EDP Sciences logo
Subscriber Authentication Point
Search
Menu
Home All issues Volume 587 (March 2016) A&A, 587 (2016) A138 Full HTML
Free Access
Issue
A&A
Volume 587, March 2016
Article Number A138
Number of page(s) 18
Section Extragalactic astronomy
DOI https://doi.org/10.1051/0004-6361/201526753
Published online 03 March 2016

© ESO, 2016

1. Introduction

Numerous studies have shown that the mass of the supermassive black hole (BH), which is believed to be hosted in the centre of every galaxy, correlates well with several properties of the host galaxy or at least its central spheroidal component (e.g. Kormendy & Richstone 1995; Magorrian et al. 1998; Gebhardt et al. 2000; Ferrarese & Merritt 2000; Marconi & Hunt 2003; Häring & Rix 2004; Graham & Driver 2007; Graham 2012; Kormendy & Ho 2013; Savorgnan et al. 2013; Läsker et al. 2014). The role of active galactic nuclei (AGN) in this context is still unclear.

AGN feedback has been suggested as a possible regulating mechanism between BHs and their host galaxies, which could contribute to the formation of the local scaling relations through the quenching of star formation. Studies that help to understand the interplay of star formation, black hole accretion, and outflows in AGN host galaxies are therefore of high importance.

Over the past years, several studies using integral-field spectroscopy (IFS) in the near-infrared (NIR) have reported that many active galaxies show recent or on-going star formation in the central kiloparsec (e.g. Böker et al. 2008; Riffel et al. 2009b; Bedregal et al. 2009; Valencia-S. et al. 2012a; Falcón-Barroso et al. 2014; Busch et al. 2015; Smajić et al. 2015). IFS has also allowed spatially resolving inflows and outflows in many galaxies (e.g. Riffel et al. 2008; Storchi-Bergmann et al. 2010; Müller-Sánchez et al. 2011; Davies et al. 2014; Riffel et al. 2015; Diniz et al. 2015).

From a sample of ~10 galaxies, the AGNIFS team (Storchi-Bergmann, Riffel, and collaborators) found that the molecular gas is mostly situated in a disk-like structure and often shows inflow patterns (“feeding”), while the ionised gas is often more perturbed and more affected by outflows from the AGN (“feedback”).

It is still debated whether outflows have positive or negative influence on star formation. Most probably, outflows can be responsible for both initiating and quenching star formation (e.g. Nesvadba et al. 2010; Maiolino et al. 2012; Silk 2013; Rashed et al. 2013; Cresci et al. 2015a,b, and references therein). Furthermore, it has been shown that the presence of a powerful AGN can significantly boost the outflow rate (Cicone et al. 2014).

Most of these studies are based on nearby Seyfert galaxies or low-luminosity AGN (redshift z ≲ 0.01). However, these objects may not be representative of the higher redshift AGN population, since the AGN power (and star formation rate) is expected to increase with redshift. With today’s instrumentation, it is not possible to resolve the centres of AGNs on sub-kpc scale at the peak of AGN and star formation activity (z ~ 2; e.g. Aird et al. 2015).

The low-luminosity type-1 QSO (LLQSO) sample was selected to fill the gap between the local Seyfert population and more powerful QSOs at higher cosmological distances. It is a subsample of the Hamburg/ESO survey (HES; Wisotzki et al. 2000) and contains only the closest 99 objects with z ≤ 0.06, which are still close enough to achieve a sub-kpc spatial resolution.

The bolometric luminosity of LLQSOs is systematically higher by at least a magnitude than the luminosity of other AGN samples at lower redshift, which compensates for the lower physical resolution that is due to the higher redshift. Furthermore, the Eddington ratio that traces the BH accretion rate is higher by up to several magnitudes (Fig. 1). Therefore, the impact of the AGN on the surrounding interstellar medium (“feedback”) is expected to be much stronger than in local low-luminosity AGN. This makes LLQSOs the ideal targets for studying the interplay between the central engine and the host galaxy (Moser et al. 2012).

A more detailed description about the sample can be found in the previous NIR imaging study (Busch et al. 2014). Several sources have already been observed in molecular gas (Bertram et al. 2007), H i (König et al. 2009), and H2O-maser emission (König et al. 2012). A previous NIR spectroscopy study was conducted by Fischer et al. (2006), while single objects from the sample have been studied in Krips et al. (2007), Scharwächter et al. (2011), Busch et al. (2015), and Moser et al. (2016). A large portion of the LLQSOs are included in the new Close AGN Reference Survey1 (CARS, B. Husemann, in prep.).

thumbnail Fig. 1

Left: bolometric luminosity as a function of the linear scale pc/arcsec. Right: histogram of the Eddington ratios of the LLQSO sample in comparison to the recent study of Müller-Sánchez et al. (2013), the NUGA sample (García-Burillo et al. 2003), and the work of the AGNIFS group (Storchi-Bergmann, Riffel, et al.).

In this paper, we present and analyse NIR spectroscopic observations of eleven sources from the LLQSO sample for which imaging data were discussed in Busch et al. (2014). LLQSOs were found to lie below the MBH − Lbulge relations of inactive galaxies, as a possible consequence of enhanced star formation (overluminous bulges) or undermassive BHs (deviations of active galaxies from the MBH − Lbulge relations have been found in the optical by Nelson et al. 2004; Kim et al. 2008; Bennert et al. 2011; Urrutia et al. 2012). Recent NIR integral-field spectroscopy of the LLQSO HE 1029–1831 shows that, at least in this particular case, the deviation is caused by an overluminosity of the bulge that is due to young stellar populations (Busch et al. 2015).

Near-infrared spectroscopy is a useful tool to assess extinction, the dominating stellar populations, excitation mechanisms, and the contributions of stellar and non-stellar components to the emission of the galaxy (e.g. Zuther et al. 2007; Smajić et al. 2012; Valencia-S. et al. 2012a; Smajić et al. 2014; Busch et al. 2015; Fazeli et al., in prep.). The NIR H + K-band contains several diagnostic lines: Hydrogen recombination lines (Paα and Brγ) can be excited by the AGN (in the broad- and narrow-line region), but are also tracers of young star formation. Shocks can be traced by molecular hydrogen (H2) rotational-vibrational lines and the forbidden [Fe ii] line. Stellar CO absorption bands (12CO(6–3) in the H-band and 12CO(2–0) in the K-band) give constraints on the stellar population, while the forbidden [Si vi] line is a clear AGN tracer (e.g. Riffel et al. 2006; Mason et al. 2015).

Molecular hydrogen H2 emission is either of thermal or non-thermal origin. The main thermal excitation mechanisms are shocks, UV radiation in dense clouds, or X-rays (e.g. Brand et al. 1989; Sternberg & Dalgarno 1989; Draine & Woods 1990), while UV-pumping (Black & van Dishoeck 1987) is a possible non-thermal excitation mechanism. The H2 line ratio 2–1 S(1)/1–0 S(1) can be used to distinguish between thermal and non-thermal excitation, while the [Fe ii]/Brγ ratio can be used to estimate the importance of X-ray excitation (e.g. Colina 1993; Alonso-Herrero et al. 1997; Reunanen et al. 2002, 2003).

A number of studies have focused on developing a diagnostic diagram in the NIR (comparable to the BPT-diagram in the optical) that distinguishes between excitation from star formation, AGN, and shocks. For this, line ratios between shock tracers (H2 or [Fe ii]) and star formation tracers (Paα, Paβ, Brγ) are used (e.g. Larkin et al. 1998; Rodríguez-Ardila et al. 2004, 2005; Riffel et al. 2013). Using integral-field spectroscopy, Colina et al. (2015) were able to spatially separate line-emitting regions with different ionization mechanisms (AGN/young stars/supernova dominated) and show that they occupy different regions in the log ([ Fe ii/ Brγ) − log (H2/ Brγ) diagram. The observed line ratios were well reproduced with photoionization models by Dors et al. (2012). Furthermore, these authors found that X-ray emission from the AGN can be considered the most important excitation mechanisms of these lines.

Here, we use H + K long-slit spectra to study extinction, star formation activity, and black-hole masses via NIR diagnostic lines in a sample of 11 LLQSOs. This paper is structured as follows: in Sect. 2 we describe the observations as well as the data reduction and calibration. In Sect. 3.1 we explain our continuum subtraction and describe the emission line fits that are based on the continuum-subtracted spectra from Sect. 3.1. Excitation mechanism are discussed in Sect. 4.1. In Sect. 4.2 we estimate black hole masses and discuss the positions of the galaxies in the black hole mass – bulge luminosity relation. In Sect. 4.3 we discuss the far-infrared (FIR) properties, and we finally draw conclusions in Sect. 5. Individual objects are discussed in Appendix A.

thumbnail Fig. 2

SofI H-band images of the eleven galaxies with slit positions indicated. The slit width is 1′′. North is up, east is left.

Table 1

Source information: coordinates, morphological classification, redshift, and observation parameters.

2. Observation, reduction, and calibration

Eleven galaxies from the LLQSO sample have been observed in seeing-limited mode with the Son of ISAAC (SofI) infrared spectrograph and imaging camera on the New Technology Telescope (NTT, ESO, Chile) in September 2009. The 1024 × 1024 Hawaii HgCdTe array provides a pixel scale of 0.288″ / pixel with a field of view of 4.9 × 4.9 arcmin2. NIR imaging data are presented in Busch et al. (2014). The data analysed in this work consist of H + K-band low-resolution long-slit spectroscopy with a slit width of 1″, resulting in a spectral resolution of about R = λ/ Δλ = 600.

For host galaxies showing prominent structures such as bars, the slit was preferably aligned along those structures. In some cases, the slit position was chosen to include neighbouring objects. The positions of the slit superimposed on the H-band images from Busch et al. (2014) are shown in Fig. 2. Integration times, seeing conditions, and the position angles of the slit are presented in Table 1. The integration time was typically two hours, and the seeing ranged from \hbox{$0\farcs9$} to \hbox{$1\farcs7$}.

