© ESO, 2013

1. Introduction

Stars in the mass range between 1 and 8 M_⊙ go through the planetary nebula (PN) phase before ending their lives as white dwarfs. The optical image of a PN is dominated by the luminous ionized envelope that is powered by the stellar core at its centre. The envelope emits in several strong lines from the UV to the NIR, and Dopita et al. (1992) showed that up to 15% of the luminosity of the central star is re-emitted in the forbidden [OIII] line at λ5007 Å.

PNs have been used as kinematic tracers of the stellar orbital distribution in the outer regions of galaxies, where the continuum from the stellar surface brightness is too low with respect to the night sky, both in nearby galaxies (Hui et al. 1993; Peng et al. 2004; Merrett et al. 2006; Coccato et al. 2009; Cortesi et al. 2013) and out to 50–100 Mpc (Ventimiglia et al. 2011; Gerhard et al. 2005). The outer regions of galaxies are particularly interesting because dynamical times are longer there, so they may preserve information of the mass assembly processes.

The observed properties of the PN population in external galaxies also correlate with the age and metallicity of the parent stellar population. These properties are the luminosity-specific PN number, α-parameter for short, that quantifies the stellar luminosity associated with a detected PN, and the shape of the PN luminosity function (PNLF). We discuss these in turn.

Observationally the values of α correlate with the integrated (B − V) colour of the parent stellar population, with the spread in observed values for the reddest galaxies increasing significantly with respect to the constant value observed in bluer ((B − V) < 0.8) objects (Hui et al. 1993; Buzzoni et al. 2006). For the reddest galaxies, the value of α correlates with the (FUV − V) colour, with the lowest number of PNs observed in old and metal-rich systems (Buzzoni et al. 2006).

To describe the shape of the PNLF for extragalactic PN populations, the analytical formula proposed by Ciardullo et al. (1989) has generally been used, but see also models by Méndez et al. (1993, 2008). At the brightest magnitudes the PNLF shows a cutoff that is observed to be invariant between different Hubble types and has been used as secondary distance indicator (Ciardullo et al. 2004). From about one magnitude fainter than the PNLF cutoff, the analytical formula predicts an exponential increase, in agreement with the slow PN fading rate described by Henize & Westerlund (1963). Observationally, the PNLF slope correlates with the star formation history of the parent stellar population, with steeper slopes observed in older stellar populations and flat or slightly decreasing slopes in younger populations (Ciardullo et al. 2004; Ciardullo 2010).

The Virgo cluster, its elliptical galaxies, and intracluster light (ICL) were the targets of several PN surveys (Ciardullo et al. 1998; Feldmeier et al. 1998, 2004; Arnaboldi et al. 2002, 2003, 2004; Castro-Rodríguez et al. 2003, 2009; Aguerri et al. 2005), aimed at measuring distances and the ICL spatial distribution. In this paper we report the results of a deep survey carried out with the Suprime-Cam at the Subaru telescope to study the PN population in the halo around M 87, one of the two brightest galaxies in the Virgo cluster.

NGC 4486 (M 87) is a giant elliptical galaxy situated at the centre of the subcluster A in the Virgo cluster (Binggeli et al. 1987), the nearest large scale structure in the local universe. According to current models of structure formation, M 87 acquired its mass over a long period of time through galaxy mergers and mass accretion. The stars in M 87 are old (Liu et al. 2005), and its stellar halo contains about 70% of the galaxy’s light down to μ_V ~ 27.0 mag arcsec^-2 (Kormendy et al. 2009). Thus M 87 is an ideal target for a survey aimed at detecting PNs in an extended galaxy halo, when investigating the halo kinematics and stellar population.

The paper is organized as follows: in Sect. 2 we describe the Suprime-Cam PN survey and the data reduction procedure. In Sect. 3 we describe the PN catalogue extraction and validation. The relation between the spatial distribution of the PN candidates and the M 87 surface brightness profile is investigated in Sect. 4. We present the PNLF measured in the M 87 halo in Sect. 5 and discuss the comparison with the PNLF for the M 31 bulge. Finally, we summarize our conclusions in Sect. 6. In the rest of the paper we adopt a distance modulus of 30.8 for M 87, which means that the physical scale is 73 pc arcsec^-1.

2. The Suprime-Cam M 87 PN survey

2.1. Imaging and observations

In March 2010 we observed two fields with the Suprime-Cam 10 k × 8 k mosaic camera, at the prime focus of the 8.2 m Subaru telescope (Miyazaki et al. 2002). The CCDs have a readout noise of 10 e⁻ and an average gain of 3.1 e⁻ ADU^-1. Each field of view covers an area of 34′× 27′, with a pixel size of 02; the two pointings cover the halo of M 87 out to a radial distance of 150 kpc. Figure 1 shows a deep V-band image of the Virgo cluster core region and the two fields studied in this work overlaid. We label these fields as the M 87 SUB1 and M 87 SUB2 fields, respectively.

Both fields were observed through an [OIII] narrow-band filter (on-band filter), centred on λ_c = 5029 Å with a band width Δλ = 74 Å and a broad-band V-filter (off-band filter). The total exposure time for the on-band images was ~3.7 h and ~4.3 h, while for the off-band images it was 1 h and 1.4 h, for the M 87 SUB1 and M 87 SUB2 fields, respectively. Deep V-band images are needed for the colour selection of the PN candidates (see Sect. 3).

Our strategy for data acquisition was set up to achieve the best image quality. Narrow-band and broad-band images were taken close to each other during the observing nights to secure similar conditions in terms of scattered light and atmospheric seeing, while calibration images, such as dark sky¹, were taken in between the science images to have a similar signal-to-noise ratio and image quality.

The nights were photometric with an overall seeing on the images less than 1″. Airmasses were 1.01 and 1.06 for the reference M 87 SUB1 [OIII] and V-band exposures, and 1.13 and 1.03 for the reference M 87 SUB2 [OIII] and V-band exposures². We did not perform any measurements for the extinction coefficients. We adopted the mean value of X = 0.12 mag/airmass, as listed for the [OIII] and V filters at the Mauna Kea summit web site³, and in agreement with Buton et al. (2013).

The on-band filter bandpass was designed such that its central wavelength coincides with the redshifted λ5007 Å emission at the Virgo cluster. The Johnson V-band filter can be used as the off-band filter despite the fact that it contains the [OIII] line in its large bandpass (~1000 Å). The depth of the Suprime-Cam survey was chosen to detect all PNs brighter than m^∗ + 2.5, where m^∗ is the [OIII]5007 Å apparent magnitude of the bright cutoff of the PNLF for a distance modulus 30.8. Table 1 gives a summary of the field positions, filter characteristics and exposure times for the on-band and off-band exposures for the analysed area around M 87.

