24.464 0.018 24.073 0.019   26 4
NGC3608_star4 169.106475 18.053684 25.544 0.032 24.902 0.031 24.719 0.039   90 10
NGC3608_star5 169.109129 18.074558 21.437 0.001 20.954 0.001 20.821 0.002  37 10
NGC3608_star6 169.293779 18.109633 21.351 0.001 20.865 0.001 20.739 0.001  139 7
NGC3608_star7 169.243571 18.115063 22.741 0.003 22.112 0.003 21.871 0.004  59 17
NGC3608_star8 169.251296 18.129112 22.151 0.002 21.527 0.002 21.315 0.002  159 11
NGC3608_star9 169.198996 18.147369 21.484 0.001 20.985 0.001 20.849 0.002   87 9
NGC3608_star10 169.148208 18.176922 25.108 0.030 24.433 0.033 24.134 0.027 107 16
NGC3608_star11 169.389467 18.250843 22.643 0.003 22.093 0.003 21.938 0.004   66 12
NGC3608_gal1 169.295992 18.040196 22.481 0.003 21.922 0.003 21.613 0.003 - -
NGC3608_gal2 169.190583 18.043064 23.802 0.006 23.230 0.006 23.023 0.008 - -
NGC3608_gal3 169.076800 18.045994 23.127 0.004 22.546 0.004 22.340 0.005 - -
NGC3608_gal4 169.218121 18.052868 21.831 0.001 21.313 0.001 21.092 0.002 - -
NGC3608_gal5 169.111229 18.095403 25.878 0.043 25.324 0.056 24.929 0.056 - -
NGC3608_gal6 169.125054 18.108242 22.672 0.003 22.205 0.003 22.070 0.004 - -
NGC3608_gal7 169.289267 18.119137 25.637 0.025 24.959 0.032 24.744 0.040 - -
NGC3608_gal8 169.159358 18.134090 25.586 0.027 25.101 0.032 24.762 0.037 - -
NGC3608_gal9 169.115850 18.138969 21.265 0.001 20.782 0.001 20.536 0.002 - -
NGC3608_gal10 169.219142 18.155857 23.989 0.008 23.270 0.008 23.021 0.009 - -
NGC3608_gal11 169.245833 18.148889 22.166 0.002 21.581 0.002 21.258 0.002 - -
NGC3608_gal12 169.282651 18.165146 23.456 0.005 22.881 0.006 22.709 0.008 - -
NGC3608_gal13 169.273496 18.165411 23.057 0.005 22.558 0.005 22.414 0.005 - -
NGC3608_gal14 169.199783 18.173775 25.439 0.041 24.884 0.042 24.507 0.038 - -
NGC3608_gal15 169.210004 18.011913 23.890 0.007 23.230 0.008 22.909 0.009 - -
Table 8: Catalogue of objects detected around NGC 3607 and NGC 3608. The horizontal lines differentiate GCs of NGC 3607, GCs of NGC 3608, 7 ambiguous objects (classified into GCs and probable UCD - see Section 4.3), Galactic stars and background galaxies. Column 1 represents the object ID with the galaxy name followed by the object classification such as GC, star and galaxy. Columns 2 and 3 present the position in Right Ascension and Declination (J2000). Columns 4 – 9 present the Subaru/Suprime-Cam photometry in g, r and i filters and their respective uncertainties (given here are extinction corrected magnitudes). The heliocentric velocity and the respective uncertainty for each object is given in column 10 and 11.

Appendix B M/l ratio calculation using Bell et al. (2003)

Relationships between stellar M/L values and various colours in SDSS and 2MASS passbands are given in Bell et al. (2003). They derived these relationships by fitting galaxy evolution models to a large sample of 22679 galaxies from the SDSS Early Data Release (Stoughton et al. 2002) and 2MASS extended source catalog (Jarrett et al. 2000). To estimate the stellar mass for our sample of 42 galaxies, we utilize the relationship between M/L ratio and (B V) colour which is given below.

(10)

We find that the Bell et al. (2003) M/L ratios are about a factor of 2 times lower for ellipticals and a factor of 1.5 times lower for lenticulars than Zepf & Ashman (1993) values. This affects the X-axes of Figures 18 and 20. Hence, we fit the trends in Figure 18 after incorporating the stellar mass from Bell et al. (2003). The fits are given below which can be compared to Equations 7 – 9.

