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Mon. Not. R. Astron. Soc.000, 000–000 (0000) Printed 21 December 2010 (MN LATEX style file v2.2)

A comprehensive classification of galaxies in the SDSS: How to telltrue from fake AGN?

R. Cid Fernandes1, G. Stasinska2, A. Mateus1, N. Vale Asari11Departamento de Fısica - CFM - Universidade Federal de Santa Catarina, Florianopolis, SC, Brazil2LUTH, Observatoire de Meudon, 92195 Meudon Cedex, France

21 December 2010

ABSTRACT

We use theWHα versus [Nii]/Hα (WHAN) diagram introduced by us in previous workto provide a comprehensive emission-line classification ofSloan Digital Sky Survey galaxies.This classification is able to cope with the large populationof weak line galaxies that do notappear in traditional diagrams due to a lack of some of the diagnostic lines. A further advan-tage of the WHAN diagram is to allow the differentiation between two very distinct classesthat overlap in the LINER region of traditional diagnostic diagrams. These are galaxies host-ing a weakly active galactic nucleus (wAGN) and “retired galaxies” (RGs), i.e. galaxies thathave stopped forming stars and are ionized by theirhotevolvedlow-massstars (HOLMES).

A useful criterion to distinguish true from fake AGN (i.e. the RGs) is the value ofξ,which measures the ratio of the extinction-corrected Hα luminosity with respect to the Hαluminosity expected from photoionization by stellar populations older than 108 yr. We findthatξ follows a markedly bimodal distribution, with aξ ≫ 1 population composed by systemsundergoing star-formation and/or nuclear activity, and a peak atξ ∼ 1 corresponding to theprediction of the RG model. We base our classification schemenot onξ but on a more readilyavailable and model-independentquantity which provides an excellent observational proxy forξ: the equivalent width of Hα. Based on the bimodal distribution ofWHα, we set the practicaldivision between wAGN and RGs atWHα = 3 Å.

Five classes of galaxies are identified within the WHAN diagram:

• Pure star forming galaxies: log[Nii]/Hα < −0.4 andWHα > 3 Å• Strong AGN (i.e., Seyferts): log[Nii]/Hα > −0.4 andWHα > 6 Å• Weak AGN: log[Nii]/Hα > −0.4 andWHα between 3 and 6 Å• Retired galaxies (i.e., fake AGN):WHα < 3 Å• Passive galaxies (actually, line-less galaxies):WHα andW[N ii] < 0.5 Å

A comparative analysis of star formation histories and of other physical and observationalproperties in these different classes of galaxies corroborates our proposed differentiation be-tween RGs and weak AGN in the LINER-like family. This analysis also shows similaritiesbetween strong and weak AGN on the one hand, and retired and passive galaxies on the other.

Key words: galaxies: active – galaxies: stellar content – galaxies: evolution – galaxies: statis-tics

1 INTRODUCTION

The goal of a classification scheme is to rationally organizea largecollection of objects (animals, plants, books, galaxies) into a fixednumber of smaller classeswithout leavinganyobjectaside, in or-der to help dealing with this large collection. There are obviouslymany ways to classify objects, depending on what propertiesoneis mostly interested in. For galaxies, one could, for example, bemainly interested in the morphology (Hubble 1936), colours (deVaucouleurs 1960), spectral features (Morgan & Mayall 1957) etc.

It is not only the considered object properties that change from oneclassification system to another, but also the philosophiesunderly-ing the classification process (hierarchical or non hierarchical, su-pervised or not supervised, . . . ) all of which have their advantagesand drawbacks. Ideally, a classification scheme should be objectiveand allow easy incorporation of new objects into predefined cate-gories. The last criterion, for instance, is not fulfilled bythe classifi-cations using principal component analysis (Sodre & Cuevas 1994;Connolly et al. 1995). Most importantly, the classification schememust be useful, Hubble’s famous tuning fork being a classical ex-

2 Cid Fernandes et al

ample. Another one is the emission-line classification scheme pio-neered byBaldwin, Phillips, & Terlevich(1981) (see alsoPastoriza1968), which has been widely used over the last three decades.

The advent of large surveys of galaxies providing extensivecollections of homogeneous data sets not easily tractable with con-ventional methods fostered new classification schemes based onautomatic procedures such as neural networks (Folkes, Lahav, &Maddox 1996), k-means cluster analysis of entire galaxy spectra(Sanchez Almeida et al. 2010), or on human-eye analysis of galaxymorphology by hundreds of thousands of volunteers, as in thebeau-tiful Galaxy Zoo project (Lintott et al. 2010).

Emission-line classifications of galaxies are among the easiestto carry out (once the emission line intensities are measured) andallow one to deal with such issues as star formation, chemical com-position or nuclear activity. They have, for example, helped to gaininsight into the nature of warm infrared galaxies (de Grijp et al.1992), of luminous infrared galaxies (Smith, Lonsdale, & Lons-dale 1998), of galaxies in compact groups (Coziol et al. 1998) orinto the connection between active galactic nuclei (AGN) and theirhost galaxies (Kauffmann et al. 2003). The most famous emission-line diagnostic diagram is that based on the [Oiii]λ5007/Hβ vs[N ii]λ6584/Hα line ratios1 (dubbed the BPT diagram afterBaldwinet al. 1981). This diagram was built to pin down the main source ofionization in the spectra of extragalactic objects, and became one ofthe major tools for the classification and analysis of emission linegalaxies in the Sloan Digital Sky Survey (SDSS,York et al. 2000).

As emphasized byCid Fernandes et al.(2010, CF10), theBPT diagram leaves unclassified a large proportion of emission linegalaxies in the SDSS, due to quality requirements on 4 emissionlines. TheKewley et al.(2006) classification is even more demand-ing, as it requires the intensities of as many as 7 lines. To remedythis situation,CF10have proposed a much more economic diagramthat allows one to attain the same objectives using only two lines,Hα and [Nii], which are generally the most prominent in the spec-tra of galaxies. This is a diagram plotting the Hα equivalent widthversus [Nii]/Hα, which was dubbed the EWHαn2 diagram in thatpaper and which we will from now on refer to as the WHAN dia-gram, for simplicity2. The border lines between different classes ofgalaxies in the WHAN diagram were defined by optimal transpo-sitions of theKauffmann et al.(2003), Stasinska et al.(2006, S06)and Kewley et al.(2006) border lines onto theWHα vs [N ii]/Hαplane.