The spectra were taken with a nodding technique. Sky-subtraction was made by subtracting consecutive frames from each other. Flat-fielding, and the correction for tilt and curvature were made by Iraf/Pyraf standard procedures. Subsequently, the frames were shifted on top of each other and a median frame was calculated.

Telluric correction was performed with G2V-stars that were observed before and after the science objects at similar airmass (Table 1). The G2V class was chosen since they have the same spectral type as the Sun, whose spectral lines are well known. For the telluric correction, the science spectra were divided by the telluric spectra (taking into account the different exposure times) and were then multiplied by a solar reference spectrum (Maiolino et al. 1996) to correct for the black body shape and lines inserted by the telluric spectrum. We shifted the spectra manually on a subpixel-scale to match the absorption features and optimise the telluric correction. Since the solar reference spectrum was only available in the H- and K-band, we interpolated the region in between with a black body function with temperature T = 5800 K. This means that we cannot correct for absorption lines of the G2V-type spectrum in the band gap. Therefore, our telluric correction leaves residuals, especially in the band gap between H- and K-band and in the region between 1.8–2μm, which has particularly low transmission. Line detections and measurements in this region have therefore to be taken with caution (however, the Paα-line that is located in the band gap is quite strong and therefore well detected in most galaxies).

Wavelength calibration was made using the lines of a xenon lamp observed every night. Flux calibration was made by scaling the solar reference spectrum to the 2MASS flux (Skrutskie et al. 2006) of the telluric star. In three nights, two or three standard stars were observed during the same night. To estimate the reliability of the flux calibration, we calibrated the standard stars with each other. The resulting calibrations varied by a factor of 10–35%, most likely caused by seeing variations during the night (for a discussion of slit-loss corrections see Rashed et al. 2015).

3. Analysis and results

In Fig. B.1 we compare the central spectra that are extracted from an aperture with a diameter corresponding to the seeing FWHM (see Table 1) to off-nuclear spectra that are extracted using the same aperture size for regions centred at 1.5 × FWHM left and right from the nucleus.

As expected, the continuum shapes of the off-nuclear spectra are bluer than those of the nuclear spectra, which are much more affected by the hot dust emission from the obscuring torus. However, the difference in continuum shape between the eleven galaxies is much higher than the difference in continuum shape between different apertures. This indicates different excitation mechanisms and/or different impact of the central AGN emission on the galaxies’ spectra, which we analyse below.

3.1. Continuum subtraction

The continuum emission of active galaxies is commonly fitted by a multicomponent model, consisting of (1) an underlying power law; (2) thermal emission from hot dust; and (3) star light from the host galaxy. As pointed out in Barvainis (1987), the prominent bump in the NIR (peaking in the K-band at ~2μm) can be produced by hot dust close to its sublimation temperature (T ~ 1500 K) that is predicted from the Unified Model (Antonucci 1993; Urry & Padovani 1995). The power-law emission is believed to originate from the accretion disk (e.g. Malkan & Sargent 1982; Malkan 1983). While it is prominent in the optical/UV, it becomes negligible in the NIR for Seyfert-2s (Ramos Almeida et al. 2009). However, in type-1 sources there might still be a significant contribution (e.g. Riffel et al. 2009a; Landt et al. 2011). Glikman et al. (2006) construct a spectral template for quasars from observations of 27 sources. In this template, the continuum emission in the NIR is best fitted by a combination of a power law (fν ∝ να with power-law index α = −0.92) and a black body with temperature T = 1260 K.

In the following, we fit the continuum shape, using the method described in Smajić et al. (2012). The continuum-subtracted spectra are then used for emission line fits. Furthermore, the different fractions of the fitted functions indicate which processes contribute to the emission of the galaxies.

In the continuum fit, we include three components: hot dust, star light, and a power law. Additionally, we consider extinction (Cardelli et al. 1989) τ(λ,AV)=−AV1.086 [0.574λ1.61−0.527λ1.61RV],\begin{eqnarray} \tau (\lambda,A_V) = - \frac{A_V}{1.086} \, \left[ \frac{0.574}{\lambda^{1.61}} - \frac{0.527}{\lambda^{1.61} R_V} \right],\label{eq:extinction} \end{eqnarray}(1)with the standard value RV = 3.1 and λ in μm. The hot dust contribution is modelled as black body radiation with the Planck function BB(sdust,λ,T)=sdust 2hc2λ5 1exp(hcλkT)−1\begin{eqnarray} \textrm{BB}(s_\mathrm{dust},\lambda,T) = s_\textrm{dust} \, \frac{2hc^2}{\lambda^5} \, \frac{1}{\exp \left( \frac{hc}{\lambda k T} \right) -1} \label{eq:blackbody} \end{eqnarray}(2)with a scale factor sdust. The contribution of the accretion disk is modelled by a power law PL(sAGN,λ,α)=sAGN λ−α−2,\begin{eqnarray} \textrm{PL}(s_\mathrm{AGN},\lambda,\alpha) = s_\textrm{AGN} \, \lambda^{-\alpha - 2} \label{eq:powerlaw} , \end{eqnarray}(3)with the power-law index α and the scale factor sAGN. We fixed the power law index to the value from the quasar template of Glikman et al. (2006) (α = −0.92). To account for the stellar contribution, we used stellar templates from the NASA Infrared Telescope Facility (IRTF) spectral library for cool stars (Rayner et al. 2009). The library offers spectra of 210 cool stars (mainly F, G, K, M spectral type) at an resolving power of R ≈ 2000. The spectra are not normalised, that is, the shape of the continuum is preserved.

The spectra had to be convolved with a Gaussian function to account for the lower resolution of our spectra and intrinsic effects such as broadening due to velocity dispersion or Doppler displacement: Star(sstar,σ,Δλ)=Spec∗1σ2πexp(−(λ−Δλ)22σ2)·\begin{eqnarray} \textrm{Star}(s_\textrm{star},\sigma, \Delta \lambda)= \textrm{Spec} * \frac{1}{\sigma \sqrt{2\pi}} \exp \left( - \frac{(\lambda- \Delta \lambda)^2}{2\sigma^2} \right)\cdot \label{eq:stellar} \end{eqnarray}(4)In total, we used the following function and fit it to the spectra using a python implementation of the amoeba-routine (downhill simplex method, Press et al. 1992): Cont(AV,sdust,T,sAGN,α,sstar,σ,Δλ)=exp(τ(λ,AV))\begin{eqnarray} &&\textrm{Cont}(A_V,s_\textrm{dust},T,s_\textrm{AGN},\alpha,s_\textrm{star},\sigma,\Delta \lambda) = \exp \left( \tau (\lambda,A_V) \right) \nonumber\\ &&\hspace*{8mm}\times \left[ \textrm{BB}(s_\mathrm{dust},\lambda,T) + \textrm{PL}(s_\mathrm{AGN},\lambda,\alpha) + \textrm{Star}(s_\textrm{star},\sigma, \Delta \lambda) \right]. \label{eq:fitfunction} \end{eqnarray}(5)

The function has six fitting parameters: visual extinction AV, temperature of the dust T, width σ and displacement Δλ of the Gaussian function used to convolve the stellar templates, and the scale factors sdust, sAGN and sstar of the dust, power law, and stellar continuum.

One example of a continuum fit is displayed in Fig. 3. Table 2 shows the results of our continuum fits: the relative contributions of the contributing functions, as well as extinction, and dust temperature. The fits give a first estimate of the contributing components, and moreover, they are used for continuum subtraction before fitting the emission lines. We point out that the fit might not always have a numerically unique solution since dust emission (T ≈ 1200 K) and strong extinction will both produce a red shape, while stellar emission and power law will both produce a blue spectrum. As pointed out by Cid Fernandes et al. (2004) and Riffel et al. (2009a), for example, young reddened starbursts are indistinguishable from a power law. Generally, for a more detailed analysis of the stellar content, a larger set of stellar populations is necessary. Using only one star as template often results in lower values for the stellar velocity dispersion (see e.g. Riffel et al. 2008; Valencia-S. et al. 2012a). However, stellar kinematics or stellar population synthesis is not the goal of this work. The only aim is to subtract the continuum emission and obtain first estimates on the contributions of the different components. Our methods serve this purpose despite the mentioned limitations and degeneracies.

thumbnail Fig. 3

Continuum fit. HE0103–5842 is shown as an example.

In all galaxies (with exception HE0045–2145, see discussion below), we fitted a black body function with temperatures 1000 K ≤ Tdust ≤ 1400 K that are typical for Seyfert-1 galaxies (Riffel et al. 2009a, 2010; Landt et al. 2011, and references therein). In some galaxies, a power-law component was also fitted. All spectra show stellar components, which contribute from 30% up to almost 90%. This shows the diversity of objects in terms of dominance of the nuclear component in the LLQSO sample. We point out that the resulting stellar fractions are consistent with the AGN/host fractions derived from the decomposition of the NIR imaging data (Busch et al. 2014) and with the amount of dilution in the CO band heads (see e.g. Davies et al. 2007; Riffel et al. 2009a) observed in the K-band spectra (i.e. galaxies with dominant stellar component show prominent CO band heads, while the band heads are completely diluted by the underlying black body and/or power law in galaxies that are dominated by the AGN).

Table 2

Results of the continuum fits: fraction of dust component (black body radiation) and its temperature T, fraction of the AGN component (power law), fraction of the stellar component, and the extinction.