(11)
(12)
(13)

We find that even if the stellar mass varies between Zepf & Ashman (1993) and Bell et al. (2003), the relationships shown by blue and red GCs with metallicity remains statistically the same. This also implies that our results remain unchanged between different M/L ratio estimations.

References

  • Alamo-Martínez et al. (2012) Alamo-Martínez K. A., West M. J., Blakeslee J. P., González-Lópezlira R. A., Jordán A., Gregg M., Côté P., Drinkwater M. J., van den Bergh S., 2012, A&A, 546, A15
  • Annibali et al. (2007) Annibali F., Bressan A., Rampazzo R., Zeilinger W. W., Danese L., 2007, A&A, 463, 455
  • Arnold et al. (2011) Arnold J. A., Romanowsky A. J., Brodie J. P., Chomiuk L., Spitler L. R., Strader J., Benson A. J., Forbes D. A., 2011, ApJL, 736, L26
  • Ashman & Zepf (1992) Ashman K. M., Zepf S. E., 1992, ApJ, 384, 50
  • Bassino et al. (2006a) Bassino L. P., Faifer F. R., Forte J. C., Dirsch B., Richtler T., Geisler D., Schuberth Y., 2006a, A&A, 451, 789
  • Bassino et al. (2006b) Bassino L. P., Richtler T., Dirsch B., 2006b, MNRAS, 367, 156
  • Beasley et al. (2002) Beasley M. A., Baugh C. M., Forbes D. A., Sharples R. M., Frenk C. S., 2002, MNRAS, 333, 383
  • Beasley et al. (2003) Beasley M. A., Harris W. E., Harris G. L. H., Forbes D. A., 2003, MNRAS, 340, 341
  • Bekki et al. (2005) Bekki K., Beasley M. A., Brodie J. P., Forbes D. A., 2005, MNRAS, 363, 1211
  • Bekki et al. (2008) Bekki K., Yahagi H., Nagashima M., Forbes D. A., 2008, MNRAS, 387, 1131
  • Bell et al. (2003) Bell E. F., McIntosh D. H., Katz N., Weinberg M. D., 2003, ApJS, 149, 289
  • Bertin & Arnouts (1996) Bertin E., Arnouts S., 1996, A&AS, 117, 393
  • Blom et al. (2012) Blom C., Spitler L. R., Forbes D. A., 2012, MNRAS, 420, 37
  • Brodie et al. (2011) Brodie J. P., Romanowsky A. J., Strader J., Forbes D. A., 2011, AJ, 142, 199
  • Brodie et al. (2014) Brodie J. P. et al., 2014, ApJ, 796, 52
  • Brodie & Strader (2006) Brodie J. P., Strader J., 2006, ARAA, 44, 193
  • Brodie et al. (2012) Brodie J. P., Usher C., Conroy C., Strader J., Arnold J. A., Forbes D. A., Romanowsky A. J., 2012, ApJL, 759, L33
  • Cantiello et al. (2015) Cantiello M. et al., 2015, A&A, 576, A14
  • Cappellari et al. (2011) Cappellari M. et al., 2011, MNRAS, 413, 813
  • Cappellari et al. (2013) Cappellari M. et al., 2013, MNRAS, 432, 1862
  • Coccato et al. (2009) Coccato L. et al., 2009, MNRAS, 394, 1249
  • Côté et al. (1998) Côté P., Marzke R. O., West M. J., 1998, ApJ, 501, 554
  • Côté et al. (2000) Côté P., Marzke R. O., West M. J., Minniti D., 2000, ApJ, 533, 869
  • Côté et al. (2001) Côté P., McLaughlin D. E., Hanes D. A., Bridges T. J., Geisler D., Merritt D., Hesser J. E., Harris G. L. H., Lee M. G., 2001, ApJ, 559, 828
  • de Vaucouleurs et al. (1991) de Vaucouleurs G., de Vaucouleurs A., Corwin, Jr. H. G., Buta R. J., Paturel G., Fouqué P., 1991, Third Reference Catalogue of Bright Galaxies. Volume I: Explanations and references. Volume II: Data for galaxies between 0 and 12. Volume III: Data for galaxies between 12 and 24.