2.2. Data reduction, astrometry and flux calibration

The removal of instrumental signature, geometric distortion correction, background (sky plus galaxy) subtraction, a first astrometric solution and the final image combination were done using standard data reduction packages developed for Suprime-Cam data (sdfred software v2.0⁴). Cosmic rays identification and rejection were carried out with L.A. Cosmic (Laplacian Cosmic Ray Identification) algorithm (van Dokkum 2001). Because of the spectroscopic follow-up our aim was to derive accurate positions of the PN candidates. To do this we improved the astrometry of the images to get a relative positional accuracy with less than in the residuals. The astrometric solution was computed using image astrometry tasks in the IRAF⁵ package imcoords, performing a geometrical transformation by 2nd order polynomial fitting. All images analysed in this paper are on the astrometric reference frame of the 2MASS catalogue⁶.

We flux calibrated the broad-band and narrow-band frames to the AB system by observing standard stars through the same filters used for the survey. We used spectrophotometric standard star Hilt600 and Landolt stars for the [OIII]-band and V-band flux calibrations respectively, giving zero points for the [OIII] and V-band frames equal to Z[OIII] = 24.29 ± 0.04 and Z[V]   =   27.40 ± 0.17, normalised to a 1 second exposure⁷.

However, the integrated flux from the [OIII] line of a PN is usually expressed by the magnitude m₅₀₀₇ using the relation introduced by Jacoby (1989): (1)where F₅₀₀₇ is in units of ergs cm^-2 s^-1. From this we determined the absolute flux calibration for the nebular flux in the [OIII] emission line, following Jacoby et al. (1987). This flux calibration takes the filter transmission efficiency at the wavelength of the emission line into account, due to the fact that the fast optics of wide-field instruments, such as the Suprime-Cam at the Subaru telescope can affect the transmission properties of the interference filter, especially for narrow-band imaging. This effect was quantified making use of the filter transmission curve described in Arnaboldi et al. (2003), representing the expected transmission of the [OIII] interference filter in the f/1.86 beam of the Suprime-Cam at the Subaru telescope.

The relation between the m₅₀₀₇ and m_AB magnitudes for the narrow-band filter is given by: (2)A detailed description of the relation between AB and m₅₀₀₇[OIII] magnitudes is given in Arnaboldi et al. (2002).

In this paper we will use the notation of m_n and m_b to refer to the narrow-band and broad band magnitudes of the objects in the AB system. We will use m₅₀₀₇ to refer to the [OIII] magnitudes introduced by Jacoby (1989).

Table 2 gives the constant value for AB-to-m₅₀₀₇ magnitude conversion, and limiting magnitudes in the on-band and off-band frames analysed in this paper.

The final products for the M 87 SUB1 and M 87 SUB2 pointings consist of the stacked images in the [OIII] and V band, astrometrically and photometrically calibrated. As consequence of the observing strategy the seeing measured as the average FWHM of stellar sources is similar in both pairs of images, and less than FWHM_seeing < 1″ (see Table 1). A fit of the PSF with the IRAF task psf (in the digiphot/daophot package) shows that a Moffat analytical function with a β parameter β = 2.5 (see IRAF/digiphot manual for more details) is the best profile to model the stellar light distribution of point sources in our images. Moreover, the PSF fit was computed in three different regions of the on-band and off-band images for both the M 87 SUB1 and M 87 SUB2 fields, covering the images from the centre to the edges: for all regions examined, the PSF fit gave the same best fit solution, showing that the PSF does not vary across the images.

3. Selection of PN candidates and catalogue extraction

As a result of their bright [OIII] (λ5007) and faint continuum emission, extragalactic PNs can be identified as objects detected in images taken through the on-band [OIII] filter, but not detected in images taken through the off-band continuum filter. In our work, all PN candidates are extracted through the on-off band technique (Jacoby et al. 1990) using selection criteria based on the distribution of the detected sources in colour magnitude diagrams (Theuns & Warren 1997). We used an automatic extraction procedure developed and validated in Arnaboldi et al. (2002, 2003) for the identification of emission line objects in our images. We give a brief summary of the selection procedure in the next section.

3.1. Extraction of point-like emission-line objects

We employed the object detection algorithm SExtractor (Bertin & Arnouts 1996), that detects and measures flux from point-like and extended objects. Sources were detected in the on-band image requiring that 20 adjacent pixels or more have flux values 1 × σ rms above the background. Magnitudes were then measured in a fixed aperture of radius R = 6 pixels (1.2″, ~3 times the seeing radius) for sources in the on-band image, and then through the aperture photometry in the V-band image at the (x,y) positions of the detected [OIII] sources with SExtractor in dual-image mode. Candidates for which SExtractor could not detect a m_b magnitude at the position of the [OIII] emitter were assigned a m_b = 28.7, i.e. the flux from an [OIII] emission of m_n = m_lim,n seen through a V-band filter (Theuns & Warren 1997). All objects were plotted in a colour magnitude diagram (CMD), m_n − m_b vs. m_n, and classified according to their positions in this diagram. Based on their strong [OIII] line emission, the most likely PN candidates are point-like objects with colour excess corresponding to an observed EW greater than 110 Å, after convolution with photometric errors as function of the magnitude. The limit of completeness is defined as the magnitude at which the recovery fraction of an input simulated point-like population with a given luminosity function⁸, drops below a threshold set to 50%. We find m_lim,5007 = 28.39; this is the limiting magnitude of our sample.

The colour selection of PN candidates requires off-band images deep enough so that the colour can be measured reliably at fainter magnitudes. As for the [OIII] images, we define the V-band limiting magnitude as the faintest magnitude at which half of the input simulated sample is retrieved from the image; we then derive m_lim,b = 26.4 and m_lim,b = 26.6 for the M 87 SUB1 and M 87 SUB2 fields respectively.

3.1.1. Colour selection

PN candidates are defined as objects with [OIII] magnitudes brighter than the [OIII] limiting magnitude and with a colour excess, m_n ≤ m_lim,n and m_n − m_b < −0.99, the latter representing the colour excess corresponding to EW_obs = 110 Å. The relation between the observed EW and the colour in magnitudes is given by Teplitz et al. (2000): EW_obs ≃ Δλ_nb(10^0.4Δm − 1), where Δλ_nb is the width of the narrow band filter and Δm = m_b − m_n is the colour. A value of EW_obs = 110 Å limits contamination from [OII]λ3726.9 emitters at redshift z ~ 0.34 (see Sect. 3.2).

Photometric errors may be responsible for continuum emission sources falling below the adopted colour excess, hence contaminating the emission object distribution. This effect is limited by defining regions in which 99% and 99.9% of a simulated continuum population would fall in this CMD. Below the 99.9% line, the probability to detect stars is reduced to the 0.1% level.