The categories of emission line galaxies considered byKauff-mann et al.(2003) andKewley et al.(2006) comprised star forminggalaxies (SF), Seyferts, and LINERs3 This latter category is sup-posed to comprise objects hosting low-level nuclear activity. How-ever, as demonstrated byStasinska et al.(2008, S08), the LINERregion in the BPT diagram also contains galaxies that have stoppedforming stars and are actually ionized by the hot evolved low-mass

1 In the remaining of this paper, [Oiii]λ5007 and [Nii]λ6584 will be de-noted [Oiii] and [Nii], respectively.2 Our WHAN diagram is, in a way, very similar to theW[N ii] vs [N ii]/Hαdiagram ofCoziol et al.(1998). However, the WHAN diagram is easierto interpret, since it concerns two physically independentquantities,WHα

measuring the amount of ionizing photons absorbed by the gasrelative tothe stellar mass, while [Nii]/Hα is a function of the nitrogen abundance, ofthe ionization state and temperature of the gas.3 In those papers, LINERs should have rather been called “galaxies withLINER-like spectra”, given that LINER is the abbreviation of ”low ioniza-tion nuclear emission regions” (as originally introduced byHeckman 1980),and the SDSS spectra cover much more than the nuclear regionsof galaxies.

stars (HOLMES) contained in them. This idea is not new. It wasalready put forward to explain the emission line spectrum ofel-liptical galaxies (Binette et al. 1994; Macchetto et al. 1996) andeven earlier in different contexts (Hills 1972; Terzian 1974; Lyon1975; Sokolowski & Bland-Hawthorn 1991). This idea is recentlygaining support (Schawinski et al. 2010; Masters et al. 2010; Kavi-raj 2010), especially with the detailed study of nearby galaxies(Sarzi et al. 2010; Annibali et al. 2010; Eracleous, Hwang, & Flohic2010). As shown byCF10, when taking into account the “forgottenpopulation” of weak line galaxies, the proportion of such “retiredgalaxies” (RGs) increases dramatically. It is therefore essential tofind ways to distinguish these RGs from truly “active” galaxies, i.e.galaxies which contain a (weak) AGN. This is one of the goals ofthe present paper.

Our second goal is to link the universe of emission line galax-ies (ELGs) with the universe of galaxies without emission lines (of-ten dubbed “Passive Galaxies”, PG). We will see that RGs and pas-sive galaxies are in fact very similar objects.

With our WHAN diagram, we will thus be able to provide acomprehensive classification of all SDSS galaxies, including PGs.

The paper is structured as follows: Section2 presents thesamples of galaxies used to work out our classification and ourstarlight data base from which the properties of galaxies that weuse are extracted. Section3 lists the initial definition of galaxyclasses: SF, Seyferts, LINERs and PGs. Section4, the core of thispaper, sorts out RGs from AGN among LINER-like galaxies andproposes a practical criterion to identify them. Section5 describesour comprehensive classification of galaxies based on the WHANdiagram and leading to five classes: SF, strong AGN, weak AGN,RG and PG. Section6 examines the star formation histories ofgalaxies within our new classification scheme, placing RGs in thecontext of the galaxy population in general. Section7 discusses thedistributions of a series of physical and observed properties in thedifferent galaxy classes, to test the pertinence of our new classifi-cation. Finally, Section8 summarizes our main results.

2 SAMPLES AND DATA PRODUCTS

We draw from the nearly 700 thousand galaxies from the MainGalaxy Sample (Strauss et al. 2002) in the 7th data release ofthe SDSS (Abazajian et al. 2009). Besides the SDSS data, wemake use of a suite ofstarlight-derived products available atwww.starlight.ufsc.br, like stellar masses, stellar extinctions andthe full star formation histories (Cid Fernandes et al. 2005). Emis-sion line properties are measured after subtraction of the underlyingstellar component determined with thestarlight fit, as described inMateus et al.(2006).

Two samples are used throughout this work. The “full sample”(hereafter “sample F”) is selected by requiring(i) a signal-to-noiseratio > 10 in the continuum around 4750 Å,(ii) no faulty pixelswithin ±15 Å of Hα and [Nii], (iii) z < 0.17 to eliminate the fewobjects at higher redshift which survive the previous criterion, and(iv) z > 0.04 to minimize apertures effects. In total, sample F con-tains 379330 galaxies.

Sample V is built from a volume limited sample satisfying0.04 < z < 0.095 and absolute r-band magnitudeMr < −20.43, towhich we further apply criteria(i) and(ii) above. These latter cutsimply only a 6 percent reduction, with little loss of completenessat the faint end, so this sample is effectively limited in volume.Sample V comprises 152749 objects. By choosingz < 0.095 we

How to tell true from fake AGN? 3

eliminate cases where Hα and [Nii] fall in a region of strong skylines, so that criterion(ii) is fulfilled.

These samples fulfill the basic requirements for this work.Condition(i) ensures meaningful spectral fits, necessary for stellarpopulation analysis and the production of templates to aid emissionline measurements, while(ii) ensures that the detection of Hα and[N ii] (the most important lines in this study) is not hampered by ar-tifacts like bad pixels or sky residuals. At the same time, neither ofthese samples is biased for or against the presence of emission lines,which allows us to treat systems with strong, weak or no emissionlines on an equal footing.

Sample F has the advantage of being more numerous and ofextending to lower luminosities, while sample V is more adequatefor demographic studies and unbiased comparisons among sub-samples. The novelties in this work, namely the criterion tosepa-rate true from fake AGN and the resulting revision of conventionalspectral taxonomy, are mainly related to the most luminous galax-ies, so that, in practise, we could use either sample V or F. Inwhatfollows, unless stated otherwise, we use sample V.

3 PRELIMINARY GALAXY CLASSES

We sub-divide galaxies into different classes according to theiremission line properties. The first division is among galaxies withand “without” emission lines, denoted ELGs and PGs, respectively.

3.1 Passive galaxies

PGs are defined as those with very weak or undetected emissionlines. Galaxies are deemed passive if the equivalent widths(Wλ) ofboth Hα and [Nii] fall below 0.5 Å, a criterion which applies to 19(20)% of sample V (F). Since these are the two strongest opticallines, this criterion automatically implies that other lines will beeven weaker in general (e.g.Brinchmann et al. 2004; CF10).