  • Di Matteo et al. (2009) Di Matteo P., Pipino A., Lehnert M. D., Combes F., Semelin B., 2009, A&A, 499, 427
  • Dirsch et al. (2005) Dirsch B., Schuberth Y., Richtler T., 2005, A&A, 433, 43
  • Dotter et al. (2010) Dotter A. et al., 2010, ApJ, 708, 698
  • Duc et al. (2015) Duc P.-A. et al., 2015, MNRAS, 446, 120
  • Emsellem et al. (2011) Emsellem E. et al., 2011, MNRAS, 414, 888
  • Faber et al. (2003) Faber S. M. et al., 2003, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 4841, Instrument Design and Performance for Optical/Infrared Ground-based Telescopes, Iye M., Moorwood A. F. M., eds., pp. 1657–1669
  • Faifer et al. (2011) Faifer F. R., Forte J. C., Norris M. A., Bridges T., Forbes D. A., Zepf S. E., Beasley M., Gebhardt K., Hanes D. A., Sharples R. M., 2011, MNRAS, 416, 155
  • Forbes et al. (1997) Forbes D. A., Brodie J. P., Grillmair C. J., 1997, AJ, 113, 1652
  • Forbes & Forte (2001) Forbes D. A., Forte J. C., 2001, MNRAS, 322, 257
  • Forbes et al. (2015) Forbes D. A., Pastorello N., Romanowsky A. J., Usher C., Brodie J. P., Strader J., 2015, MNRAS, 452, 1045
  • Forbes et al. (2012) Forbes D. A., Ponman T., O’Sullivan E., 2012, MNRAS, 425, 66
  • Forbes et al. (2006a) Forbes D. A., Ponman T., Pearce F., Osmond J., Kilborn V., Brough S., Raychaudhury S., Mundell C., Miles T., Kern K., 2006a, PASA, 23, 38
  • Forbes et al. (2006b) Forbes D. A., Sánchez-Blázquez P., Phan A. T. T., Brodie J. P., Strader J., Spitler L., 2006b, MNRAS, 366, 1230
  • Forbes et al. (2011) Forbes D. A., Spitler L. R., Strader J., Romanowsky A. J., Brodie J. P., Foster C., 2011, MNRAS, 413, 2943
  • Forte et al. (2012) Forte J. C., Vega E. I., Faifer F., 2012, MNRAS, 421, 635
  • Geisler et al. (1996) Geisler D., Lee M. G., Kim E., 1996, AJ, 111, 1529
  • Gnedin et al. (2014) Gnedin O. Y., Ostriker J. P., Tremaine S., 2014, ApJ, 785, 71
  • Graham & Driver (2005) Graham A. W., Driver S. P., 2005, PASA, 22, 118
  • Griffen et al. (2010) Griffen B. F., Drinkwater M. J., Thomas P. A., Helly J. C., Pimbblet K. A., 2010, MNRAS, 405, 375
  • Hargis & Rhode (2014) Hargis J. R., Rhode K. L., 2014, ApJ, 796, 62
  • Harris (2009) Harris W. E., 2009, ApJ, 703, 939
  • Harris (2010) Harris W. E., 2010, Royal Society of London Philosophical Transactions Series A, 368, 889
  • Harris et al. (2000) Harris W. E., Kavelaars J. J., Hanes D. A., Hesser J. E., Pritchet C. J., 2000, ApJ, 533, 137
  • Hearin et al. (2014) Hearin A. P., Watson D. F., Becker M. R., Reyes R., Berlind A. A., Zentner A. R., 2014, MNRAS, 444, 729
  • Hirschmann et al. (2015) Hirschmann M., Naab T., Ostriker J. P., Forbes D. A., Duc P.-A., Davé R., Oser L., Karabal E., 2015, MNRAS, 449, 528
  • Hopkins et al. (2009) Hopkins P. F., Cox T. J., Dutta S. N., Hernquist L., Kormendy J., Lauer T. R., 2009, ApJS, 181, 135
  • Hudson et al. (2015) Hudson M. J. et al., 2015, MNRAS, 447, 298
  • Jang et al. (2014) Jang I., Gliozzi M., Hughes C., Titarchuk L., 2014, MNRAS, 443, 72