3.2. Emission line analysis

The strong Paα hydrogen recombination line (λrest = 1.87561 μm) is located in the band gap between the H and K-band, which is significantly affected by residuals from the telluric absorption. As a result of the redshift of the LLQSO sample (0.02 ≤ z ≤ 0.06), the emission line is shifted towards the K-band in a region where it is still affected by the telluric absorption but is already well detectable. In nine galaxies, we were able to separate the Paα into two components, a broad (with FWHM typically between 2000 and 5000 km s-1) and a narrow component (FWHM below 1000 km s-1).

A second strong hydrogen recombination line located in the K-band is Brγ (λrest = 2.166112 μm). Since the Brγ line is typically weaker than Paα by a factor of more than 10, we constrained the widths of Brγ to the widths resulting from the Paα fits. This reduces the number of fit parameters and makes the fits more reliable.

All emission line fits were performed using the Python-version of Mpfitexpr (Markwardt 2009). We performed a Monte Carlo simulation with 100 iterations to estimate the uncertainties of the fit. In each iteration, we added Gaussian noise to the spectrum. The width of the noise distribution corresponds to the root-mean-square of the residual spectrum that we obtained after subtracting the fitted line from the spectrum. This gives us the uncertainty at the position of the line, which is particularly useful for the Paα emission line, which is located in a very noisy region of the spectrum. In the following, we take the mean of the 100 fits as best fit and the standard deviation as uncertainty. The results of the line fits are presented in Table 3.

Table 3

Hydrogen recombination line fits.

More than half of the galaxies in the sample show significant emission in molecular hydrogen. Particularly the transitions H2(1–0)S(3) (λ1.958 μm), H2(1–0)S(2) (λ2.034 μm), and H2(1–0)S(1) (λ2.122 μm) are detected. This shows that LLQSOs are not only rich in cold molecular gas (see Bertram et al. 2007), but also contain warm, excited molecular gas. Ratios of molecular hydrogen lines can give valuable information on the excitation mechanisms (Davies et al. 2003, 2005). The observations analysed here are too shallow to investigate H2 excitation mechanisms in detail. Nevertheless, the detection of molecular hydrogen in this study helps to select candidates for follow-up studies on the hydrogen excitation mechanisms based on deeper observations. The results of the emission line fits are reported in Table 4.

Most of the galaxies show emission in the forbidden iron transition [Fe ii] at λ1.644 μm. This line can be excited by the AGN, but is also known as shock tracer and particularly used as supernova rate estimator (e.g. Calzetti 1997; Alonso-Herrero et al. 2003). Furthermore, some galaxies, particularly HE2129–3356 and HE2221–0221, show strong emission in the coronal line [Si vi], which is a common AGN tracer.

In their studies of Seyfert galaxies and quasars, Riffel et al. (2006) and Mason et al. (2015) detected the same set of lines. They reported that [Fe ii], H2, and [Si vi] are detected more frequently in nearby AGN than in quasars. Our detection rates in low-luminosity QSOs ([Fe ii]: 7/11, H2(1–0)S(1): 6/11, see Table 4) lie between their detection rates for AGN and quasars, as expected for a “bridge” sample between these two populations.

Table 4

Emission line fluxes of molecular hydrogen lines and of the forbidden line [Fe ii].

4. Discussion

4.1. Excitation mechanisms

In the optical, emission line ratios are commonly used to distinguish between different excitation mechanisms (mainly star formation vs. AGN ionization) making use of different diagnostic diagrams (e.g. Baldwin et al. 1981; Kewley et al. 2001; Kauffmann et al. 2003; Schawinski et al. 2007; Bremer et al. 2013; Vitale et al. 2015).

A diagnostic diagram in the NIR has been suggested by Larkin et al. (1998) and further developed by Rodríguez-Ardila et al. (2004, 2005), Riffel et al. (2013). It uses the line ratios log (H2(1−0)S(1) / Brγ) and log ([Fe ii] λ1.257 μm / Paβ) to distinguish between excitation from pure photoionization and from pure shocks. It shows a transition from starburst galaxies to LINERs, passing AGNs where both excitation mechanisms are of importance (see also models by Dors et al. 2012). We used the conversion factors [Fe ii] λ1.644 μm / [Fe ii] λ1.257 μm = 0.744 (Nussbaumer & Storey 1988) and Paα/ Paβ = 2.05 (Osterbrock & Ferland 2006) to convert the accessible Paα and [Fe ii] λ1.644 μm line fluxes to the needed J-band line fluxes. In contrast to the J-band ratio, the used lines Paα and [Fe ii] λ1.644 μm are separated in wavelength by an amount where reddening effects become significant. The line ratio will change by a factor of 100.4 × [ A( [ Fe ii ] ) − A(Paα) ], which corresponds to 100.0204 × AV for the Calzetti et al. (2000) extinction law (A( [ Fe ii ]) and A(Paα) denote the extinction at the wavelength of [Fe ii] and Paα, respectively, while AV denotes visual extinction). This means that for a typical extinction of AV = 2, we underestimate the line ratio by 10% and by 60% for an extreme extinction of AV = 10. Because we were unable to reliably determine extinction from our data, we include this uncertainty in the error bars.

Nine out of eleven observed galaxies have the required line detections to be placed in the diagnostic diagram at least with upper limits. All of these galaxies are consistent with AGN photoionization. This confirms that LLQSOs already show a significant to dominating contribution of non-stellar emission from the central AGN. However, some galaxies are shifted towards the region where starburst-dominated galaxies are located. This underlines the importance of both ionization mechanisms in the LLQSO sample. The more detailed analysis of individual objects (Appendix A) shows more indications for ongoing star formation in several objects.

With the present data, we can only probe the excitation mechanisms in a single aperture, corresponding to the central ~1 kpc. With follow-up integral-field spectroscopy, we will be able to trace the ionization mechanisms in a spatially resolved way (see pilot study by Busch et al. 2015).

Table 5

BH mass estimates from the broad Paα line compared with A. Schulze’s BH mass estimates from Hα.

4.2. Black hole mass – bulge luminosity relation

We used the broad component of the Paα emission line that we detected in nine out of eleven galaxies to estimate the mass of the central supermassive black hole. According to Kim et al. (2010), MBH=107.16 ± 0.04(LPaα1035W)0.49±0.06(FWHMPaα103kms-1)2M⊙.\begin{eqnarray} M_\textrm{BH} = 10^{7.16\, \pm\, 0.04} \left( \frac{L_{\mathrm{Pa}\alpha}}{10^{35} \mathrm{W}} \right)^{0.49\pm 0.06} \left( \frac{\mathit{FWHM}_{\mathrm{Pa}\alpha}}{10^3\ \mathrm{km}\ \mathrm{s}^{-1}} \right)^2~M_\odot. \label{eq:massbh} \end{eqnarray}(6)The calculated black hole masses are presented in Table 5. We compare them to black hole masses available from Schulze & Wisotzki (2010) and Schulze (priv. comm.). They used optical spectra and computed BH masses using the scaling relation between broad-line region (BLR) size and continuum luminosity by Bentz et al. (2009) and the scale factor f = 3 / 4 from Netzer et al. (1990), which results in MBH=5.025×(L51001037W)0.52(FWHMHβkms-1)2M⊙.\begin{eqnarray} M_\mathrm{BH} = 5.025 \times \left( \frac{L_{5100}}{10^{37}~ \mathrm{W}} \right)^{0.52} \left( \frac{\mathit{FWHM}_{\mathrm{H}\beta}}{\mathrm{km}~\mathrm{s}^{-1}} \right)^2~M_\odot. \end{eqnarray}(7)The mass estimates agree well with each other. The deviations are ≤0.3 dex, which is small given the uncertainties of BH mass estimates in general. The BH mass estimates from Paα in the NIR thus confirm Schulze’s masses from Hβ, which we used in Busch et al. (2014) to test the MBH − Lbulge relation.

Furthermore, we obtained BH masses for four galaxies that we did not have BH masses of before (HE0036–5133, HE2112–5926, HE2211–3903, and HE2221–0221). We used the K-band bulge magnitudes derived in Busch et al. (2014) to plot them in the MBH − Lbulge diagram (Fig. 5). The new data points confirm the results found in the previous studies (Busch et al. 2014, 2015). With the new data we enlarged the sample from 12 to 16 LLQSOs that systematically lie below published MBH − Lbulge relations of inactive galaxies, which makes the findings even more trustworthy.

thumbnail Fig. 4

Diagnostic diagram in the NIR with line ratios log (H2(1−0)S(1) / Brγ) and log ([Fe ii] λ1.257 μm / Paβ) (for details see text). Apertures have a diameter corresponding to 3 × FWHM of the seeing. The positions of nine galaxies in the diagnostic diagram are shown. The lines indicate regions that are typically populated by starburst galaxies, AGNs, and LINERs resp. Dashed lines: Rodríguez-Ardila et al. (2005) and Riffel et al. (2010), dotted lines: Riffel et al. (2013).

thumbnail Fig. 5

Correlation between black hole mass MBH and absolute K-band magnitude MK of the spheroidal component. Bulge magnitudes are taken from Busch et al. (2014). Black hole masses for the red points are adopted from A. Schulze (data points already published in Busch et al. 2014). The green point is taken from Busch et al. (2015). Four data points with black hole masses derived from NIR spectroscopy in this work are added in blue.

Recently, specific growth rates of the BH and the stellar host have been used to explore the time evolution (or “flow patterns”) in the MBH − M∗ relation. AGN that are offset from the relation mostly have evolutionary vectors that are anti-correlated with their positions. This means that they are moving back towards the relation (Merloni et al. 2010; Sun et al. 2015). As discussed in Busch et al. (2014), possible explanations for a deviation of active galaxies from a near-linear MBH − Lbulge relation are (a) bulges that contain young stellar populations (as opposed to the usual picture that bulges are mainly consisting of old stars) and are therefore brighter at given mass; or (b) undermassive black holes that are in a growing phase. Both scenarios fit into a framework of bulge and black hole coevolution that is not solely based on a “classical” merging of galaxies, but includes interaction of the black hole and the surrounding host galaxy via AGN feeding and feedback. The offset of the LLQSOs in the BH-bulge relation might thus shed light on the sequence of star forming and BH fuelling/feedback phases, which is an important component for understanding the BH-bulge coevolution.