In the literature one often finds PGs and ELGs defined in termsof limits on the signal to noise ratio (S Nλ) of emission lines (e.g.Miller et al. 2003; Brinchmann et al. 2004; Mateus et al. 2006). Weconsider aWλ-based criterion more appropriate, as it is based ona more direct measurement of the line strength and less explicitlydependent on the quality of the data. In additionWλ’s have an as-trophysical meaning. We emphasize that the adopted limiting valueof 0.5 Å is not really meaningful. As will be seen later, none ofthe discussion presented in this paper would have changed ifhadwe adopted a slightly different limit. We also note in passing thatthe denomination “Passive” for “line-less” systems is adopted justfor compatibility with current nomenclature, with no evolutionaryconnotation as the word would suggest.

3.2 Emission Line Galaxies: SF, Seyferts and LINERs

ELGs are defined as everything else, i.e., any source where eitherWHα or W[N ii] is> 0.5 Å. Notice that this stretches the definition ofan ELG to its limit, since, in extreme cases, only one of Hα or [N ii]is detected! Sample V (F) has 124410 (303194) ELGs, 94 (92)% ofwhich have both lines stronger than 0.5 Å.

ELGs are normally subdivided into SF and AGN-like classeson the basis of diagnostic diagrams involving at least 4 lines, likethe BPT diagram ([Oiii]/Hβ versus [Nii]/Hα). As shown inCF10,the weakness of either or both of Hβ and [Oiii] implies that suchtraditional classification schemes leave large numbers of ELGs un-classified. This problem is particularly severe in the rightwing of

Figure 1. The WHAN diagram, used to establish emission line classes. Theline labelled S06T represents the optimal transposition of theS06SF/AGNspectral classification scheme, while the line atWHα = 6 Å represents thetransposed version of theKewley et al.(2006) Seyfert/LINER class divi-sion. Dotted lines markWHα = 0.5 andW[N ii] = 0.5 Å limits, below whichline measurements are uncertain. Passive galaxies (WHα andW[N ii] < 0.5Å, red dots) lie below the dashed line. Dots correspond to sample F andcontours to sample V. Contours are drawn at number densities= 0.1, 0.2,0.4 and 0.6 of the peak value. As in other figures in this paper,only 1 tenthof theobjectsisplotted toavoidovercrowding.

the BPT diagram, where AGN-like systems reside, but well overhalf of the galaxies have unusable Hβ and/or [O iii] fluxes. In thatsame paper we introduced alternative diagnostic diagrams able tocope with galaxies with weak or undetected Hβ and/or [O iii], themost economic of which isWHα versus [Nii]/Hα, hereafter theWHAN diagram. We base all our classifications on this diagram,which, unlike any other, allows us to classifyall galaxies.

The WHAN diagram is shown in Fig.1. The vertical lineat log[Nii]/Hα = −0.40 corresponds to the optimal transpositionof the S06BPT-based SF/AGN division, designed to differentiatesources where star formation provides all ionizing photonsfromthose where a harder ionizing spectrum is required. Similarly, thedivision atWHα = 6 Å represents an optimal transposition of theKewley et al.(2006) division between Seyferts and LINERs on thebasis of 7 emission lines. UnlikeKewley et al.(2006) and otherstudies (e.g.,Kauffmann et al. 2003; Mateus et al. 2006), we do notdefine a “SF+AGN composite” category, and so our Seyfert/LINERborder line goes all the way to the SF/AGN frontier. (The reader isreferred toCF10for a discussion of the pros and cons of this dia-gram, as well as for a discussion of why we prefer not to define acomposite category.) Sample V splits into percentage fractions of23, 21 and 37 SF, Seyferts and LINERs, respectively. PGs accountfor the remaining 19%. The corresponding fractions for sample Fare very similar: 25, 19, 36 and 20%.

The dotted lines in Fig.1 correspond to min(WHα,W[N ii]) =0.5 Å. Points below these lines (painted in orange or red) are thus

4 Cid Fernandes et al

very uncertain4, but are nevertheless plotted to remind the reader ofthe existence of such objects. There is indeed some advantage in in-cluding line-less galaxies in an emission line classification schemesince the absence of emission lines is also an information! The re-gion below the dotted lines includes both ELGs with only one line> 0.5 Å, as well as PGs (below the dashed lines, in red) for whichwe have Hα and [Nii] fluxes in our database.

4 IDENTIFYING RETIRED GALAXIES

Ideally, splitting ELGs into SF and AGN would separate systemswhose emission lines are powered exclusively by young starsfromthose where accretion onto a central super-massive black hole dom-inates (or at least contributes to) the ionizing radiation field. How-ever, as reviewed in the introduction, this binary scheme does notaccount for a third phenomenon: photoionization by HOLMES.These Retired Galaxies (RGs) are numerous in large-scale surveys(S08), where they occupy LINER-like locations in the commonlyused diagnostic diagrams, and are thus erroneously associated tonon-stellar activity.

This section investigates ways of identifying RGs. We startwith a theoretically motivated definition, but end up with a verysimple empirical criterion to diagnose RGs among ELGs.

4.1 Ionizing photons budget: Theξ ratio

By definition, RGs are systems whose old stellar populationssuf-fice to explain the observed emission line properties. Sincewe al-ready know that nebulae photoionized by HOLMES cover mostregions in intensity ratios diagrams (S08), we shall adopt an opera-tionally simple energy-budget criterion and define RGs asgalaxieswhoseold stellarpopulationsarecapableof accountingfor all oftheobservedHα emission.

Let LintHα be the intrinsic (extinction corrected) Hα luminosity

of a galaxy, andLexpHα (t > 108yr) denote the luminosity expected

from photoionization by populations older than 108 yr. The ratio

ξ =Lint

Hα

LexpHα (t > 108yr)

(1)

should thus be able to tell if a galaxy is retired (ξ 6 1) or whethersome extra source of ionizing photons is present. Given the ordersof magnitude difference between the ionizing photon rates of youngand old populations, even tiny amounts of ongoing star formationsuffice to swamp the ionizing field produced by HOLMES, leadingto ξ ≫ 1 and to a SF-like emission-line pattern due to the softerionizing spectrum. Similarly,ξ will also grow above 1 in the pres-ence of an AGN. In fact, as long as a galaxy has enough gas toabsorb allhν > 13.6 eV photons produced within it,ξ should neverdrop below 1.