Theoretically a PN is a point-like source with no detected continuum, i.e. with no broadband magnitude measured by Sextractor. Nevertheless we expect a continuum contribution in an aperture at the position of a [OIII] source in the halo of M 87 because of crowding effects or residuals from the subtraction of the continuum light from the M 87 halo. This is confirmed by the distribution of colours measured for a simulated PN population in the outer regions of M 87, which we discuss in Sect. 3.1.4.

3.1.2. Point-like versus extended sources

At a distance of ~15 Mpc, PNs are unresolved points of green light. We need to distinguish them from spatially extended background galaxies and, possibily, HII regions. Point-like objects in our catalogue are candidates that satisfy the following criteria:

• 1.

  we compare the SExtractor m_n and m_core magnitudes, where m_core represents the measured magnitude in a fixed aperture of radius R = 2 pixels (0.4″). For point-like objects m_n − m_core has a constant value as function of magnitude, while it varies for extended sources. We analyzed this difference for simulated point-like objects and determined the range of m_n − m_core as function of m_n where 96% of the simulated population fall. This range is narrow for bright magnitudes but it becomes wider towards fainter magnitudes due to the photometric errors.

• 2.

  We derive the distribution for the SExtractor half-light radius R_h, i.e. the radius within which half of the object’s total flux is contained. For point-like objects this value should be constant but due to photometric errors it is confined to a range 1 ≤ R_h ≤ 4 pixels that we determine as the range in which 95% of the simulated population lies.

These criteria for the selection of point-like sources are shown in Fig. 2. For the simulated population, 91% of the input sources are recovered as point-like objects. Therefore, applying these criteria to the observed objects excludes 9% of the PNs from the sample along with the extended objects.

3.1.3. Masking of bad/noisy regions

The last step of our automatic selection is the masking of the areas where the detection and photometric measurements of sources are dominated by non-Poisson noise. These areas include diffraction and bleed spikes from the brightest stars, bad pixel regions and higher background noise regions, the latter mostly at the edges of the images because the dithering strategies lead to different exposure depth near the edges (we will discuss this further in Sect. 3.1.5). After these regions are excluded, the total effective area of our survey is ~0.43 deg².

The selection described above leads to a catalogue, hereafter called automatic sample, containing 792 objects classified as PN candidates. These sources are point-like objects with colour excess, corresponding to an observed equivalent width EW_obs > 110 Å  and are located in regions of the colour–magnitude diagram where the contamination by foreground stars is below 0.1%.

Figure 3 shows the colour selection for the M 87 SUB1 and M 87 SUB2 fields, with the PN candidates represented by asterisks.

3.1.4. Missing PNs in the photometric sample

The selection procedure based on flux thresholds is sensitive to photometric errors. At fainter magnitudes, PN candidates with intrinsic EW_obs > 110 Å, or m_n ≤ m_lim,n, can have smaller measured EW_obs, or fainter m_n, because of photometric errors and are, therefore, excluded from our selection. We quantified this effect by simulating an [OIII] emission line population, with an exponential LF in the magnitude range 23 ≤ m ≤ 27.5, randomly distributed on the on-band scientific image. No continuum emission was assigned to the objects. We then carried out the photometry as for the real sources: their CMD is shown in Fig. 4. We see that many simulated PNs have a measured continuum magnitude due to crowding effects or residuals from the galaxy background. This CMD shows also that for simulated sources brighter than the limiting magnitude, the percentage of PNs that we would miss due to photometric errors is 28.3% and 29.8% in the M 87 SUB1 and M 87 SUB2 fields respectively. This implies that our automatic procedure, within the magnitude range 23 ≤ m_n ≤ m_lim,n, recovers 0.71 of the total sample. In Table 5 we show the dependence of this colour completeness on magnitude.

3.1.5. Catalogue validation: visual inspection and joint candidates in both fields

Finally, the photometric catalogue obtained from the automatic selection described in the previous sections was visually inspected, in particular for regions near bright stars or with a higher noise level. This visual inspection led to a catalogue of 688 candidates, the removed spurious detections being ~11% of the automatic extracted sources.

In the final catalogue 18 candidates appear in both fields and their magnitudes are measured independently in the two pointings. Differences between the independent measurements are consistent with the errors.

3.2. Possible sources of contaminants in the PN sample

Following Aguerri et al. (2005), we examined the main contaminants and estimate their contributions to the final catalogue.

Contamination by faint continuum objects – At m_lim,n, the faint continuum objects can mimic an [OIII] emission line population, because they are scattered into the region where PN candidates are selected. We constrained this contribution by computing the 99.9% lines for the distribution of continuum objects. From the total number of the observed foreground stars, we determine the number of objects (0.1%) that would be scattered in the region of colour selected PNs. The resulting contribution from faint stars equals 9% and 11% of the total extracted sample for the M 87 SUB1 and M 87 SUB2 fields, respectively.

Contamination by background galaxies: Lyman α galaxies and [OII] emitters – The strong [OIII] λ5007 PN emission with no associated detected continuum allows us to identify PNs as objects with negative colour (Theuns & Warren 1997). This colour selection will also identify Lyα galaxies at redshift z ~ 3.1 as well as [OII] λ3727.26 emitters at redshift z ~ 0.34, whose emission lines fall within the bandpass of our narrow band filter.

The contamination by Lyα is quantified by using the number density of z = 3.1 Lyα galaxies from Gronwall et al. (2007) where their limiting magnitude of m_lim,n(G07)(5007) = 28.31 for statistical completeness makes their survey as deep as ours. We consider their Lyα LF given by a Schechter function of the form: (3)with their best-fit values of log₁₀L^∗ = 42.66 erg s^-1 and α = −1.36, corrected for the effects of photometric errors and their filter’s non-square transmission curve. At redshift z = 3.1, the surveyed area of 0.43 deg², observed through the Suprime-Cam narrow-band filter, samples ~3.1 × 10⁵ Mpc³ (Hogg 1999). However, as pointed out by Gronwall et al. (2007), when working with narrow band data taken through a nonsquare filter bandpass, the effective survey volume is ~25% smaller than that inferred from the interference filter’s FWHM. We thus compute the number of expected Lyα emitters by using a survey volume of ~2.3 × 10⁵ Mpc³. The Lyα population at z ~ 3.1 shows clustering over a correlation length of r₀ = 3.6 Mpc (Gawiser et al. 2007), corresponding to an angular size in the Virgo cluster of . Hence we need to allow for the large-scale cosmic variance in the average Lyα density, which in our case is ~20% (Somerville et al. 2004; Gawiser et al. 2007). From our effective sampled volume, we predict, then, that the number of expected Lyα contaminants is 25% ± 5% of our total catalogue.