Table 6

IRAS fluxes and derived quantities: IR and FIR luminosity, FIR star formation rate.

According to the NIR diagnostic diagram (Sect. 4.1), the analysed galaxies are dominated by the AGN. However, some objects are shifted towards the location of sources dominated by star formation. Furthermore, in the discussion of individual objects (see Appendix A) we point out that many objects show indications for ongoing star formation. This could indicate overluminous bulges associated with lower mass-to-light ratios.

As pointed out in Bertram et al. (2007), LLQSOs are rich in cold molecular gas. Here, we find more than half of our objects to show molecular hydrogen emission in the NIR, which indicates that part of the molecular gas content is also excited. Furthermore, several of the objects can be classified as (ultra-)luminous infrared galaxies ((U)LIRGs). While the presence of molecular gas and star formation are related (e.g. see the famous Kennicutt-Schmidt law; Kennicutt 1998) and molecular gas is therefore a prerequisite for star formation, the role of molecular gas in the context of AGN fuelling is not fully understood. On the one hand, Riffel et al. (2015, and references therein) showed that H2 is typically located in a disk in the plane of the galaxy and often shows inflows to the centre (is a tracer of AGN fuelling). On the other hand, cold molecular gas can also be found in outflows (feedback; García-Burillo et al. 2014).

Recently, an alternative explanation for the offset in the MBH − Lbulge relation has been proposed (Graham 2012; Graham & Scott 2013; Scott et al. 2013; Graham & Scott 2015). It was suggested that the relation between BH mass and stellar mass (and luminosity) of the host spheroid is a “broken” relation, with a near-linear MBH − Mbulge, ∗ relation only for high BH masses (MBH ≳ 108M⊙). Galaxies with lower BH masses are found to follow a different, steeper relation, which indicates that the black hole is growing more rapidly than the surrounding spheroid. Graham (2016) stated that the LLQSOs presented in Busch et al. (2014) follow the relation of lower-mass galaxies. We see that all eleven galaxies (plus the five galaxies added since then) are systematically shifted below the relation, however. This indicates that LLQSOs are also offset from the new relation. However, reliable mass-to-light ratios are indispensable to decide on the validity of the offset of active galaxies. As discussed above, a lower mass-to-light ratio would be expected in the case of enhanced nuclear star formation.

By using integral-field spectroscopy, Busch et al. (2015) showed that in the case of HE 1029–1831 circumnuclear star formation shifts the LLQSO away from the MBH − Lbulge relation of inactive galaxies. Only by using spatially resolved data at highest angular resolution (using adaptive-optics-assisted integral-field spectroscopy with e.g. SINFONI at the Very Large Telescope) as in the mentioned pilot study, central AGN emission and circumnuclear star formation can be reliably separated.

4.3. Far-infrared properties

Seven galaxies are listed in the IRAS Faint Source Catalog (Moshir 1990). We present the fluxes measured at 12 μm, 25 μm, 60 μm, and 100 μm in Table 6. From these, infrared luminosities LIR(8−1000 μm)[W]=4π(DL[Mpc])2×1.72×1031  ×(13.48F12 μm+5.16F25 μm+2.58F60 μm+F100 μm)\begin{eqnarray} L_\mathrm{IR}(8{-}1000\mm)[\mathrm{W}] &=& 4\pi \left( D_{\rm L}[\mathrm{Mpc}] \right)^2 \times 1.72 \times 10^{31} \nonumber\\ &&\quad\quad\times \left(13.48\ F_{12\mm} + 5.16\ F_{25\mm} + 2.58\ F_{60\mm} + F_{100\mm} \right) \end{eqnarray}(8)and FIR luminosities LFIR(40−500 μm)[W]=4π(DL[Mpc])2×1.2×1031(2.58F60 μm+F100 μm)\begin{eqnarray} L_\mathrm{FIR}(40{-}500\mm)[\mathrm{W}] = 4\pi \left( D_{\rm L}[\mathrm{Mpc}] \right)^2 \times 1.2 \times 10^{31} \left( 2.58\ F_{60~\mm} + F_{100~\mm} \right) \end{eqnarray}(9)can be calculated (Sanders & Mirabel 1996). With the calibration of Panuzzo et al. (2003), we can further calculate FIR-star formation rates: SFR(FIR)M⊙yr-1=LFIR1.134×1036W·\begin{eqnarray} \frac{\mathrm{SFR (FIR)}}{M_\odot \ \mathrm{yr}^{-1}} = \frac{L_\mathrm{FIR}}{1.134\times 10^{36}~\mathrm{W}}\cdot \label{eq:sfrfir} \end{eqnarray}(10)We recall that the FIR luminosity LFIR traces the star formation rate on timescales of 100 Myr and can therefore not directly be compared to star formation rates from the often-used estimators Hα and Brγ, which trace recent star formation. Furthermore, the FIR luminosity LFIR, particularly if measured in galaxy-wide apertures, can be heavily affected by AGN emission and should therefore better be considered as an upper limit.

In an attempt to constrain the contribution of the AGN to the FIR luminosity, we placed the galaxies in the FIR colour–colour diagrams, taken from Kewley et al. (2000) (see Fig. 6). In these, the typical area of starburst galaxies is indicated by a dashed line. Furthermore, a reddening line is indicated by a solid line. The mixing line corresponds to the mixture of a typical unreddened Seyfert-1 spectrum with a “warm” starburst. Although the diagrams show an ambiguity between the mixing of AGN and starburst and reddening, we can make the following statements: HE0045 is completely dominated by the starburst, which is fully consistent with the findings discussed in the notes on the individual object in Appendix A. All other galaxies have significant contributions from the AGN. For HE0103, HE0119, and HE2112, this contribution is lower than 50%, while it is higher than 50% for HE0323 and HE2211. HE2221 is completely dominated by the AGN, which is consistent with the finding in the imaging study (Busch et al. 2014), where it is the galaxy with the highest AGN fraction in the sample of 20 galaxies.

thumbnail Fig. 6

Far-infrared colour–colour diagrams, modified from Kewley et al. (2000). Top: log (f60 μm/f25 μm) vs. log (f25 μm/f12 μm) diagram. Bottom: log (f60 μm/f12 μm) vs. log (f100 μm/f25 μm) diagram. In both plots, the solid line is the reddening line calculated by Dopita et al. (1998). The straight dashed line marks the typical area of starbursts. The “extreme mixing line” corresponds to the mixture of a typical Seyfert-1 spectrum and a starburst component (with denoted percentage). The open symbols are Seyfert-1, starburst, and Seyfert-2 galaxies with (HBLR) and without (Sy2) broad-line components observed in polarised light, taken from the sample of Rush et al. (1993). The positions of the analysed galaxies are indicated by the coloured data points.

5. Conclusions

We analysed NIR H + K-band long-slit spectra of eleven galaxies from the low-luminosity type-1 QSO sample. Low-resolution spectroscopy provides an insight into the gas reservoirs and possible excitation mechanisms. Our main results are the following:

  • 1.

    All galaxies show the hydrogen recombination line Paα, and most galaxies also show Brγ. In nine out of eleven galaxies, we detected broad components as expected for type-1 AGN. In two galaxies, only narrow components are visible. One of these, HE0045–2145, is clearly a misclassification and rather a starburst galaxy than an AGN. Therefore, it has to be removed from the LLQSO sample.

  • 2.

    From the broad components of the Paα emission line, we estimated black hole masses. For those galaxies with previous black hole mass estimates, the masses agree well with our new masses. We added four more data points in the black hole mass – bulge luminosity diagram that we discussed in Busch et al. (2014), supporting the finding that LLQSOs do not follow the black hole mass – bulge luminosity relations of inactive galaxies. The nature of this offset is essential for understanding the BH – host galaxy evolution.

  • 3.

    From continuum fits, we derived estimates for the contributions of stars, hot dust (from obscuring torus), and power law. The stellar component is significant in all galaxies, ranging from ~30% (AGN dominated) to ~90% (dominated by stellar component). Dust temperatures are in the range of 1000–1400 K, which is typical for type-1 AGN.

  • 4.

    More than half of the galaxies show strong molecular hydrogen lines, which is indicative for a large reservoir of warm, excited molecular gas. The detection of molecular hydrogen in this study motivates deeper follow-up observations to study the H2 excitation mechanisms in LLQSOs in more detail.

  • 5.

    We analysed the gas excitation mechanisms in the galaxies using a NIR diagnostic diagram (log (H2(1−0)S(1) / Brγ) vs. log ([Fe ii] λ1.257 μm / Paβ)). All galaxies fall in a region populated by AGN. However, several tend towards the region of star-forming galaxies, emphasising the relevance of both star formation and AGN in LLQSOs.

To conclude, the spectra we analysed show the importance of both non-stellar continuum emission and stellar radiation in the LLQSO sample. No clear trend can be seen from our data, however, which means that some galaxies are clearly dominated by stellar radiation, others by non-stellar continuum emission, and yet others are a mixture. The diversity of radiation mechanisms confirms the theory that the LLQSO sample constitutes a transition between the clearly AGN-dominated QSOs at higher redshift and the probably mainly secularly evolving galaxies with – if at all – only very weak nuclear activity.

The analysed sources are all offset from the MBH − Lbulge relation. At the same time, they show signs for ongoing or recent star formation, which may indicate overluminous bulges due to lower mass-to-light ratios.

Additional multiwavelength data and spatially resolved integral-field spectroscopy will be analysed to further investigate the interplay between AGN activity and star formation in the observed LLQSOs.