This way of identifying RGs was previously employed byS08.In what follows we open up the details and discuss the caveatsre-lated to the estimation ofξ.

4 Our line fitting code, described inMateus et al.(2006), only accepts fitswith Wλ > 0.1 Å, which produces the empty triangle in the bottom left partof the plot.

4.2 Computation of the expected Hα luminosity

Neglecting escape and extinction of Lyman continuum radiation,the denominator in eq.1 becomes

LexpHα (t > 108yr) =

hνHαfHα× Qexp

H (t > 108yr) (2)

where fHα = 2.226 for case B hydrogen recombination, andQexpH is

the expected ionizing photon rate. Thestarlight spectral decompo-sition in terms of a base of simple stellar populations (SSPs) allowsus to estimateQexp

H for each of our galaxies. The code returns themass associated to each ofN⋆ SSPs of different ages (t) and metal-licities (Z), so that the expected flux of ionizing photons from thepopulations of interest here is simply

QexpH (t > 108yr) = M⋆

∑

j;t j>108yr

µ jqH, j (3)

whereM⋆ is the total stellar mass formed,~µ is the mass-fractionpopulation vector, andqH, j = qH(t j ,Z j) is the number of H-ionizingphotons emitted per unit time and initial mass for thej th SSP. Sincethe expected Hα luminosity is to be compared to the one observedwithin the 3′′aperture of the SDSS spectroscopic fiber, the stellarmass in this equation corresponds to that inside the fiber (i.e., wedo not extrapolate to account for the whole galaxy). In fact,all ouranalysis up to Section6 uses within-the-fiber data only.

The starlight fits used in this work employ the same base ofSSPs fromBruzual & Charlot(2003, hereafter BC03) described byMateus et al.(2006). Naturally, the spectral fits are carried out inthe optical range, but theBC03models also provide predictions forthe ionizing energies. This is a key element of our analysis,so itis worth to open a parenthesis to examine these predictions moreclosely.

4.3 SSP models beyond 1 Rydberg

Fig.2 shows the evolution of SSP properties of interest to this work.Panels on the left (a–d) correspond toBC03models of 6 differentmetallicities used in our analysis. These are discussed first.

Fig. 2a shows the evolution of the hardness of the ionizingspectrum, as measured by the ratio of HeI to HI ionizing photons.The difference between the soft ionization field produced by OBstars (t < 107 yr) and that produced by HOLMES is clearly visible.Panel b shows the specific ionizing photon rate,qH(t,Z), the mostrelevant quantity for our analysis. One sees the well known dropby ∼ 5 orders of magnitude inqH from the HII region phase to thetime when the HOLMES regime sets in at 108 yr andqH approxi-mately stabilizes at a level of the order of 1041 s−1 M−1

⊙ . The specificcontinuum around Hα, cHα (equivalent to the inverse of the mass tolight ratio,κHα), however, keeps decreasing with time (panel c) andtherefore the predictedWHα (bottom panel) goes through a mini-mum at 108 yr and grows to 1.5–2.5 Å att & 1010 yr. This is enoughto explain a significant proportion of the ELGs in the LINER zoneof Fig. 1.

The right column of panels Fig.2 shows the predictions ob-tained for other evolutionary synthesis models at solar metallicityandChabrier(2003) initial mass function (IMF, the same one usedfor the BC03 models on the left panels). Red lines show results fora preliminary version of Charlot & Bruzual models. Blue lines areused for Popstar models (Molla, Garcıa-Vargas, & Bressan 2009)with Lejeune, Cuisinier, & Buser(1997) library and NLTE mod-els byRauch(2003) and “Padova 1994” tracks (version updated byMolla & Garcıa-Vargas 2000). Green lines represent Pegase models

How to tell true from fake AGN? 5

Figure 2. Evolution of the ratio between the Hei and Hi ionizing photon rates (panel a), Hi-ionizing photon rate (b, in units ofs−1M−1⊙ ), Hα continuum (c, in

units of L⊙Å−1M−1⊙ ), and Hα equivalent width (d) for simple stellar populations in theBC03models of metallicities betweenZ⋆ = 0.005 and 2.5Z⊙. Panels

on the right areZ⊙ models from 5 different sources (includingBC03for comparison). See text for details. The right axis scale in panels c and g gives the massto light ratio in the Hα continuum:κHα = c−1

Hα (in units ofM⊙L−1⊙ Å).

(Fioc & Rocca-Volmerange 1997) with their 200 Å–10µm stellarlibrary (see their section 2.2.1 for more details). Two setsof evolu-tionary tracks are shown for Pegase: Light green is used forBres-san et al.(1993) tracks up to TP-AGB, dark green shows the set forSchaller et al.(1992) andCharbonnel et al.(1996) tracks. Both setsuseSchoenberner(1983) andBloecker(1995) for the PAGB. Forreference, theBC03predictions forZ⊙ are repeated (black lines).

Despite a general qualitative agreement, there are significantquantitative differences. For ages above 108 yr, the hardness ra-tio varies from 10 to 20%, whileqH differs by up to 1 dex fromone model to another. Those differences could be due to differ-ences in stellar tracks, model atmospheres and even interpolationtechniques. ForWHα, the greatest difference is above 109 yr forthe BC03 models, which are∼ 0.5 Å larger than the others. Wehave also compared different IMFs (e.g.,Salpeter 1955andKroupa2001). For a given metallicity in a model, different IMFs result invariations from 0.2 up to 0.5 dex inqH andcHα, which is typicallyof the same order of variations for different models with the sameIMF.

For the sake of internal consistency with ourstarlight anal-ysis of SDSS galaxies, in what follows we adopt the predictionsfrom BC03, but this quick compilation illustrates that there are stillplenty of uncertainties in model predictions for the Lyman contin-

uum of old stellar populations. Naturally, these uncertainties prop-agate to our estimate ofξ. Fortunately, there is an empirical alterna-tive to circumvent the uncertainties related to this choice(Section4.8).

4.4 Extinction correction

Let us now turn to the numerator of eq. (1). The caveat in this caseis that a nebular extinction is needed to correctLobs

Hα to LintHα, which

in turn requires good measurements of both Hα and Hβ. While thisis not a problem for ELGs in the SF and Seyfert categories, mostof the LINER-like systems do not comply with this requirementbecause of their feeble Hβ.