The contribution from [OII] emitters is considerably reduced by selecting candidates with observed equivalent width, EW_obs > 110 Å, corresponding to a colour threshold m_n − m_b < −0.99 (Teplitz et al. 2000). This is because no [OII] emitters with EW_obs > 95 Å have been found (Colless et al. 1990; Hammer et al. 1997; Hogg et al. 1998). Note that in the Gronwall et al. (2007) sample of Lyα galaxies at z = 3.1, the fraction of [OII] contaminants is considered to be negligible because of their EW selection (Δm_(G07) ~ −1). This means that if there is a fraction of [OII] emitters with EW_obs > 110 Å in our sample, then their contribution is accounted for by using the Lyα LF from Gronwall et al. (2007).

3.3. Comparison with previous PN samples in M 87

Our survey area overlaps with those studied by Jacoby et al. (1990), Ciardullo et al. (1998, hereafter C98) and Feldmeier et al. (2003, hereafter F03); the locations of the C98 and F03 fields are over-plotted on Fig. 1.

We have a limited number of objects in common between Jacoby et al. (1990) and our catalogue (~14% of their catalogue), because of the high residual background in our images from the bright central regions of M 87. These fractions are larger for the C98 and F03 catalogues, and we discuss them in turn.

C98 carried out an [OIII] λ5007 survey for PNs, covering a 16′ × 16′ field around M 87. They identified 329 PNs in the M 87 halo, 187 of these in a statistically complete sample down to m₅₀₀₇ = 27.15. Of the 329 sources selected by C98, 201 fall within our surveyed area⁹. Of these sources, 91% are matched with [OIII] detected objects in our survey, but only ~60% satisfy our selection criteria for PN candidates, see the CMD for the C98 candidates (green squares) in Fig. 4.

F03 carried out a survey of intracluster PNs (ICPNs) over several fields in the Virgo cluster region; the one overlapping with the current Subaru survey is a 16′ × 16′ area north of M 87, labeled “FCJ” field in F03 and Aguerri et al. (2005). 100% of the candidates in this field match with [OIII] sources in the M 87 SUB1 field, but only 42% satisfy the selection criteria for PN candidates in our survey, see the CMD for the F03 candidates (cyan triangles) in Fig. 4.

On the basis of the common PN candidates with the largest signal-to-noise ratios, we can compare the photometric calibration for the m₅₀₀₇ magnitudes and any variations with magnitude in the different samples. We find a constant offset that does not vary with magnitude between the C98, F03 samples and our survey, with our m₅₀₀₇ system being ~0.3 mag fainter. In Fig. 5 the F03 mags (corrected for ~0.3 mag shift) are compared with ours.

In Sect. 5 we will compare the empirical PNLF for our PN sample with those for the C98 and F03 data, and based on this comparison will argue that there is a systematic effect in the C98 and F03 photometry, causing brighter m₅₀₀₇. In what follows, the calibration offset is therefore applied to the previously published data whenever we compare them with our PN magnitudes.

4. The radial profile of the PN population and comparison with the M 87 surface brightness

In what follows we present the number density distribution from our PN sample, one of the largest both in number of tracers and in radial extent. Plotted in Fig. 6 is the position of our PN candidates together with the outline of our survey region.

4.1. The PN radial density profile

We now investigate whether the PN number density profile follows the surface brightness profile of the galaxy light. On the basis of the simple stellar population theory, the luminosity-specific stellar death rate is insensitive to the population’s age, initial mass function, and metallicity (Renzini & Buzzoni 1986). Thus the probability of finding a PN at any location in a galaxy should be proportional to the surface brightness of the galaxy at that location.

In order to compare the surface brightness and PN number density profiles, we bin our PN sample in elliptical annuli, whose major axes are aligned with M 87′s photometric major axis and with ellipticities measured from the isophotes (see Fig. 6). We compute PN number densities as ratios between the number of PNs in each annulus and the area of the intersection of the annulus with our field of view. These areas, A(R), are estimated using a Monte Carlo integration technique.

The PN number density profile must be corrected for spatial incompleteness because the bright galaxy background and bright foreground stars may affect the detection of PNs. We compute this completeness function, C(R), by adding a PN sample modelled according to a Ciardullo et al. (1989) PNLF on the scientific image. C(R) is then the fraction of simulated objects recovered by Sextractor in the different elliptical annuli vs the input modelled population, for PNs brighter than m₅₀₀₇ = 28.3 (values are given in Table 4). The expected PN total number is then: (4)where the value 0.71 accounts for the average colour incompleteness (see Sect. 3.1.4). In Fig. 7 we show the comparison between the major axis stellar surface brightness profile in the V band, μ_K09 (Kormendy et al. 2009), and the PN logarithmic number density profile, defined as: (5)where (6)is the PN number density corrected for spatial and colour incompleteness, and μ₀ is a constant value added to match the PN number density profile to the μ_K09 surface brightness profile. In the same plot, we also indicate radii that select three different regions in the halo of M 87; these regions are for , and , where represents the mean distance of the PN sample from the centre of M 87. The innermost and the outermost number density points are at radii R = 2.8′ and R = 27.6′ respectively, and in Fig. 7 they are indicated with dotted red lines. The surface brightness and the logarithmic PN number density profile agree well in the innermost region, they slightly deviate in the intermediate region while in the outermost region the logarithmic PN number density profile flattens. The difference between the two profiles amounts to 1.2 mag at the outermost radii. This discrepancy would still be observed if we had used instead the candidates from the automatic selection procedure without any final inspection: the difference between the two number densities is too small to affect the slope at large radii (see Fig. 7, bottom panel). We also note that the flattening of the logarithmic number density profile is seen in both Suprime-Cam fields independently.

Empirically, the logarithmic PN number density profile follows light in elliptical (Coccato et al. 2009) and S0 (Cortesi et al. 2013) galaxies. However, these studies cover the halos out to typically only 20 kpc. The presence of Lyα background galaxies at z ~ 3.1 could also contribute to the flatter slope of the logarithmic PN number density profile, and so we need to evaluate their contribution to the number density at large radii. In Fig. 7 we show the contribution from Lyα contaminants (black dotted line) to the logarithmic number density (25% ± 5% of the total sample), assuming a homogeneous distribution in the surveyed area. We can make this assumption because the survey area 0.43 deg² extends over many correlation lengths of the Lyα population at z = 3.14 (see Sect. 3.2).

Hence we can statistically subtract the contribution of the Lyα backgrounds objects from the number of PN candidates in each annulus and obtain the corrected number density profile (filled circles in Fig. 7). Now the error bars also account for the expected fluctuation of Lyα density (see Sect. 3.2 for details) and the flattening of the logarithmic PN number density profile is still observed. Using the derived α_2.1 value for the halo from Sect. 4.4, the surface brightness profile translates to ~295 PNs down to m₅₀₀₇ = 28.3. Down to this magnitude our catalogue contains ~420 estimated PNs. The excess, then, is ~3.6 × σ_Lyα, where σ_Lyα is the standard deviation in the expected number of Lyα emitters from cosmic variance and Poisson statistics. Hence, the flattening can not be explained by fluctuations in the Lyα fraction. In what follows we investigate the physical origin of the flatter logarithmic PN number density profile.