  • Jarrett et al. (2000) Jarrett T. H., Chester T., Cutri R., Schneider S., Skrutskie M., Huchra J. P., 2000, AJ, 119, 2498
  • Jedrzejewski & Schechter (1988) Jedrzejewski R., Schechter P. L., 1988, ApJL, 330, L87
  • Kartha et al. (2014) Kartha S. S., Forbes D. A., Spitler L. R., Romanowsky A. J., Arnold J. A., Brodie J. P., 2014, MNRAS, 437, 273
  • Katz & Ricotti (2013) Katz H., Ricotti M., 2013, MNRAS, 432, 3250
  • Kim et al. (2013) Kim H.-S., Yoon S.-J., Sohn S. T., Kim S. C., Kim E., Chung C., Lee S.-Y., Lee Y.-W., 2013, ApJ, 763, 40
  • Krajnović et al. (2011) Krajnović D. et al., 2011, MNRAS, 414, 2923
  • Kron (1980) Kron R. G., 1980, ApJS, 43, 305
  • Kundu & Whitmore (2001a) Kundu A., Whitmore B. C., 2001a, AJ, 121, 2950
  • Kundu & Whitmore (2001b) Kundu A., Whitmore B. C., 2001b, AJ, 122, 1251
  • Larsen et al. (2001) Larsen S. S., Brodie J. P., Huchra J. P., Forbes D. A., Grillmair C. J., 2001, AJ, 121, 2974
  • Lauer et al. (2005) Lauer T. R. et al., 2005, AJ, 129, 2138
  • Li & Gnedin (2014) Li H., Gnedin O. Y., 2014, ApJ, 796, 10
  • Liu et al. (2011) Liu C., Peng E. W., Jordán A., Ferrarese L., Blakeslee J. P., Côté P., Mei S., 2011, ApJ, 728, 116
  • Maraston (2005) Maraston C., 2005, MNRAS, 362, 799
  • McDermid et al. (2015) McDermid R. M. et al., 2015, MNRAS, 448, 3484
  • McLaughlin et al. (1994) McLaughlin D. E., Harris W. E., Hanes D. A., 1994, ApJ, 422, 486
  • Miyazaki et al. (2002) Miyazaki S. et al., 2002, PASJ, 54, 833
  • Mulchaey et al. (2003) Mulchaey J. S., Davis D. S., Mushotzky R. F., Burstein D., 2003, ApJS, 145, 39
  • Muratov & Gnedin (2010) Muratov A. L., Gnedin O. Y., 2010, ApJ, 718, 1266
  • Naab et al. (2014) Naab T. et al., 2014, MNRAS, 444, 3357
  • Oser et al. (2010) Oser L., Ostriker J. P., Naab T., Johansson P. H., Burkert A., 2010, ApJ, 725, 2312
  • Ouchi et al. (2004) Ouchi M. et al., 2004, ApJ, 611, 660
  • Park & Lee (2013) Park H. S., Lee M. G., 2013, ApJL, 773, L27
  • Pastorello et al. (2015) Pastorello N. et al., 2015, MNRAS, 451, 2625
  • Paturel et al. (2003) Paturel G., Petit C., Prugniel P., Theureau G., Rousseau J., Brouty M., Dubois P., Cambrésy L., 2003, A&A, 412, 45
  • Peacock et al. (2015) Peacock M. B., Strader J., Romanowsky A. J., Brodie J. P., 2015, ApJ, 800, 13
  • Peng et al. (2006) Peng E. W., Jordán A., Côté P., Blakeslee J. P., Ferrarese L., Mei S., West M. J., Merritt D., Milosavljević M., Tonry J. L., 2006, ApJ, 639, 95
  • Pipino et al. (2010) Pipino A., D’Ercole A., Chiappini C., Matteucci F., 2010, MNRAS, 407, 1347
  • Pota et al. (2013) Pota V. et al., 2013, MNRAS, 428, 389
  • Rhode et al. (2010) Rhode K. L., Windschitl J. L., Young M. D., 2010, AJ, 140, 430
  • Rickes et al. (2009) Rickes M. G., Pastoriza M. G., Bonatto C., 2009, A&A, 505, 73
  • Schlegel et al. (1998) Schlegel D. J., Finkbeiner D. P., Davis M., 1998, ApJ, 500, 525
  • Spitler (2010) Spitler L. R., 2010, MNRAS, 406, 1125
  • Spitler et al. (2008) Spitler L. R., Forbes D. A., Strader J., Brodie J. P., Gallagher J. S., 2008, MNRAS, 385, 361