One way to circumvent this difficulty is to estimate Hα/Hβ interms of something else. To do this, we first select LINERs with re-liable Hα/Hβ, and then calibrate the relation between nebular (Aneb

V )and stellar (A⋆V) extinctions (as done byAsari et al. 2007for SFgalaxies), such thatA⋆V can be used to estimateAneb

V . In practice,we imposeWHα and WHβ > 0.5 Å, and Hα/Hβ > 3 (such thatAneb

V > 0)5, which leaves us with only∼ 30% of the full list of LIN-

5 For the Cardelli et al. (1989) law used in our analysis,AnebV =

6 Cid Fernandes et al

ERs. Nebular and stellar extinctions are indeed correlatedfor thissub-sample (Spearman rank correlation coefficientRS = 0.38), al-beit with substantial scatter. A fit through the median relation yieldsAneb

V = 1.56A⋆V + 0.53, with typical residuals of∼ 0.5 mag. Objectsused in this calibration haveAneb

V = 1.0± 0.5 (median± semi inter-quartile range), while for LINERs as a whole the above relationleads toAneb

V = 0.8 ± 0.2. This method most likelyoverestimatesAneb

V , as there is a trend of increasingAnebV with increasingWHα, and

Hα is much weaker in the full sample than in the sub-set used tocalibrate theAneb

V (A⋆V) relation (WHα = 1.5 versus 3.7 Å in the me-dian, respectively).

Another alternative is to apply the extinction correction whenthe data allow (WHα andWHβ > 0.5 Å, and Hα/Hβ > 3), and as-sumeAneb

V = 0 otherwise.These methods lead to estimates ofξ which should bracket the

correct solution. SinceAnebV = 1 implies a 0.32 dex increase inLint

Hα,for the typical inferredAneb

V values these two methods should differby ∼ 0.2–0.3 dex inξ.

4.5 Results: The bimodal distribution ofξ

The ξ values derived range from∼ 0.1 to 1000. This is illustratedin Fig. 3a, which shows the distribution ofξ values for all ELGs insample V. The distribution is stronglybimodal, with a highξ peakat∼ 30 and a lower one centred close toξ = 1.

The two histograms in Fig.3a differ in the recipe to correctLobs

Hα for extinction when Hα/Hβ is unreliable. The dotted curve isfor no correction, whereas the solid one is for theAneb

V (A⋆V) methoddiscussed above. The curves are identical forξ > 10 because theseare Seyfert and SF galaxies, with Hα and Hβ fluxes good enoughto apply the standard correction. In the lowξ peak the two methodsdiffer by just 0.2 dex on the median, as anticipated. Consideringthe smallness of this difference and that these recipes are likely tobracket the range of possibilities, we henceforth use the averageξ,whose distribution peaks atξ ∼ 1.

Overall, the lowξ population is unbelievably close to the ex-act prediction for RGs:ξ = 1! But are these galaxies really retiredin the sense of not having formed stars for a long while? To an-swer this question, Fig.3b shows the histograms obtained selectingonly galaxies whose populations younger than 108 yr contributesno more thanX8 = 10% of the light at 4020 Å.6 Clearly, the lowξpeak is made up of galaxies which have formed little or no stars inthe past 108 yr, in full agreement with the RG scenario ofS08.

The width of the RG peak in theξ distribution (as measuredfrom the one-sided dispersion of its lower half, and assuming thatthe distribution is symmetric) isσlogξ ∼ 0.24 dex for the full ELGsample or 0.13 dex eliminating those withWHα < 0.5 Å. From theconsiderations in the above sections, one would hardly expect ξ tobe more precise than a factor of 2, so that this observed dispersioncan be accounted for by uncertainties in the data and the analysis.

While theory says that RGs should haveξ 6 1, theobservedbimodality in theξ distribution suggests thatξ < 2 or 3 is a more

7.215 log (Hα/Hβ)obs(Hα/Hβ)int

. We adopt an intrinsic Balmer decrement of 3, suitablefor LINERs (S08).6 To have an idea of what this means,X8 < 10% corresponds to specificstar formation rates. 2.5× 10−12 yr−1, or, equivalently, to less than 0.025percent of the stellar mass having formed in the past 108 yr. In fact, for lightlevels as low asX8 = 10% the spectral synthesis analysis is affected bothby its own limitations and by side-effects of incompleteness in evolutionarysynthesis models, such that the true contribution from young stars cannotbe reliably estimated (see Section6).

Figure 3. Histograms of the ratio between the intrinsic Hα luminosityLHα

to that predicted to originate from photoionization by HOLMES (equation1) for ELGs in our volume limited sample, and for two choices ofextinctioncorrection for objects with unreliable Hα/Hβ: estimateAneb

V from A⋆V (solidlines), and no-correction (dotted). Panel (a) includes allsources, while in(b) we select galaxies whose populations younger than 108 yr contributeless thanX8 = 10% to the continuum atλ = 4020 Å. Panel (c) shows theξdistribution which results from requiring convincing detections of Hβ and[O iii], as well as Hα and [Nii].

effective way of separating RGs from the rest of the ELG popu-lation (Fig.3a). About 1/3 of the full ELG sample hasξ < 2. Thereason why this huge population of RGs has received little attentionto date is because standard selection criteria tend to exclude themfrom emission line studies. Fig.3c illustrates how the distributionof ξ changes by imposing aWλ > 0.5 Å quality control for the 4lines in the BPT diagram: Hβ, [O iii], Hα and [Nii]. This reducesthe ELG sample as a whole by∼ 1/3, but the fraction ofξ < 2sources decreases by a factor of 3. The bimodality is still present,but the lowξ peak is heavily suppressed, so that RGs are the mainvictims of this cut. With the WHAN diagram we are able to res-cue RGs and weak line galaxies in general, eliminating this biasand thus drawing a much more complete view of ELGs in the localUniverse.

4.6 Results:ξ versus [Nii]/Hα

Fig. 4 shows the results of this analysis in aξ versus [Nii]/Hα di-agram, color coding SF, Seyferts and LINERs according to theirlocation on the WHAN diagram (Fig.1). The plot shows that SF,

How to tell true from fake AGN? 7

Figure 4. ELGs in theξ versus [Nii]/Hα diagram, color coding PGs, LIN-ERS, Seyferts and SF, as in Fig.1. Also as in Fig.1, gray points are sourceswith either or both of Hα and [Nii] weaker than 0.5 Å. Solid contours arefor the full sample V, while the dashed ones are for galaxies with less thanX8 = 10% of their flux atλ = 4020 Å due to populations younger than 108

yr (for clarity, only the two outer contours are plotted in this case). Dot-ted horizontal lines mark a range of×2 around the prediction of the retiredgalaxy model (ξ = 1).