4.2. The α parameter

The total number of PNs, N_PN, is correlated with the total bolometric luminosity of the parent stellar population, L_bol, through the so-called α-parameter, that defines the luminosity-specific PN density: N_PN = αL_bol.

From stellar evolution theory it is found that the luminosity-specific stellar death rate is insensitive to a stellar population’s age, initial mass function, and metallicity (Renzini & Buzzoni 1986). Therefore, the total number of PNs associated with a parent stellar population can be computed from the bolometric luminosity using the formula: (7)where B is the specific evolutionary flux (stars yrs^-1), L_TOT is the total bolometric luminosity of the parent stellar population, and τ_PN is the PN visibility lifetime. From Eq. (7), the luminosity-specific PN number, α, is (8)Observed values of the α parameter can then be interpreted as different values of τ_PN for the PNs associated with different stellar populations, because variations of B with metallicity or Initial Mass Function (IMF) slope are small (Renzini & Buzzoni 1986).

For any specific observation, the actual value of N_PN depends on the flux limit of the survey in which the PNs are detected. Hence, for our survey depth we are interested in estimating α_2.1, the number of PNs within Δm = 2.1 magnitudes of the bright cutoff, per given amount of bolometric luminosity emitted by the stellar population of the galaxy’s halo or ICL. For any Δm, α_Δm is defined such that (9)where N(m) is the PNLF and M^∗ is the bright cutoff magnitude.

4.3. Halo and intracluster PN population

When studying the PN population in the outer region of M 87 out to 150 kpc from the galaxy’s centre, we expect contributions from the halo PNs and from ICPNs. The presence of ICL in cluster cores is a by-product of the mass assembly process of galaxy clusters (Murante et al. 2004, 2007).

The existence of intracluster PNs in Virgo has been demonstrated on the basis of extended imaging surveys in the 5007 Å [OIII] line (Feldmeier et al. 1998, 2003; Arnaboldi et al. 2002; Aguerri et al. 2005; Castro-Rodríguez et al. 2009) and spectroscopic follow-up (Arnaboldi et al. 1996, 2003, 2004). From the spectroscopic follow-up of Arnaboldi et al. (2004) we know the M 87 halo and the Virgo core ICL to coexist for distances R   >   16′ from M 87’s centre (the FCJ field). In fact, the projected phase space diagram from Doherty et al. (2009) (PN line-of-sight velocity vs. radial distance from M 87 centre) shows the coexistence of the halo and ICL PN population out to 150 kpc.

To estimate the ICL luminosity in our two fields, we assume a constant surface brightness μ_V = 27.7 mag arcsec^-2 (Mihos et al. 2005), and a V-band bolometric correction BC_V = −0.85 (Buzzoni et al. 2006), which then gives a total bolometric luminosity in the ICL of L_ICL = 1.66 × 10¹⁰  L_⊙ ,bol. This amounts to about one quoter of the bolometric luminosity of the M 87 halo.

4.4. Two component photometric model for M 87 halo and ICL, and determination of their α_2.5 parameters

Therefore we now investigate whether the observed discrepancy between the logarithmic PN number density profile and μ_V surface brightness profile in Fig. 7 may be explained by considering two PN populations associated with the M 87 halo and ICL. The physical parameter that links a PN population to the luminosity of its parent stars is the luminosity-specific PN number, the α parameter, thus a discrepancy between μ_V and the measured logarithmic PN number density profile may come from different α values for the halo and ICL stellar population. This possibility is supported by Doherty et al. (2009) who measured two different values of α for the bound (M 87 halo) and unbound (ICL) stellar component, which we are going to label α_halo and α_ICL in what follows. We can then define a photometric model with two components: where (R) represents the predicted PN surface density in units of N_PN   pc^-2, I(R)_halo and I_ICL are the surface brightnesses for the halo and the ICL components, and I_K09 from Kormendy et al. (2009) is the observed total surface brightness from M 87 centre out to 40′, accounting for both halo and ICL components. These surface brightnesses are in units of L_⊙   pc^-2 and their bolometric values are computed from the measured profiles in units of mag  arcsec^-2 via the formula: where BC_V = −0.85 and BC_⊙ = −0.07 are the V-band and the Sun bolometric corrections, and K = 26.4 mag  arcsec^-2 is a conversion factor from mag  arcsec^-2 to physical units L_⊙   pc^-2 in the V-band. Assuming a fixed value of BC_V = −0.85 for every galaxy type has a 10 per cent internal accuracy, i.e. ±0.1 mag, for a range of simple stellar population (SSP) models ranging from irregular to elliptical galaxies (Buzzoni et al. 2006).

The surface brightness profile can be expressed in terms of PN surface density : (12)where μ₀ is a function of the α_2.1,halo parameter: (13)The value of μ₀ is given by the value of the constant offset used in Eq. (5), and it can be fixed by fitting this offset between the observed logarithmic PN surface density and the surface brightness profile, μ_V, at smaller radii (R ≤ 6.8′). From the fitted offset of μ₀ = 16.0 ± 0.1 mag arcsec^-2, we compute the value for α_2.1,halo (Eq. (13)) resulting in α_2.1,halo = (0.63 ± 0.08) × 10^-8 PN . The error on the determined α_2.1,halo is computed from the propagation of the errors on the variables μ₀ and BC_V, the latter having a 10% accuracy.

In Fig. 8 we show the fit of the two component PN model to the observed PN logarithmic number density profile for α_2.1,ICL/α_2.1,halo = 3 and a constant ICL surface brightness of μ_ICL = 27.7 mag  arcsec^-2 (Mihos et al. 2005). The proposed model predicts a flatter slope for the PN logarithmic number density profile than the slope of the V-band surface brightness profile μ_V, as observed. The fitted value for α_2.1,ICL is then α_2.1,ICL = (1.89 ± 0.29) × 10^-8 PN .

Equation (11) shows explicitly that when α_2.1,halo = α_2.1,ICL then the logarithmic PN number density profile should closely follow light.

The measured values of α_2.1,halo and α_2.1,ICL correspond, down to the survey depth, to an estimated ~390 halo PNs, and ~310 ICPNs in the completeness-corrected sample, and to ~230 halo PNs and ~190 ICPNs in the observed sample.