2

Assuming a case B recombination scenario, and the low-density limit with typical temperatures of T = 10 000 K, we expect a line ratio of about Paα/Brγ = 12.5 (Osterbrock & Ferland 2006). Furthermore, assuming a dust-screen-model fobs(λ) = fintr(λ) × 10− 0.4Aλ and the mean RV-dependent extinction law by Cardelli et al. (1989)⟨ A(λ) /A(V) ⟩ = a(λ) + b(λ) /RV with RV = 3.1, a(λ) = 0.574λ-1.61 and b(λ) = −0.527λ-1.61, by using hydrogen recombination lines, we can estimate the extinction AV=−2.5log((fPaα′/fBrγ′)/(fPaα/fBrγ))a(λPaα)+b(λPaα)/RV−a(λBrγ)+b(λBrγ)/RV\hbox{$A_V = \frac{-2.5 \log \left( (f_{\rm Pa\alpha}' / f_{\rm Br\gamma}' )/(f_{\rm Pa\alpha} / f_{Br\gamma} ) \right) }{ a(\lambda_{\rm Pa\alpha}) + b(\lambda_{\rm Pa\alpha})/R_V - a(\lambda_{\rm Br\gamma}) + b(\lambda_{\rm Br\gamma})/R_V}$} with observed fluxes fPaα′,fBrγ′\hbox{$f_{\rm Pa\alpha}',f_{\rm Br\gamma}'$} and the expected line ratio fPaα/fBrγ ≈ 12.5.

Acknowledgments

The authors thank the anonymous referee for comments that helped to improve the paper. This work was supported by the Deutsche Forschungsgemeinschaft (DFG) via SFB 956, subproject A2. G. Busch and N. Fazeli are members of the Bonn-Cologne Graduate School for Physics and Astronomy (BCGS). S. Smajić is member of the International Max Planck Research School for Astronomy and Astrophysics Bonn/Cologne (IMPRS). M. Valencia-S. received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement No. 312789. J. Scharwächter acknowledges the European Research Council for the Advanced Grant Program Number 267399-Momentum. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