Seyferts and LINERs are well separated in terms ofξ and [Nii]/Hα,with SF and Seyferts being responsible for theξ ≫ 1 peak andLINERs for theξ ∼ 1 RG-like population. The plot also illustrateshow dangerous it is to define a galaxy as an AGN on the basis of[N ii]/Hα alone.Miller et al. (2003), for instance, define “2-linesAGN” as sources with log[Nii]/Hα > −0.2 (similar to the “lowS/N AGN” in Brinchmann et al. 2004), while our analysis suggeststhat most of this population does not require an energetically sig-nificant non-stellar source of ionizing photons.

The dashed contours in Fig.4 show the location of ELGs withX8 < 10%. This clarifies that the bulk of the population of largeξ sources among galaxies with a predominantly old stellar popu-lation (seen in the middle panel of Fig.3) is composed of LIN-ERs approaching the Seyferts zone. Further considering that theX8 cut eliminates sources with significant on-going star formation,this strongly indicates that these sources have a non-stellar sourcepowering most of its line emission (i.e., they are AGN in old hosts).

We note in passing a tiny population of galaxies with smallξ(i.e. consistent with our RG model) but located to the left oftheS06line in Fig. 4. These could correspond low-mass, low-metallicitygalaxies in a quiescent phase between bursts of star formation, suchas the galaxies discussed bySanchez Almeida et al.(2008).

4.7 ξ versusWHα

An important aspect of Fig.4 is its striking similarity to theWHAN diagram of Fig.1. This happens becauseξ and WHα arevery closely related. The equivalent width of Hα is defined asWHα = Lobs

Hα/CobsHα , where the denominator is the observed contin-

Figure 5. Relation betweenξ and WHα. The bottom panel (c) shows theaverageξ value discussed in Section4.4, while in the middle one (b) noextinction correction correction was applied toLHα. Points in cyan (or-ange) correspond to galaxies where Hα/Hβ is (is not) reliable. The blacksolid line panels b and c shows the median relation, while thedashedlines represent the 10 and 90% percentiles . In all projectedhistograms theblack line represents all ELGs, while the filled region represents those withlog[N ii]/Hα > −0.4 (i.e., to the right of the S06T line in the WHAN dia-gram), and the dashed red line indicates PGs for which a (necessarily smalland uncertain) Hα flux is available. The arrow in theWHα distribution marksthe proposed limit to separate RGs from other ELGs.

uum luminosity around Hα7. Hence, extinction corrections aside,WHα andξ (equation1) have the same numerator.

BecauseqH(t,Z) varies little witht or Z after 108 yr (Fig. 2band f), and since most of the mass is always in the old popula-tions (over 99% for our samples),Qexp

H (t > 108yr) in equation (3)

7 CHα is not to be confused with the continuum luminosity per unit mass,cHα, introduced in Section4.3. We use capital letters for extensive quantities(like CHα andQH ), and lower case for specific ones (cHα andqH ).

8 Cid Fernandes et al

becomes∼ M⋆qoldH , whereqold

H , the specific ionizing flux due toHOLMES, is of the order of 1041 s−1M−1

⊙ . M⋆ can be written interms of the intrinsic continuum at Hα and the galaxy’s luminosityweighted mass-to-light ratio:M⋆ = Cint

Hα〈κHα〉L, whereκHα = c−1Hα

was previously discussed in Section4.3. While κHα spans nearly 3decades for SSPs between 106 and 1010 yr (Fig.2c), galaxies are notSSPs, and young (lowκHα) populations rarely make a substantialcontribution to the Hα continuum, so that the luminosity weightedκHα values are of the same order for most sources. For the V sample,for instance, log〈κHα〉L = 4.56± 0.14 (median± semi-interquartilerange), while sub-samples of SF, Seyferts, LINERs and PGs have4.35±0.12, 4.47±0.08, 4.65±0.08 and 4.70±0.06, respectively (inunits of M⊙ L−1

⊙ Å). As a result,LexpHα is directly proportional to the

Hα continuum, with little scatter due to variations in the starforma-tion histories, and thus the denominators ofξ andWHα essentiallymeasure the same thing.

With these approximations, one finds that

ξ =WHα

WoldHα

100.4(AnebHα −A⋆Hα) (4)

where theAnebHα − A⋆Hα term comes from the fact thatξ accounts for

possible differences in the nebular and stellar extinctions, whereasWHα does not, and

WoldHα ≡

hνHαfHα〈κHα〉Lqold

H = 0.355Å

[

〈κHα〉L

104

] [

qoldH

1041

]

(5)

is the expected equivalent width for an old population (for〈κHα〉Lin units of M⊙ L−1

⊙ Å, andqoldH in s−1 M−1

⊙ ).Fig. 5 shows the observed relation betweenξ andWHα. The

bottom panel (c) shows the dust correctedξ value. Points in cyanand orange correspond to galaxies where Hα/Hβ is or is not reli-able, respectively. The correlation is very tight, as indicated by the10 and 90% percentile lines (in dashed). However, the differentialextinction correction toξ creates a hump starting aroundWHα ∼ 2Å, coinciding with the transition between Hα/Hβ-based correctionsand those based on other methods (see Section4.4).

Fig. 5b shows the same relation assuming that Hα and itscontinuum are equally affected by dust (Aneb

V = A⋆V), so that thelast term in equation (4) disappears, and so does the hump in Fig.5c. The relation is linear except at highWHα, where it steepensbecause of the decrease in〈κHα〉L in galaxies with lots of star-formation. A fit through the median curve in its lowWHα endyields logξobs = 1.07 logWHα − 0.17. Forcing a linear fit leads toWold

Hα = 0.67 Å. For comparison, this value ofWoldHα is reached for

ages of a few Gyr for theBC03SSPs, or closer to 10 Gyr for othermodels (bottom panels in Fig.2).