To compute α_2.5 for a PN sample which is not complete to m^∗ + 2.5, but only to a magnitude m_c < m^∗ + 2.5, then α_2.5 is extrapolated by (14)where (15)If we use the derived M 87 PNLF (see Sect. 5) to compute the cumulative number of PNs expected within 2.5 mag from the bright cut off, normalised to the cumulative number in the first 2.1 mag, we obtain Δ_2.1 ≃ 1.7. As a result, the predicted values of α_2.5, for the halo and ICL components are and . The errors on the α_2.5 values also take the uncertainty on the magnitude of the bright cutoff into account.

4.5. Comparison with previously determined α_2.5 values

The α_2.5 values for the M 87 halo and ICL were measured in previous works by Durrell et al. (2002) and Doherty et al. (2009). There are several assumptions that are made when computing these values, hence is important to address them before the actual numbers are compared. As already pointed out in the previous section, when α_2.5 is computed for a PN sample which is not complete to m^∗ + 2.5, then α_2.5 is extrapolated by following Eq. (14).

In the studies of Durrell et al. (2002) and Doherty et al. (2009), the PN samples were complete one magnitude down the bright cutoff of the PNLF. Then, α_2.5 was computed using the extrapolation on the basis of the analytic formula for N(m) by Ciardullo et al. (1989).

In Sect. 5 we derive the M 87 PNLF in the brightest 2.5 mag range, which shows a steeper slope at ~1.5 mag below the cutoff than what is predicted by the analytical formula. If we use the observed PNLF then the cumulative number of PNs expected within 2.5 mag from the bright cut off, normalised to the cumulative number in the first magnitude, is about 2.7 times that predicted by the analytical PNLF. When comparing the actual values for the α_2.5 measured by Durrell et al. (2002) and Doherty et al. (2009), they need to be rescaled by this fraction.

In the current work and in Doherty et al. (2009), α_2.5 values were measured for the halo and ICL separately, using only the PNs associated with each component. In Doherty et al. (2009) the line-of-sight velocity of each PN was used to tag the PN candidate as M 87 halo or ICL. In Durrell et al. (2002) this information was not available. Following Doherty et al. (2009), only 58% of the PN candidates considered by Durrell et al. (2002) are truly ICL. When this correction is applied, then the value measured by Durrell et al. (2002) for the ICPNs is α_2.5,ICL = 1.3 × 10^-8 PN . If in addition we correct this α_2.5 value for the ICL for the different shape of the PNLF, we get α_2.5,ICL,DU02 = 3.6 × 10^-8 PN . This value is ~10% greater than ours, but consistent within the uncertainties and the contamination from continuum sources in the F03 catalogue (see Sect. 3.3).

When we correct the α_2.5 values for halo and ICL from Doherty et al. (2009) by a factor 2.7, we obtain α_2.5,halo,D09 = 8.4 × 10^-9 PN and α_2.5,ICL,D09 = 1.9 × 10^-8 PN . These values are smaller than ours, but consistent within the uncertainties and the colour/detection completeness corrections made here (see Sect. 4.1) but not in Doherty et al. (2009).

4.6. Implications of the measured α_2.5 values

According to the analytical formula of Ciardullo et al. (1989) for the PNLF, α_2.5 equals ~1/10 of the total luminosity-specific PN number α (see also Buzzoni et al. 2006), assuming that PNs are visible down to 8 mags from the bright cutoff (Ciardullo et al. 1989). If we use this analytical function to extrapolate the total number of PNs from 2.5 to 8 mag below the bright cutoff our measured values for α_2.5 translate to total luminosity-specific PN numbers for the two components of α_halo = 1.1 × 10^-7 PN and α_ICL = 3.3 × 10^-7 PN . We can recast these numbers in terms of the PN visibility lifetime τ_PN through Eq. (8), assuming  yr^-1 (Buzzoni et al. 2006). τ_PN is then ≃6.1 × 10³ yr and ≃18.3 × 10³ yr for the M 87 halo and ICL PNs. Note, that these would be lower limits, because at this stage we do not know whether the extrapolation from 2.5 mag below the cutoff to fainter magnitudes follows the analytical formula, or whether it is steeper.

Observationally there is some evidence that α is on average larger for bluer systems (Peimbert 1990; Hui et al. 1993). The measurements of the colour profile in M 87 show a bluer gradient towards larger radii (Liu et al. 2005; Rudick et al. 2010): a fit of the colour profile along the major axis inside 1000″ has a slope of –0.11 in Δ(B − V)/Δlog    (R) (Rudick et al. 2010). The observed PN logarithmic number density profile measured in this work is consistent with the empirical result of a gradient towards bluer colours at large radii. In the proposed model, the gradient is caused by the increased contribution of ICL at large radii, which is bluer than the M 87 halo population.

One can ask whether the metallicity of the parent stellar population may influence the number of PNs associated with a given bolometric luminosity. We are interested in population effects in the advanced evolutionary phases of stellar evolution, as the [OIII] 5007 Å emission and its line width are weakly dependent on the chemical composition of the nebula (Dopita et al. 1992; Schönberner et al. 2010). For stellar populations with the same IMF and ages, Weiss & Ferguson (2009) computed models for the asymptotic giant branch (AGB) for stars between 1.0 and 6.0 M_⊙ with different metallicities. Their results show that the number of AGB stars varies with the chemical composition, because the latter effects the lifetime on the thermally pulsing AGB, with the longest lifetime obtained for metallicity between half and one tenth of Z_⊙. Given that the evolutionary path followed by a star from the end of the AGB to the beginning of the cooling phase of the central white dwarf corresponds to the planetary nebulae phase, it is suggestive to infer that stellar populations with metallicity –0.5 to –0.1 solar may have a larger number of PNs than stellar populations with solar metallicity or higher, for the same bolometric luminosity. The metallicities of ICL stars in the Virgo cluster core were measured by Williams et al. (2007) using colour magnitude diagrams to be between –0.5 to –0.1 solar. If the stars in the M 87 halo have a higher metallicity, we might expect a variation of the luminosity specific PN number in the region of radii where the M 87 stellar halo and the ICL are superposed along the line-of-sight (Doherty et al. 2009), as is quantified for the first time in this work.

5. Planetary nebula luminosity function in the outer regions of M 87

5.1. PNLF

We can now use our large PN sample in the outer regions of M 87 to investigate the properties of the PNLF. To do this we need to take into account that the fraction of detected PNs on our scientific images can be affected by incompleteness, and that this incompleteness is a function of magnitude.