References

  1. Aird, J., Coil, A. L., Georgakakis, A., et al. 2015, MNRAS, 451, 1892 [NASA ADS] [CrossRef] [Google Scholar]
  2. Alonso-Herrero, A., Rieke, M. J., Rieke, G. H., & Ruiz, M. 1997, ApJ, 482, 747 [NASA ADS] [CrossRef] [Google Scholar]
  3. Alonso-Herrero, A., Rieke, G. H., Rieke, M. J., & Kelly, D. M. 2003, AJ, 125, 1210 [NASA ADS] [CrossRef] [Google Scholar]
  4. Antonucci, R. 1993, ARA&A, 31, 473 [Google Scholar]
  5. Baldwin, J. A., Phillips, M. M., & Terlevich, R. 1981, PASP, 93, 5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  6. Barvainis, R. 1987, ApJ, 320, 537 [NASA ADS] [CrossRef] [Google Scholar]
  7. Bedregal, A. G., Colina, L., Alonso-Herrero, A., & Arribas, S. 2009, ApJ, 698, 1852 [NASA ADS] [CrossRef] [Google Scholar]
  8. Bennert, V. N., Auger, M. W., Treu, T., Woo, J.-H., & Malkan, M. A. 2011, ApJ, 726, 59 [NASA ADS] [CrossRef] [Google Scholar]
  9. Bentz, M. C., Peterson, B. M., Netzer, H., Pogge, R. W., & Vestergaard, M. 2009, ApJ, 697, 160 [Google Scholar]
  10. Bertram, T., Eckart, A., Fischer, S., et al. 2007, A&A, 470, 571 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  11. Black, J. H., & van Dishoeck, E. F. 1987, ApJ, 322, 412 [NASA ADS] [CrossRef] [Google Scholar]
  12. Böker, T., Falcón-Barroso, J., Schinnerer, E., Knapen, J. H., & Ryder, S. 2008, AJ, 135, 479 [NASA ADS] [CrossRef] [Google Scholar]
  13. Brand, P. W. J. L., Toner, M. P., Geballe, T. R., et al. 1989, MNRAS, 236, 929 [NASA ADS] [CrossRef] [Google Scholar]
  14. Bremer, M., Scharwächter, J., Eckart, A., et al. 2013, A&A, 558, A34 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  15. Busch, G., Zuther, J., Valencia-S., M., Moser, L., & Eckart, A. 2012, in Proc. Nuclei of Seyfert galaxies and QSOs – Central engine & conditions of star formation, PoS(Seyfert 2012)060 [Google Scholar]
  16. Busch, G., Zuther, J., Valencia-S., M., et al. 2014, A&A, 561, A140 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  17. Busch, G., Smajić, S., Scharwächter, J., et al. 2015, A&A, 575, A128 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  18. Calzetti, D. 1997, AJ, 113, 162 [NASA ADS] [CrossRef] [Google Scholar]
  19. Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682 [NASA ADS] [CrossRef] [Google Scholar]
  20. Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245 [NASA ADS] [CrossRef] [Google Scholar]
  21. Cicone, C., Maiolino, R., Sturm, E., et al. 2014, A&A, 562, A21 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  22. Cid Fernandes, R., Gu, Q., Melnick, J., et al. 2004, MNRAS, 355, 273 [NASA ADS] [CrossRef] [Google Scholar]
  23. Colina, L. 1993, ApJ, 411, 565 [NASA ADS] [CrossRef] [Google Scholar]
  24. Colina, L., Piqueras López, J., Arribas, S., et al. 2015, A&A, 578, A48 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  25. Cresci, G., Mainieri, V., Brusa, M., et al. 2015a, ApJ, 799, 82 [Google Scholar]
  26. Cresci, G., Marconi, A., Zibetti, S., et al. 2015b, A&A, 582, A63 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  27. Davies, R. I., Sternberg, A., Lehnert, M., & Tacconi-Garman, L. E. 2003, ApJ, 597, 907 [NASA ADS] [CrossRef] [Google Scholar]
  28. Davies, R. I., Sternberg, A., Lehnert, M. D., & Tacconi-Garman, L. E. 2005, ApJ, 633, 105 [NASA ADS] [CrossRef] [Google Scholar]
  29. Davies, R. I., Müller Sánchez, F., Genzel, R., et al. 2007, ApJ, 671, 1388 [NASA ADS] [CrossRef] [Google Scholar]
  30. Davies, R. I., Maciejewski, W., Hicks, E. K. S., et al. 2014, ApJ, 792, 101 [NASA ADS] [CrossRef] [Google Scholar]
  31. Diniz, M. R., Riffel, R. A., Storchi-Bergmann, T., & Winge, C. 2015, MNRAS, 453, 1727 [NASA ADS] [CrossRef] [Google Scholar]
  32. Dopita, M. A., Heisler, C., Lumsden, S., & Bailey, J. 1998, ApJ, 498, 570 [NASA ADS] [CrossRef] [Google Scholar]
  33. Dors, Jr., O. L., Riffel, R. A., Cardaci, M. V., et al. 2012, MNRAS, 422, 252 [NASA ADS] [CrossRef] [Google Scholar]
  34. Draine, B. T., & Woods, D. T. 1990, ApJ, 363, 464 [NASA ADS] [CrossRef] [Google Scholar]
  35. Eckart, A., van der Werf, P. P., Hofmann, R., & Harris, A. I. 1994, ApJ, 424, 627 [NASA ADS] [CrossRef] [Google Scholar]
  36. Falcón-Barroso, J., Ramos Almeida, C., Böker, T., et al. 2014, MNRAS, 438, 329 [NASA ADS] [CrossRef] [Google Scholar]
  37. Ferrarese, L., & Merritt, D. 2000, ApJ, 539, L9 [NASA ADS] [CrossRef] [Google Scholar]
  38. Fischer, S., Iserlohe, C., Zuther, J., et al. 2006, A&A, 452, 827 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  39. García-Burillo, S., Combes, F., Hunt, L. K., et al. 2003, A&A, 407, 485 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  40. García-Burillo, S., Combes, F., Usero, A., et al. 2014, A&A, 567, A125 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  41. Gebhardt, K., Bender, R., Bower, G., et al. 2000, ApJ, 539, L13 [NASA ADS] [CrossRef] [Google Scholar]
  42. Glikman, E., Helfand, D. J., & White, R. L. 2006, ApJ, 640, 579 [NASA ADS] [CrossRef] [Google Scholar]
  43. Graham, A. W. 2012, ApJ, 746, 113 [NASA ADS] [CrossRef] [Google Scholar]
  44. Graham, A. W. 2016, Galactic Bulges, Astrophys. Space Sci. Lib., 418, 263 [NASA ADS] [CrossRef] [Google Scholar]
  45. Graham, A. W., & Driver, S. P. 2007, ApJ, 655, 77 [NASA ADS] [CrossRef] [Google Scholar]
  46. Graham, A. W., & Scott, N. 2013, ApJ, 764, 151 [NASA ADS] [CrossRef] [Google Scholar]
  47. Graham, A. W., & Scott, N. 2015, ApJ, 798, 54 [NASA ADS] [CrossRef] [Google Scholar]
  48. Grupe, D., Beuerman, K., Mannheim, K., et al. 1995, A&A, 300, L21 [NASA ADS] [Google Scholar]
  49. Häring, N., & Rix, H.-W. 2004, ApJ, 604, L89 [NASA ADS] [CrossRef] [Google Scholar]
  50. Helou, G. 1986, ApJ, 311, L33 [NASA ADS] [CrossRef] [Google Scholar]
  51. Hopkins, P. F., Richards, G. T., & Hernquist, L. 2007, ApJ, 654, 731 [NASA ADS] [CrossRef] [Google Scholar]
  52. Kauffmann, G., Heckman, T. M., Tremonti, C., et al. 2003, MNRAS, 346, 1055 [Google Scholar]
  53. Kennicutt, Jr., R. C. 1998, ApJ, 498, 541 [NASA ADS] [CrossRef] [Google Scholar]
  54. Kewley, L. J., Heisler, C. A., Dopita, M. A., et al. 2000, ApJ, 530, 704 [NASA ADS] [CrossRef] [Google Scholar]
  55. Kewley, L. J., Dopita, M. A., Sutherland, R. S., Heisler, C. A., & Trevena, J. 2001, ApJ, 556, 121 [Google Scholar]
  56. Kim, M., Ho, L. C., Peng, C. Y., et al. 2008, ApJ, 687, 767 [NASA ADS] [CrossRef] [Google Scholar]
  57. Kim, D., Im, M., & Kim, M. 2010, ApJ, 724, 386 [NASA ADS] [CrossRef] [Google Scholar]
  58. König, S., Eckart, A., García-Marín, M., & Huchtmeier, W. K. 2009, A&A, 507, 757 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  59. König, S., Eckart, A., Henkel, C., & García-Marín, M. 2012, MNRAS, 420, 2263 [NASA ADS] [CrossRef] [Google Scholar]
  60. Kormendy, J., & Ho, L. C. 2013, ARA&A, 51, 511 [Google Scholar]
  61. Kormendy, J., & Kennicutt, Jr., R. C. 2004, ARA&A, 42, 603 [NASA ADS] [CrossRef] [Google Scholar]
  62. Kormendy, J., & Richstone, D. 1995, ARA&A, 33, 581 [NASA ADS] [CrossRef] [Google Scholar]
  63. Krips, M., Eckart, A., Neri, R., et al. 2007, A&A, 464, 187 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  64. Landt, H., Elvis, M., Ward, M. J., et al. 2011, MNRAS, 414, 218 [NASA ADS] [CrossRef] [Google Scholar]
  65. Larkin, J. E., Armus, L., Knop, R. A., Soifer, B. T., & Matthews, K. 1998, ApJS, 114, 59 [NASA ADS] [CrossRef] [Google Scholar]
  66. Läsker, R., Ferrarese, L., van de Ven, G., & Shankar, F. 2014, ApJ, 780, 70 [NASA ADS] [CrossRef] [Google Scholar]
  67. Leighly, K. M., Hamann, F., Casebeer, D. A., & Grupe, D. 2009, ApJ, 701, 176 [NASA ADS] [CrossRef] [Google Scholar]
  68. Magorrian, J., Tremaine, S., Richstone, D., et al. 1998, AJ, 115, 2285 [NASA ADS] [CrossRef] [Google Scholar]
  69. Mahony, E. K., Croom, S. M., Boyle, B. J., et al. 2010, MNRAS, 401, 1151 [NASA ADS] [CrossRef] [Google Scholar]
  70. Maiolino, R., Rieke, G. H., & Rieke, M. J. 1996, AJ, 111, 537 [NASA ADS] [CrossRef] [Google Scholar]
  71. Maiolino, R., Gallerani, S., Neri, R., et al. 2012, MNRAS, 425, L66 [NASA ADS] [CrossRef] [Google Scholar]
  72. Malkan, M. A. 1983, ApJ, 268, 582 [NASA ADS] [CrossRef] [Google Scholar]
  73. Malkan, M. A., & Sargent, W. L. W. 1982, ApJ, 254, 22 [NASA ADS] [CrossRef] [Google Scholar]
  74. Marconi, A., & Hunt, L. K. 2003, ApJ, 589, L21 [NASA ADS] [CrossRef] [Google Scholar]
  75. Markwardt, C. B. 2009, in Astronomical Data Analysis Software and Systems XVIII, eds. D. A. Bohlender, D. Durand, & P. Dowler, ASP Conf. Ser., 411, 251 [Google Scholar]
  76. Mason, R. E., Rodríguez-Ardila, A., Martins, L., et al. 2015, ApJS, 217, 13 [NASA ADS] [CrossRef] [Google Scholar]
  77. Mehdipour, M., Branduardi-Raymont, G., & Page, M. J. 2012, A&A, 542, A30 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  78. Merloni, A., Bongiorno, A., Bolzonella, M., et al. 2010, ApJ, 708, 137 [NASA ADS] [CrossRef] [Google Scholar]
  79. Moser, L., Zuther, J., Busch, G., Valencia-S., M., & Eckart, A. 2012, in Proc. Nuclei of Seyfert galaxies and QSOs – Central engine & conditions of star formation, PoS(Seyfert 2012)069 [Google Scholar]
  80. Moser, L., Krips, M., Busch, G., et al. 2016, A&A, 587, A137 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  81. Moshir, M. E. 1990, in IRAS Faint Source Catalogue, version 2.0 [Google Scholar]
  82. Müller-Sánchez, F., Prieto, M. A., Hicks, E. K. S., et al. 2011, ApJ, 739, 69 [NASA ADS] [CrossRef] [Google Scholar]
  83. Müller-Sánchez, F., Prieto, M. A., Mezcua, M., et al. 2013, ApJ, 763, L1 [NASA ADS] [CrossRef] [Google Scholar]
  84. Nelson, C. H., Green, R. F., Bower, G., Gebhardt, K., & Weistrop, D. 2004, ApJ, 615, 652 [NASA ADS] [CrossRef] [Google Scholar]
  85. Nesvadba, N. P. H., Boulanger, F., Salomé, P., et al. 2010, A&A, 521, A65 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  86. Netzer, H., Maoz, D., Laor, A., et al. 1990, ApJ, 353, 108 [NASA ADS] [CrossRef] [Google Scholar]
  87. Nussbaumer, H., & Storey, P. J. 1988, A&A, 193, 327 [NASA ADS] [Google Scholar]
  88. Osterbrock, D. E., & Ferland, G. J. 2006, Astrophysics of gaseous nebulae and active galactic nuclei (University Science Books) [Google Scholar]
  89. Panuzzo, P., Bressan, A., Granato, G. L., Silva, L., & Danese, L. 2003, A&A, 409, 99 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  90. Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. 1992, Numerical recipes in FORTRAN, The art of scientific computing (Cambridge University Press) [Google Scholar]
  91. Ramos Almeida, C., Pérez García, A. M., & Acosta-Pulido, J. A. 2009, ApJ, 694, 1379 [NASA ADS] [CrossRef] [Google Scholar]
  92. Rashed, Y. E., Zuther, J., Eckart, A., et al. 2013, A&A, 558, A5 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  93. Rashed, Y. E., Eckart, A., Valencia-S., M., et al. 2015, MNRAS, 454, 2918 [NASA ADS] [CrossRef] [Google Scholar]
  94. Rayner, J. T., Cushing, M. C., & Vacca, W. D. 2009, ApJS, 185, 289 [NASA ADS] [CrossRef] [Google Scholar]
  95. Reunanen, J., Kotilainen, J. K., & Prieto, M. A. 2002, MNRAS, 331, 154 [NASA ADS] [CrossRef] [Google Scholar]
  96. Reunanen, J., Kotilainen, J. K., & Prieto, M. A. 2003, MNRAS, 343, 192 [NASA ADS] [CrossRef] [Google Scholar]
  97. Riffel, R., Rodríguez-Ardila, A., & Pastoriza, M. G. 2006, A&A, 457, 61 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  98. Riffel, R. A., Storchi-Bergmann, T., Winge, C., et al. 2008, MNRAS, 385, 1129 [NASA ADS] [CrossRef] [Google Scholar]
  99. Riffel, R., Pastoriza, M. G., Rodríguez-Ardila, A., & Bonatto, C. 2009a, MNRAS, 400, 273 [NASA ADS] [CrossRef] [Google Scholar]
  100. Riffel, R. A., Storchi-Bergmann, T., Dors, O. L., & Winge, C. 2009b, MNRAS, 393, 783 [NASA ADS] [CrossRef] [Google Scholar]
  101. Riffel, R. A., Storchi-Bergmann, T., & Nagar, N. M. 2010, MNRAS, 404, 166 [NASA ADS] [Google Scholar]
  102. Riffel, R., Rodríguez-Ardila, A., Aleman, I., et al. 2013, MNRAS, 430, 2002 [NASA ADS] [CrossRef] [Google Scholar]
  103. Riffel, R. A., Storchi-Bergmann, T., & Riffel, R. 2015, MNRAS, 451, 3587 [NASA ADS] [CrossRef] [Google Scholar]
  104. Rodríguez-Ardila, A., Pastoriza, M. G., Viegas, S., Sigut, T. A. A., & Pradhan, A. K. 2004, A&A, 425, 457 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  105. Rodríguez-Ardila, A., Riffel, R., & Pastoriza, M. G. 2005, MNRAS, 364, 1041 [NASA ADS] [CrossRef] [Google Scholar]
  106. Rush, B., Malkan, M. A., & Spinoglio, L. 1993, ApJS, 89, 1 [NASA ADS] [CrossRef] [Google Scholar]
  107. Sanders, D. B., & Mirabel, I. F. 1996, ARA&A, 34, 749 [NASA ADS] [CrossRef] [Google Scholar]
  108. Savorgnan, G., Graham, A. W., Marconi, A., et al. 2013, MNRAS, 434, 387 [NASA ADS] [CrossRef] [Google Scholar]
  109. Scharwächter, J., Dopita, M. A., Zuther, J., et al. 2011, AJ, 142, 43 [NASA ADS] [CrossRef] [Google Scholar]
  110. Schawinski, K., Thomas, D., Sarzi, M., et al. 2007, MNRAS, 382, 1415 [NASA ADS] [CrossRef] [Google Scholar]
  111. Schoenmakers, A. P., de Bruyn, A. G., Röttgering, H. J. A., van der Laan, H., & Kaiser, C. R. 2000, MNRAS, 315, 371 [NASA ADS] [CrossRef] [Google Scholar]
  112. Schulze, A., & Wisotzki, L. 2010, A&A, 516, A87 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  113. Scott, N., Graham, A. W., & Schombert, J. 2013, ApJ, 768, 76 [NASA ADS] [CrossRef] [Google Scholar]
  114. Silk, J. 2013, ApJ, 772, 112 [NASA ADS] [CrossRef] [Google Scholar]
  115. Skrutskie, M. F., Cutri, R. M., Stiening, R., et al. 2006, AJ, 131, 1163 [NASA ADS] [CrossRef] [Google Scholar]
  116. Smajić, S., Fischer, S., Zuther, J., & Eckart, A. 2012, A&A, 544, A105 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  117. Smajić, S., Moser, L., Eckart, A., et al. 2014, A&A, 567, A119 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  118. Smajić, S., Moser, L., Eckart, A., et al. 2015, A&A, 583, A104 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  119. Sternberg, A., & Dalgarno, A. 1989, ApJ, 338, 197 [NASA ADS] [CrossRef] [Google Scholar]
  120. Storchi-Bergmann, T., Lopes, R. D. S., McGregor, P. J., et al. 2010, MNRAS, 402, 819 [NASA ADS] [CrossRef] [Google Scholar]
  121. Sun, M., Trump, J. R., Brandt, W. N., et al. 2015, ApJ, 802, 14 [NASA ADS] [CrossRef] [Google Scholar]
  122. Urrutia, T., Lacy, M., Spoon, H., et al. 2012, ApJ, 757, 125 [NASA ADS] [CrossRef] [Google Scholar]
  123. Urry, C. M., & Padovani, P. 1995, PASP, 107, 803 [NASA ADS] [CrossRef] [Google Scholar]
  124. Valencia-S., M., Zuther, J., Eckart, A., et al. 2012a, A&A, 544, A129 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  125. Valencia-S., M., Zuther, J., Eckart, A., et al. 2012b, in Proceedings of Nuclei of Seyfert galaxies and QSOs – Central engine & conditions of star formation, PoS(Seyfert 2012)017 [Google Scholar]
  126. Véron-Cetty, M.-P., & Véron, P. 2006, A&A, 455, 773 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  127. Vitale, M., Fuhrmann, L., García-Marín, M., et al. 2015, A&A, 573, A93 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  128. Wisotzki, L., Christlieb, N., Bade, N., et al. 2000, A&A, 358, 77 [NASA ADS] [Google Scholar]
  129. Xu, D., Komossa, S., Zhou, H., et al. 2012, AJ, 143, 83 [NASA ADS] [CrossRef] [Google Scholar]
  130. Zuther, J., Iserlohe, C., Pott, J.-U., et al. 2007, A&A, 466, 451 [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]