4.8 Retired Galaxies: An empirical diagnostic

This section started with the promise of finding a diagnosticto iden-tify RGs. Theξ ratio was introduced with this purpose, and we haveseen that it is indeed capable of capturing in a single numbertheessence of RGs. However, there are caveats related to this index.Firstly, uncertainties related to the extinction correction affectξ ata level of±0.2 dex. Secondly, and far more important,ξ is explicitlydependent on evolutionary synthesis models, whose predictions forthe ionizing fluxes from old stellar populations are still plagued bysystematic uncertainties (Section4.3, see alsoS08). Hence, while itis clear that RGs should populate the bottom of theξ-scale, theoryalone does not allow us to establish a firm upper limit below whicha galaxy can confidently be considered as retired. A last caveat is

that the evaluation ofξ requires a stellar population analysis, whichis not always available or possible.

Fortunately, there is a simple empirical alternative to circum-vent these difficulties.

As seen in Fig.5a, and in agreement with previous studies(e.g,Bamford et al. 2008), the distribution ofWHα is strongly bi-modal, with a low peak centred atWHα = 1 Å, an upper one at16 Å, and an intermediate minimum in the neighbourhood of 3 Å.This happens both for the sample as a whole and for the sub-setofAGN-like galaxies, shown by the filled histogram. This bimodalitystrongly suggests that different mechanisms are at work: Photoion-ization by HOLMES on the lowWHα side and by massive starsand/or AGN for largeWHα. One can therefore use the observedWHα

distribution to set an empirical bound for RGs. Of course, the samecan be done for theξ distribution, but it is clearly advantageousto draw this boundary on the basis of an universally available andmodel-independent quantity:WHα.

We conducted various experiments to define an optimalboundary to separate the two peaks, all giving results between 3and 4 Å. We settled forWHα = 3 Å. We thus propose to the follow-ing practical definition:RGsareELGswith WHα < 3 Å.

For WHα < 3 Å, the central black hole may well be active(i.e., accreting), but its ionizing photon output is comparable to orweaker than that produced by HOLMES. ForWold

Hα values between 1and 2 Å, which are compatible with current models (bottom panelsin Fig. 2), one finds that at our proposedWHα = 3 Å borderlinethe AGN contributes between∼ 1/3 and 2/3 of the ionizing power.Naturally, this fraction decreases even more asWHα decreases, tothe point that AGN contribution is negligible for the bulk ofthelow WHα population. It is therefore not correct to use the emissionlines ofWHα < 3 Å systems to infer AGN properties.

5 A COMPREHENSIVE CLASSIFICATION OFGALAXIES

We are now able to separate fake AGN (= RGs) from true AGN. Inthe LINER zone of Fig. 1, true AGN are defined by 3 Å<WHα < 6Å. To avoid confusion, we shall call these sources “weak AGN”(wAGN). In the interest of a consistent notation, we shall also re-name Seyferts as “strong AGN” (sAGN). Finally, we remove fromthe SF category those galaxies consistent with a RG classification,that is to say those havingWHα < 3 Å.

Our final classification scheme is shown in Fig.6. Sample Vsources split into percentage fractions of 22, 21, 8, 31 and 18 SF,sAGN, wAGN, RG and PG, respectively. RGs therefore exist inlarge numbers in the SDSS, in agreement with the basic predictionthat such systems are bound to exits as a mere consequence of stel-lar evolution. AGN, on the other hand, are far less common thanone would infer associating all LINERs to non-stellar activity.

This revised classification scheme has three main virtues:

(i) It identifies the three main ionizing agents in galaxies:youngstars, AGN and HOLMES.

(ii) It is based on the cheapest and therefore most inclusivedi-agnostic diagram, the only one able to handle the huge populationof weak line galaxies (CF10). In fact, it even allows us to visualizea region occupied by PGs, something which is not possible in con-ventional intensity ratio diagnostic diagrams (of course,galaxieswith no emission lines at all do not appear in this diagram).

(iii) The SF/AGN and wAGN/sAGN frontiers are based on opti-mal transpositions of widely used demarcation lines definedon the

How to tell true from fake AGN? 9

Figure 6. The WHAN diagram with the revised categories: SF, sAGN,wAGN, RGs and PGs. Points with one of [Nii] or Hα weaker than 0.5 Åare plotted in gray, except for PGs, which are plotted in red.

basis of traditional diagnostic diagrams, such that the WHAN dia-gram preserves traditional classification schemes where they workwell, while at the same time fixing their inability to deal with weakline galaxies and to distinguish true from fake AGN.

Before exploring the realm of galaxies under the perspectiveof this new classification scheme, a few clarifications are inorder.

First, notice that our SF/AGN border line corresponds to thetransposition of theS06 BPT-based criterion to isolate systemspowered exclusively by young stars from those where a harderion-izing source is required. As such, it would have been better to callit a “pure-SF” demarcation line, instead of a “SF/AGN” divisoryline. This is all the more true now that we realize that the AGN-likepopulation of ELGs in the BPT is full of retired galaxies whoseblack holes are not active enough to affect their emission lines.

Secondly, notice that theWHα < 3 Å criterion slightly modifiesour idealized definition of RGs as “systems whose old stellarpop-ulations suffice to explain the observed emission line properties”(Section4.1) to one where HOLMES make at least a substantialcontribution to the ionizing field. Such a fuzzy frontier is unavoid-able. Our criterion is designed to avoid RGs being misclassified asAGN. On the other hand, some galaxies hosting an AGN, partic-ularly those containing a weakly active nucleus, may drift to theWHα < 3 Å zone if observed within an aperture larger than theSDSS 3′′ spectroscopic fiber.

Numerical experiments show that this is indeed likely to hap-pen. Picking the Hα luminosity of a randomly chosen AGN andadding it to the spectrum of a randombona fide RG with WHα

within 1 ± 0.5 Å, we find that about 1/3 of the resulting simulatedspectra haveWHα < 3 Å, most of which in the 2–3 Å range. We thusexpect that a fraction of the systems here classified as RGs turn outto harbour weak AGN when looked up more closely. At first sight,this seems to be a drawback of usingWHα for spectral classifica-tion, but it is not. After all, if an AGN is present and yetWHα < 3

Å, then it is still correct to infer that old stars make a substantialcontribution to the observed line emission.

Finally, we note that the division of AGN into strong and weakcategories on the basis ofWHα is also to some extent aperture de-pendent, since sAGN may be diluted to wAGN as more stellarcontinuum is sampled. This, however, is a minor issue, sincebothclasses are clearly AGN-dominated systems, so there is no risk ofmisidentifying the dominant ionizing agent.