As for the spatial completeness, we quantify this effect by adding a modelled sample of PNs on the scientific images and computing the fraction recovered by Sextractor of this input simulated PN population as function of magnitude (see Table 5 for values). In Fig. 9 we show the PNLF for the extracted candidates corrected for colour and detection incompleteness. We can compare this PNLF with the analytical formula by Ciardullo et al. (1989)(16)where c₁ is a normalisation constant, c₂ = 0.307 and M^∗(5007) = −4.51 mag is the absolute magnitude of the PNLF bright cutoff (Ciardullo et al. 1989). In Fig. 9 the red solid line shows the prediction from the analytical formula for a distance modulus of 30.8, after convolution with photometric errors and normalisation to the brightest observed bins. The comparison of the derived PNLF with the analytical formula shows two differences. First we detect one object with a m₅₀₀₇ = 25.7 that is ~0.6 mag brighter than the expected cutoff for a distance modulus of 30.8. Second we measure a slope in the PNLF at ~1.5 mag below the cutoff that is steeper than what is predicted by the analytical formula in this magnitude range (Ciardullo et al. 1989; Henize & Westerlund 1963).

We discuss the significance of these deviations in turn.

Over-luminous source

– Figure 9 also shows the comparison of the PNLF from our PN sample with the Lyα LF from Gronwall et al. (2007), scaled to our effective surveyed volume. The hatched range gives the uncertainty in the latter. We see that the bright end of the Lyα LF is consistent with the luminosity of the over-luminous object, within the photometric errors. Earlier suggestions that such overluminous objects could be due to a depth effect from Virgo ICL (Jacoby et al. 1990, C98) are not consistent with more recent studies of the ICL in Virgo; see Sect. 5.4. To definitively resolve the question of the nature of this object, whether it is a Lyα emitter or an object in the M 87 halo, requires spectroscopic follow-up.

Steeper PNLF 1.5 mag below the bright cutoff

– First, we carried out a Kolmogorov-Smirnov test to check whether our empirical PNLF can be drawn from the Ciardullo et al. (1989) analytical function, and this possibility is rejected. Next, because our sample covers 0.5° in the M 87 halo, we can investigate the radial variation of the PNLF out to radii of 30′. We compute the PNLF for each of the three PN subsamples associated with the radial bins described in Sect. 4.1, correcting for colour and detection incompleteness, and subtracting the respective contribution from Lyα background objects. These are shown in the upper panel of Fig. 10, where the error bars also account for ~20% cosmic variance in the Lyα density (see Sect. 3.2). We then compare the three PNLFs after normalisation to the total number of objects in each radial bin and carry out a Kolmogorov-Smirnov test to check whether they can result from the same underlying distribution. The probability that these three PNLFs are extracted from the same distribution is high, P_KS > 99%.

These results are significant because the steepening of the PNLF is thus shown to be present in all three radial bins, while we know that the ICPN population contributes mostly to the outermost bin, see discussion in Sect. 4.3, and that any residual contamination of Lyα background emitters is also largest in the outermost radial region. We also checked that the PNLFs in the two Suprime-Cam fields are similar; both show the steepening at faint magnitudes. Hence we must conclude that the observed steepening of the PNLF is an intrinsic property of the PN population (halo and ICL) in the outer regions of M 87.

5.2. Generalised analytical model for the PNLF and distance modulus of M 87 halo

We determine the PNLF for the M 87 halo as follows. From the colour and detection corrected PNLF, shown in the top panel of Fig. 9, we subtract the expected contribution from Lyα emitters. Using the measured values of α_2.5 for the M 87 halo and ICL PN population, we derive the fraction of PN in the M 87 halo, using (17)where L_ICL,   L_Lhalo are given in Table 3 and α_2.5,ICL/α_2.5,halo = 3. We show the PNLF of the halo of M 87 in Fig. 11; error bars include Poisson statistics and a 20% cosmic variance of the Lyα density.

We now attempt to fit this PNLF with a generalised version of the analytical formula reported in Eq. (16). We continue to assume that the bright cutoff near M^∗ is invariant for different PN populations, but we now allow for a free faint-end slope parameter c₂, in addition to varying the parameter c₁ equivalent to sample size. For the fit to the data we use robust non-linear least squares curve fitting, i.e., the IDL routine mpfit (Markwardt 2009), and account for the photometric errors. We derive the following values for the free parameters: c₁ = 2017.2, c₂ = 1.17 and m^∗ = 26.23, for a reduced χ² = 1.01. Figure 11 shows that this model is an excellent fit to the empirical PNLF for the halo of M 87.

The fitted value of m^∗ = 26.23 corresponds to a nominal distance modulus for M 87 of m − M = 30.74 mag, or D = 14.1 Mpc. Since the generalised PNLF formula has not been calibrated and our candidates are not spectroscopically confirmed, we regard this as preliminary and do not give an error on m − M. This value for the distance modulus is ~0.4 mag brighter than that measured with the surface brightness fluctuation method, 31.18 ± 0.07 mag (Mei et al. 2007), and with the tip of the red giant branch, 31.12 ± 0.14 mag (Bird et al. 2010), which correspond to distances of 17.2 ± 0.5 Mpc and 16.7   ±   0.9 Mpc.

5.3. Comparison of the PNLF in the M 87 halo and in the M 31 bulge

Here we compare the observed properties of the PNLF in M 87 with the PNLF in the M 31 bulge which was used to calibrate the analytical formula by C98. The PN population of the M 31 bulge was studied in detail by Ciardullo et al. (1989); it has similar depth to our PNLF, i.e. it is complete 2.5 mag down the bright cutoff of the PNLF. Also, the stellar population in the M 31 has similar metallicity, colour and stellar population age as the population in the inner ~6′ of M 87; see Table 3 for a complete list of the parameters.

We take the PN samples for M 31 and the halo of M 87 and normalise them by the sampled luminosity, then correct for the distance modulus. The results are shown in Fig. 12, where the PNLF for the halo of M 87 is shown before and after the subtraction of Lyα contaminants. For the points where the Lyα contribution is subtracted the error bars take a ~20% cosmic variance of the Lyα density (see Sect. 3.2) into account. Within 1 mag of the bright cutoff, the M 87 PN population has fewer PNs than the PNLF of M 31, i.e., the slope of the PNLF for the halo of M 87 is steeper towards fainter magnitudes.

In old stellar populations, one expects the PN central stars to be dominated by low mass cores M_core ≤ 0.55  M_⊙. Thus it is plausible that the slope of the PNLF should be steeper than predicted by modelling the fading of a uniformly expanding homogeneous gas sphere ionised by a non-evolving star in a single PN (Henize & Westerlund 1963). The comparison between the PNLFs of M 87 and M 31 may indicate that the M 87 halo hosts a stellar population with a larger fraction of low mass cores with respect to the M 31 bulge.

From observations, there is further evidence that the faint end slope of the luminosity function may depend on the parent stellar population. Ciardullo et al. (2004) reported that star-forming systems, like the disk of M 33 and the Small Magellanic Cloud (SMC), have shallower slopes ~1.5 mags below the bright cutoff when compared to older stellar populations such as NGC 5128 or M 31. These shallower slopes correspond to lower values of c₂ in Eq. (16).