Appendix A: Discussion of individual objects

In the following section, we discuss individual objects. Spectra of all galaxies are shown in Fig. A.2. All spectra were extracted from an aperture with radius corresponding to 3 × FWHM of the seeing.

05 HE0036–5133

The galaxy HE0036–5133 (also known as WPVS7) at z = 0.0288, corresponding to DL = 126.1 Mpc, has been classified as Narrow-line Seyfert-1 (NLS1 Véron-Cetty & Véron 2006) and found to be X-ray transient with a broad-absorption line outflow (Leighly et al. 2009). Fitting the hydrogen recombination line Paα yields a width of the broad component of around 1300 km s-1, confirming the classification as NLS1 (but see also discussion about NLS1 in Valencia-S. et al. 2012b).

08 HE0045–2145

HE0045–2145 (also known as ESO 540-27) is a barred spiral galaxy at redshift z = 0.0214, corresponding to DL = 93.2 Mpc, that is, one of the closest objects in our sample. An elongated, patchy structure in the direct neighbourhood indicates tidal interaction with a possibly disrupted companion. However, more detailed information about this object is lacking, and the galaxy itself does not show other indications for interaction. The NIR colours suggest a low AGN contribution. Prominent stellar absorption features in the analysed nuclear spectrum support the importance of the stellar contribution in the central region as well. We were unable to find broad components in the hydrogen recombination lines, which should be visible in a type-1 galaxy.

The IRAS colours are log (fν(12 μm) /fν(25 μm)) = −0.7 and log (fν(60 μm) /fν(100 μm)) = −0.17. In the IRAS colour–colour diagrams by Helou (1986), this lies in a region characteristic of starburst-dominated galaxies. In the FIR colour–colour diagram of Kewley et al. (2000) (Fig. 6), this galaxy also lies on the starburst line.

The galaxy has a H i gas mass of 5.7 × 109M⊙ (König et al. 2009) and a H2 gas mass of 2.9 × 109M⊙ (Bertram et al. 2007). This means that stars are formed quite efficiently with a star formation efficiency (SFE) of LFIR/MH2=17L⊙ M⊙-1\hbox{$L_\mathrm{FIR}/M_{\rm H_2} = 17~L_\odot\,M_\odot^{-1}$} (Eckart et al. 1994).

In the continuum fit (Sect. 3.1), the resulting black body temperature of T ~ 25 000 K is much higher than expected for a dusty torus with a temperature T ~ 1200 K. This is the black body temperature of O and B stars, indicative of a recent starburst. All these arguments indicate that HE0045–2145 is highly dominated by its stellar components and ongoing star formation and that the classification as Seyfert 1 is invalid.

In Fig. A.1 we show the radial distribution of the Paα emission, which shows a bump at a distance of ~4″ from the centre, which might be attributed to a star formation region.

thumbnail Fig. A.1

HE0045–2145: Radial distribution of the intensity of the Paα emission line. The off-central bump at ~4″ south-west of the nucleus (see Fig. 2) might be attributed to a circumnuclear star-forming region.

thumbnail Fig. A.2

Spectra of all analysed galaxies. Spectra have been extracted from an aperture with a radius corresponding to 3 × FWHM of the seeing. The region between H- and K-band with low transmission has been marked in grey.

11 HE0103–5842

thumbnail Fig. A.2

continued.

thumbnail Fig. A.2

continued.

HE0103–5842 (also known as ESO 113-10) has a redshift of z = 0.0257 (DL = 112.2 Mpc). At first view, it looks like a barred galaxy. However, the morphological study (Busch et al. 2014) reveals a more complex underlying structure. The galaxy has been classified as Seyfert 1.8 by Véron-Cetty & Véron (2006). No broad components could be resolved in our NIR spectra. The blue continuum and deep stellar absorption features indicate that the nuclear non-stellar component is weaker than the stellar component. In the continuum fit, the stellar component has indeed a fraction of ~90%.

Mehdipour et al. (2012) measured the Balmer decrement and found a significant amount of reddening in the line of sight. However, neither our NIR Paα/Brγ line ratios nor our continuum fit show a significant contribution from extinction.

16 HE0119–0118

thumbnail Fig. A.2

continued.

HE0119–0118 (also known as Mrk1503 or II Zw 1) is a barred spiral galaxy at z = 0.0547, corresponding to DL = 244 Mpc and can be classified as luminous infrared galaxy (LIRG) based on the IRAS fluxes. The IRAS colours of log (fν(12 μm) /fν(25 μm)) ≤ −0.3 and log (fν(60 μm) /fν(100 μm)) = −0.1 lie in the region of starburst-dominated galaxies. In the FIR colour–colour diagrams of Kewley et al. (2000) (Fig. 6), the galaxy consistently shows a starburst contribution of at least 50%. The H2 mass is MH2 = 5.4 × 109M⊙ (Bertram et al. 2007). This results in a star formation efficiency of 24 L⊙ M⊙-1\hbox{$24\,L_\odot\,M_\odot^{-1}$}, almost the highest detected SFE found in Galactic star-forming regions (≤30 L⊙ M⊙-1\hbox{$L_\odot\, M_\odot^{-1}$}; Eckart et al. 1994, and references therein). The broad hydrogen recombination lines (FWHMBLR ≈ 3000 km s-1) and the position in the diagnostic diagram (Fig. 4) indicate black hole accretion and that the classification as Seyfert galaxy (Sy1.5, Véron-Cetty & Véron 2006) is correct. However, the mentioned arguments show that star formation is a significant factor.

In the imaging study (Busch et al. 2014), we found the central stellar component (bulge) to be very compact, and we were unable to separate it from the nuclear non-stellar component. This could indicate the presence of a compact, disky pseudo-bulge. These objects are products of secular evolution and often star forming, in contrast to classical bulge components (see review of Kormendy & Kennicutt 2004). The coexistence of prominent non-stellar (e.g. broad lines) and stellar (CO absorption) features in the spectrum indicate that even in the nuclear region, star formation could be of importance, motivating more detailed investigation of this source, for instance by integral field spectroscopy.

The black hole mass estimates for this source range from log (MBH/M⊙) = 7.1 (Xu et al. 2012) over log (MBH/M⊙) = 7.58 (Bennert et al. 2011) and log (MBH/M⊙) = 7.9 (Schulze, priv. comm.) to log (MBH/M⊙) = 7.9 (this study), demonstrating the uncertainty of black hole mass estimates.

31 HE0323–4204

HE0323–4204 has been classified as Seyfert 1.5 by Véron-Cetty & Véron (2006). The prominent broad emission line components of Paα and Brγ, the significant power-law and hot dust contribution to the continuum flux, and the forbidden [Fe ii] line indicate strong nuclear activity.

The IRAS colours are log (fν(12 μm) /fν(25 μm)) ≤ −0.18 and log (fν(60 μm) /fν(100 μm)) = −0.36, which is between the typical colours of starburst galaxies and elliptical galaxies. In the FIR colour–colour diagrams (Fig. 6), they lie on the extreme mixing line with a starburst contribution of ≳50%. The continuum fit yields a stellar contribution of ~30%. Furthermore, stellar absorption lines and the position in the diagnostic diagram (Fig. 4) close to the location of starburst galaxies indicate an at least moderate contribution of star formation.