  • Spitler et al. (2006) Spitler L. R., Larsen S. S., Strader J., Brodie J. P., Forbes D. A., Beasley M. A., 2006, AJ, 132, 1593
  • Stoughton et al. (2002) Stoughton C. et al., 2002, AJ, 123, 485
  • Strader et al. (2005) Strader J., Brodie J. P., Cenarro A. J., Beasley M. A., Forbes D. A., 2005, AJ, 130, 1315
  • Strader et al. (2004) Strader J., Brodie J. P., Forbes D. A., 2004, AJ, 127, 3431
  • Strader et al. (2006) Strader J., Brodie J. P., Spitler L., Beasley M. A., 2006, AJ, 132, 2333
  • Strader et al. (2012) Strader J., Fabbiano G., Luo B., Kim D.-W., Brodie J. P., Fragos T., Gallagher J. S., Kalogera V., King A., Zezas A., 2012, ApJ, 760, 87
  • Strader et al. (2011) Strader J., Romanowsky A. J., Brodie J. P., Spitler L. R., Beasley M. A., Arnold J. A., Tamura N., Sharples R. M., Arimoto N., 2011, ApJS, 197, 33
  • Tempel et al. (2015) Tempel E., Guo Q., Kipper R., Libeskind N. I., 2015, MNRAS, 450, 2727
  • Terlevich & Forbes (2002) Terlevich A. I., Forbes D. A., 2002, MNRAS, 330, 547
  • Tonini (2013) Tonini C., 2013, ApJ, 762, 39
  • Tortora et al. (2010) Tortora C., Napolitano N. R., Cardone V. F., Capaccioli M., Jetzer P., Molinaro R., 2010, MNRAS, 407, 144
  • Trenti et al. (2015) Trenti M., Padoan P., Jimenez R., 2015, ApJL, 808, L35
  • Tully (1988) Tully R. B., 1988, Nearby galaxies catalog, Cambridge and New York, Cambridge University Press, 221 p
  • Usher et al. (2012) Usher C., Forbes D. A., Brodie J. P., Foster C., Spitler L. R., Arnold J. A., Romanowsky A. J., Strader J., Pota V., 2012, MNRAS, 426, 1475
  • Usher et al. (2013) Usher C., Forbes D. A., Spitler L. R., Brodie J. P., Romanowsky A. J., Strader J., Woodley K. A., 2013, MNRAS, 436, 1172
  • Walker et al. (2006) Walker M. G., Mateo M., Olszewski E. W., Pal J. K., Sen B., Woodroofe M., 2006, ApJL, 642, L41
  • Wang et al. (2013) Wang Q., Peng E. W., Blakeslee J. P., Côté P., Ferrarese L., Jordán A., Mei S., West M. J., 2013, ApJ, 769, 145
  • Zepf & Ashman (1993) Zepf S. E., Ashman K. M., 1993, MNRAS, 264, 611
Comments 0
Request Comment
You are adding the first comment!
How to quickly get a good reply:
  • Give credit where it’s due by listing out the positive aspects of a paper before getting into which changes should be made.
  • Be specific in your critique, and provide supporting evidence with appropriate references to substantiate general statements.
  • Your comment should inspire ideas to flow and help the author improves the paper.

The better we are at sharing our knowledge with each other, the faster we move forward.
""
The feedback must be of minimum 40 characters and the title a minimum of 5 characters
   
Add comment
Cancel
Loading ...
78907
This is a comment super asjknd jkasnjk adsnkj
Upvote
Downvote
""
The feedback must be of minumum 40 characters
The feedback must be of minumum 40 characters
Submit
Cancel

You are asking your first question!
How to quickly get a good answer:
  • Keep your question short and to the point
  • Check for grammar or spelling errors.
  • Phrase it like a question
Test
Test description