5.4. Comparison with previous PNLF distance measurements for M 87

We already referred in Sect. 3.3 to the work of C98 who carried out a PN survey in an area of 16′ × 16′ centred on M 87, and investigated the properties of the PNLF. F03 studied the properties of ICPNs in the Virgo ICL, including an area of 16′ × 16′ centred 14.8′ north of M 87. Both surveys overlap with our current survey, but have a constant zero point offset Δ = 0.3 mag relative to our measured magnitudes (see discussion in Sect. 3.3).

Figure 13 compares the PNLFs of the matched subsamples common to our catalogue and those of C98 and F03, respectively. A residual zero point offset Δ = 0.3 mag has been applied to the C98 and F03 subsamples. With this zero point shift, the empirical PNLFs agree very well within one magnitude of the bright cutoff, and are consistent with a distance modulus of 30.8. Without the zero point shift, the distance modulus obtained from the PNLFs of C98 and F03 would be 30.5. Thus the value obtained for our new M 87 halo PN sample (30.75), which is closer to other determinations (Sect. 5.2), indicates a systematic effect in the C98, F03 photometry, in the sense of brighter m₅₀₀₇ in these samples. This systematic effect can explain and resolve some of the tension between the assumptions and results of C98, F03, and more recent findings on the spatial distribution of ICPNs in the Virgo cluster and the radial extension of the M 87 halo, as discussed now.

C98 find that their empirical PNLF deviates from the analytical formula and link the deviations with the presence of an ICPN population uniformly distributed in the Virgo cluster volume. C98 assume that all PNs at distances larger than R_iso > 4.8′ from the centre of M 87 are ICPNs. Fitting a PNLF to this component, they derive that the ICPN population must extend 4 Mpc in front of M 87; see Fig. 8 and its figure caption in C98 for further details. The depth effect, which is equivalent to a brightening of the PNLF by foreground ICPNs, places the near edge of the ICL population at a distance modulus of 30.3, corresponding to a distance of 11 Mpc.

The assumptions of C98 on the membership of PNe at R_iso > 4.8′ from M 87 to the ICL and on the spatial distribution of the ICL are not supported by the results of the spectroscopic follow-up of Arnaboldi et al. (2004) and the wide area ICPN survey by Castro-Rodríguez et al. (2009). The spectroscopic follow-up by Arnaboldi et al. (2004) showed that in the FCJ field of F03 at 14.8′ from the M 87 centre 87% of the PNs are bound to the M 87 halo, and only 13% are ICPNs. Thus the fraction of ICPNs in regions that are even closer to the centre, as in the C98 R_iso > 4.8′, sample, will be down to a few %. The wide area survey for ICPNs carried out by Castro-Rodríguez et al. (2009) shows that the ICL is associated with only the densest regions of the Virgo cluster, ~0.4 Mpc around M 87. Hence the brightening of the PNLF expected due to foreground ICPNs is <0.1 mag. These results indicate that the C98 PNs at R_iso > 4.8′ are bound to the M 87 halo, with very limited contamination by ICL: thus they are at the distance of M 87 and the brightening of the C98 sample is not caused by volume effects or a contamination by ICPNs. Similar arguments apply to the F03 PN sample.

We therefore conclude that the magnitudes for the C98, F03 PNs sample must be systematically too bright by 0.3 mag, and that our new photometry is reliable.

6. Summary and conclusions

We carried out a deep survey for planetary nebulas (PNs) in two wide fields of 34′× 27′, covering the halo of the cD galaxy NGC 4486 (M 87) in the Virgo cluster with Suprime-Cam at the Subaru telescope. Both fields were imaged through a narrow-band filter centred at the redshifted [OIII]λ5007 Å emission and a broad-band V filter. The surveyed area covers the halo of M 87 out to a radial distance of 150 kpc. This is the largest survey so far for PNs in the M 87 halo in terms of number of detected PN candidates, depth and area coverage.

PN candidates were identified via the on-off band technique on the basis of automatic selection criteria using their narrow-band colours and their two-dimensional light distributions. The final photometric catalogue contains 688 objects, with a magnitude range extending from the apparent magnitude of the bright cutoff at Virgo distance down to 2.2 mag deeper.

We studied the radial number density profile of the PN candidates and compared it with the V-band surface brightness profile of the stellar light in the M 87 halo. The logarithmic PN number density profile shows good agreement with the μ_V surface brightness profile within 13′ from M 87’s centre, but flattens at large radii. At the most distant point the difference to the prediction from the light profile is 1.2 mags. We investigated whether contributions from background Lyα emitters at redshift z = 3.1 can be responsible for the flatter distribution. By using the Lyα LF from Gronwall et al. (2007) scaled to our effective surveyed volume, we constrained the Lyα contribution to be 25% ± 5% of the total sample over the whole surveyed area, and we concluded that it cannot explain the factor 3 more sources in the outer regions that are responsible for the flatter profile.

Stimulated by the early finding of Doherty et al. (2009) who determined different luminosity specific PN numbers for the M 87 halo light and the ICL, we propose a two component model for the PN population, with the ICL contributing a larger number of PNs per unit light. This composite model is consistent with the observed flattening of the logarithmic PN density profile when the luminosity specific PN numbers are and , for the M 87 halo and ICL population respectively.

Because of the large magnitude range of this PN survey, we were able to study the shape of the PNLF in detail and measured a steeper slope at faint magnitudes than what is expected from the analytical formula of Ciardullo et al. (1989). The fit of the generalised PNLF formula (Eq. (16)) to the empirical PNLF of the M 87 halo population gives faint-end slope of c₂ = 1.17 and a nominal distance modulus of 30.74.

The depth of the current survey allowed us to carry out a comparison with the benchmark for PNLF studies, the M 31 bulge. The comparison of the M 87 halo PNLF with the M 31 bulge PNLF, when both are normalised by the sampled bolometric luminosity, shows that the former has fewer PNs at bright magnitudes, and a steeper slope towards the faint end. The steepening of the PNLF at fainter magnitudes is consistent with a larger fraction of PNs with low mass cores. PN evolution models and stellar population measurements at large radii will be needed to understand the stellar population effects that shape the PNLF of the M 87 halo.

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Acknowledgments

We thank the on-site Subaru staff for their support and A. Aguerri and N. Castro-Rodriguez for helping with the routines that were used for the PNs catalogue extraction. We are also thankful to G. Bono and A. Weiss for helpful discussions on stellar evolution in the AGB phase, and to the referee for her/his comments which improved the paper. A.L. thanks J. Elliott, B. Agarwal and G. Avvisati for the support and helpful comments on the paper. M.A.R. would like to thank F. Makata for support in using the Suprime-Cam Data Reduction (sdfred) pipeline. L.C. acknowledges funding from the European Community’s Seventh Framework Programme (FP7/2007-2013/) under grant agreement No. 229517. This research has made use of the 2MASS catalogue data.

References

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