The galaxy at redshift z = 0.058 (DL = 259.3 Mpc) is very inclined and probably a spiral galaxy. In the direct neighbourhood, there is a companion galaxy that shows signs of interaction. In some astronomical databases, the galaxy is sometimes confused with another spiral galaxy in the neighbourhood. This galaxy also falls into the slit, but shows no signs for non-stellar contribution.

80 HE2112–5926

HE2112–5926 is part of the interacting galaxy pair ESO 144-21. It has an elliptical shape; the interacting partner is a spiral galaxy. The source has redshift z = 0.0317 (DL = 139.1).

The stellar contribution to the continuum emission is ~80%. This agrees with the low-luminosity fraction of the non-stellar point source found in the imaging study (Busch et al. 2014).

81 HE2128–0221

HE2128–0221 appears as an elongated elliptical at redshift z = 0.0528 (DL = 235.2 Mpc) and is the least luminous object observed in this study. This explains the low signal-to-noise ratio. The continuum is blue and shows clear stellar absorption lines. The hydrogen recombination line Paα is weaker than the continuum emission. However, a broad component is clearly seen. The Brγ line almost vanishes in the noise and shows a strange (boxy) shape.

82 HE2129–3356

HE2129–3356 is an elliptically shaped galaxy at redshift z = 0.0293 (DL = 128.3 Mpc), classified as Seyfert 1.2 by Véron-Cetty & Véron (2006). The galaxy is surrounded by several objects it is most probably interacting with. Stellar absorption features are present, and the stellar component contributes ~75% to the continuum emission. Nevertheless, the position in the diagnostic diagram (Fig. 4) is that of an AGN-dominated galaxy, and the presence of the coronal line [Si vi] emission indicates a type-1 AGN. The hydrogen recombination lines are also very strong and show the broadest line widths (~5000 km s-1) in this study.

83 HE2204–3249

HE2204–3249, also known as ESO 404-29, has a redshift of z = 0.0594 (DL = 265.9 Mpc) and is the most distant object observed in this study. In the imaging study (Busch et al. 2014), the decomposition revealed a non-axisymmetrical residuum, which might be interpreted as a dust lane. From the line ratio Paα/Brγ, we obtain a high extinction of AV ≈ 50 mag2. The continuum fit shows no sign of extinction (but see the comment on the reliability of this fit in Sect. 3.1).

Several objects located in the direct neighbourhood are probably in a tidal interaction with the galaxy. The spectrum shows a very blue continuum shape, and deep stellar absorption lines are seen. From the continuum fit we obtain a stellar contribution to the continuum of about 90%. Additionally, the hydrogen recombination lines are much weaker than the continuum flux. Nevertheless, we detect broad components (FWHMBLR ≈ 4000 km s-1), consistent with the classification as Seyfert 1.2 in Véron-Cetty & Véron (2006).

84 HE2211–3903

HE2211–3903 (also known as ESO 344-16) is a barred spiral galaxy, classified as Seyfert 1.5 galaxy by Véron-Cetty & Véron (2006). The redshift is z = 0.0398, corresponding to DL = 175.6 Mpc. In the previous imaging study (Busch et al. 2014), we revealed an additional spiral arm and an inner ring that can also be found in emission line maps by Scharwächter et al. (2011).

Confirming the results from Fischer et al. (2006), the spectrum is very red as a result of black body radiation coming from hot dust at a temperature of ~1100 K, which makes up about 60% of the continuum radiation. Probably because of this high contribution of hot dust, the spectrum does not show any other features than the hydrogen recombination lines Paα and Brγ.

Scharwächter et al. (2011) reported that based on unresolved narrow-line ratios (log ([N ii] / Hα) = −0.2, log ([O iii] / Hβ) = 0.2 and log ([S ii] / Hα) = −0.5), the galaxy would be classified as “composite”. However, through spatially resolved spectroscopy, they found an extended narrow-line region on scales up to 8 kpc.

Based on IRAS fluxes, the galaxy is a LIRG. The H2 mass is MH2 = 9.3 × 109M⊙ (Bertram et al. 2007), resulting in an SFE of 4M⊙L⊙-1\hbox{$4~M_\odot~L_\odot^{-1}$}. We see that the IRAS data suggest ongoing star formation activity. Since there is no evidence for any strong interaction, HE2211–3903 is probably an example for a secularly evolving galaxy.

85 HE2221–0221

HE2221–0221 is also known as 3C 445 and classified as a “double-double” radio galaxy (Schoenmakers et al. 2000). It has a redshift of z = 0.057 (DL = 254.7 Mpc). In NIR images, it appears as round elliptical with very bright unresolved nucleus. Our imaging study (Busch et al. 2014) shows that the host is almost outshone by the nucleus. The Paα line is very broad and strong. However, the Brγ line is rather weak. This could be induced by reddening. Indeed, the shape and the NIR colours are extremely red and the continuum fit yields an extinction of AV = 1.6 mag. Nevertheless, the spectrum shows some stellar features, and the stellar contribution is around ~30%. The presence of strong [Si vi] emission is indicative of nuclear activity.

Appendix B: Additional figure

thumbnail Fig. B.1

H + K-band spectra of the objects. For each galaxy, we show a central spectrum (black line/lower spectrum) that is extracted from an aperture with diameter corresponding to the FWHM of the seeing (see Table 1). In addition, we show an off-central spectrum (grey line/upper spectrum), centred 1.5 ×FWHM left and right from the nucleus. The spectra are shown in restframe wavelength and have been normalised at 2.159 μm. Important emission and absorption lines have been marked.

thumbnail Fig. B.1

continued

All Tables

Table 1

Source information: coordinates, morphological classification, redshift, and observation parameters.

In the text
Table 2

Results of the continuum fits: fraction of dust component (black body radiation) and its temperature T, fraction of the AGN component (power law), fraction of the stellar component, and the extinction.

In the text
Table 3

Hydrogen recombination line fits.

In the text
Table 4

Emission line fluxes of molecular hydrogen lines and of the forbidden line [Fe ii].

In the text
Table 5

BH mass estimates from the broad Paα line compared with A. Schulze’s BH mass estimates from Hα.

In the text
Table 6

IRAS fluxes and derived quantities: IR and FIR luminosity, FIR star formation rate.

In the text

All Figures

thumbnail Fig. 1

Left: bolometric luminosity as a function of the linear scale pc/arcsec. Right: histogram of the Eddington ratios of the LLQSO sample in comparison to the recent study of Müller-Sánchez et al. (2013), the NUGA sample (García-Burillo et al. 2003), and the work of the AGNIFS group (Storchi-Bergmann, Riffel, et al.).
In the text
thumbnail Fig. 2

SofI H-band images of the eleven galaxies with slit positions indicated. The slit width is 1′′. North is up, east is left.
In the text
thumbnail Fig. 3

Continuum fit. HE0103–5842 is shown as an example.
In the text
thumbnail Fig. 4

Diagnostic diagram in the NIR with line ratios log (H2(1−0)S(1) / Brγ) and log ([Fe ii] λ1.257 μm / Paβ) (for details see text). Apertures have a diameter corresponding to 3 × FWHM of the seeing. The positions of nine galaxies in the diagnostic diagram are shown. The lines indicate regions that are typically populated by starburst galaxies, AGNs, and LINERs resp. Dashed lines: Rodríguez-Ardila et al. (2005) and Riffel et al. (2010), dotted lines: Riffel et al. (2013).
In the text
thumbnail Fig. 5

Correlation between black hole mass MBH and absolute K-band magnitude MK of the spheroidal component. Bulge magnitudes are taken from Busch et al. (2014). Black hole masses for the red points are adopted from A. Schulze (data points already published in Busch et al. 2014). The green point is taken from Busch et al. (2015). Four data points with black hole masses derived from NIR spectroscopy in this work are added in blue.
In the text
thumbnail Fig. 6

Far-infrared colour–colour diagrams, modified from Kewley et al. (2000). Top: log (f60 μm/f25 μm) vs. log (f25 μm/f12 μm) diagram. Bottom: log (f60 μm/f12 μm) vs. log (f100 μm/f25 μm) diagram. In both plots, the solid line is the reddening line calculated by Dopita et al. (1998). The straight dashed line marks the typical area of starbursts. The “extreme mixing line” corresponds to the mixture of a typical Seyfert-1 spectrum and a starburst component (with denoted percentage). The open symbols are Seyfert-1, starburst, and Seyfert-2 galaxies with (HBLR) and without (Sy2) broad-line components observed in polarised light, taken from the sample of Rush et al. (1993). The positions of the analysed galaxies are indicated by the coloured data points.
In the text
thumbnail Fig. A.1

HE0045–2145: Radial distribution of the intensity of the Paα emission line. The off-central bump at ~4″ south-west of the nucleus (see Fig. 2) might be attributed to a circumnuclear star-forming region.
In the text
thumbnail Fig. A.2

Spectra of all analysed galaxies. Spectra have been extracted from an aperture with a radius corresponding to 3 × FWHM of the seeing. The region between H- and K-band with low transmission has been marked in grey.
In the text
thumbnail Fig. A.2

continued.
In the text
thumbnail Fig. A.2

continued.
In the text
thumbnail Fig. A.2

continued.
In the text
thumbnail Fig. B.1

H + K-band spectra of the objects. For each galaxy, we show a central spectrum (black line/lower spectrum) that is extracted from an aperture with diameter corresponding to the FWHM of the seeing (see Table 1). In addition, we show an off-central spectrum (grey line/upper spectrum), centred 1.5 ×FWHM left and right from the nucleus. The spectra are shown in restframe wavelength and have been normalised at 2.159 μm. Important emission and absorption lines have been marked.
In the text
thumbnail Fig. B.1

continued
In the text

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

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

Initial download of the metrics may take a while.