6 THE STAR FORMATION HISTORIES WITHIN OURCLASSIFICATION SCHEME

A popular approach to digest the flood of information from mega-surveys like the SDSS is to compare the properties of galaxies indifferent classes (SF vs. AGN, Seyfert vs. LINER, early vs. latetype, field vs. cluster, etc.), in search of clues which may helpunderstanding why galaxies are the way they are (seeBlanton &Moustakas 2009for examples of studies following this generalstrategy). Taxonomy, of course, plays a central role in suchcom-parative studies. In this section we present yet another compara-tive study, but this time following the new taxonomical frameworkproposed in the previous section and summarized in Fig.6. Themain novelty in this scheme is the introduction of the RG cate-gory, whose members were previously misclassified as AGN. Amajor goal of this section is to place RGs in the context of thegalaxy population in general, which was never done before. On theone hand, we want to compare RGs with other ELGs, especiallywAGN, with which RGs were so far confused. It is also interest-ing to compare wAGN to sAGN, since this division was merelyintroduced for the sake of “backwards compatibility” with the his-torical Seyfert/LINER sub-division of AGNs. On the other hand,it is equally important to compare RGs and PGs. According to theRG concept, galaxies without emission lines shouldnotexist, sinceHOLMES provide a minimum floor ionizing field capable of pow-ering emission lines. Seen from this perspective, the existence ofPGs raises questions like: Are they truly line-less systemsor is itjust an issue of detectability? Perhaps PGs lack the warm/cold ISMto reprocess ionizing photons?

Thestarlight-SDSS database offers plenty of material for thiscomparative study. In what follows we use a suite of physicalprop-erties as well as the detailed star formation histories (SFHs) to ad-dress the issues raised above. Only the volume limited sample isused from here on.

An instructive way to start this comparative study is to investi-gate how SFHs vary as a function of the SF/sAGN/wAGN/RG/PGspectral types defined in Fig.6. This is done in Fig.7 where wechopped the WHAN diagram in 0.2×0.3 dex wide bins in [Nii]/HαandWHα. In each box we plot the time-dependent median of thespecific star formation rate (SSFR) for galaxies in the box, colourcoding the curves according to the Fig.6 emission line taxonomy.The SSFR(t) functions were smoothed exactly as inAsari et al.(2007), which should be consulted for details on how this and otherrepresentations ofstarlight-based SFHs are derived.

Because this is our classification diagram, most boxes con-tain only one curve, with exceptions at locations straddling thenominal frontier between RGs and wAGN (atWHα = 3 Å) andwAGN/sAGN (WHα = 6 Å). Although there are strong systematicchanges in SFHs across this plane, in these border-line cases onesees little difference between the SFHs of two adjacent classes.

Several trends are visible in Fig.7. For instance, focusing onthe column centred at log[Nii]/Hα = −0.1, one sees that both PGs

10 Cid Fernandes et al

Figure 7. Star formation histories as a function of spectral class andthelocation in the WHAN diagram. Each box shows the median SSFR(t) forgalaxies of different types, coded by different colours. The scale of theseplots is shown in the inset. Curves are only drawn for boxes containing over200 galaxies. Only galaxies in the volume limited sample areused in thisplot. Contours correspond to 0.1 and 0.6 of the peak density.

and RGs have SFHs concentrated in the largest ages, while, movingupwards the retrieved SFHs reveal increasing levels of recent star-formation. Due to the large apertures covered by the SDSS fibers,most of the Hα emission in these AGN probably comes from star-formation rather than the AGN (e.g.Kauffmann et al. 2003), so theincrease in recent SSFR withWHα is expected. However, the effectof the AGN is present, as can be inferred by the decreasing levelsof recent SSFR towards the right for fixedWHα (best visible in therows centred atWHα ∼ 10 Å).

Before we proceed, a parenthesis must be opened to clarify themeaning of the small upturn in SSFR(t) att . 107 yr for RGs andPGs. This feature is almost certainly an artifact of incomplete mod-elling of the horizontal branch phase (Koleva et al. 2008; Ocvirk2010; Cid Fernandes & Gonzalez Delgado 2010). This effect wasquantified byOcvirk (2010), who estimates that these “fake youngbursts” account for up to 12% of the recovered optical light frac-tions in spectral synthesis of genuinely old stellar populations. Inunits of the SSFR, this corresponds to a few times 10−12 yr−1, whichis precisely the level at which upturns at small ages are seenin Fig.7. We further find a clear trend for this artefact to be related to thestellar metallicity, with the lowt hump increasing for decreasingZ⋆, in agreement with the fact that bluer horizontal branches occurfor lower Z⋆ (Harris 1996; see also Fig.8 for indirect evidence ofthis trend). Finally, if the upturn were really due to young stars,WHα

should be much larger than observed. Including these fake youngbursts in the predictions for the ionizing flux leads to (in the me-dian) expectedWHα values over 3 times as large as observed forRGs. The overprediction for PGs is even worse, with a median ex-pectedWHα of 3.2 Å, while their definition requiresWHα < 0.5 Å.

Credible levels of recent star-formation only appear for boxescentred atWHα > 3 Å in Fig. 7, coinciding with the RG/wAGN

Figure 8. As Fig. 7, but in the plane defined by the mean stellar surfacedensity and stellar mass.µ⋆ is computed dividing half the stellar mass bythe radius which contains half the z-band light.

transition. This suggests thatall AGN are associated to at leastsome level of ongoing star-formation. While this is well known anddocumented for sAGN (the so called “starburst-AGN connection”)the situation is much less clear for wAGN (e.g.Gonzalez Delgadoet al. 2004and references therein). It is difficult to compare thisfinding with those in previous studies because of varying defini-tions of wAGN, but we can confidently say that the signatures ofstar formation in wAGN identified in this work would be wiped outwithout removing the RGs from the AGN category.

We now investigate how SFHs vary in terms of properties notexplicitly involved in the definition of our spectral classes. Fig.8shows SFHs in a stellar mass (M⋆) versus mass density (µ⋆) dia-gram. This allows us to compare the SFHs of galaxies which aresimilar in terms of global properties, but differ in terms of emissionline properties, since this time each box can contain galaxies ofdifferent classes. Again, several trends are evident to the eye.Oneof them is the signature of downsizing, here manifested by the de-crease in recent SSFR levels as one moves up the mass scale, seen,for instance, in the curves for SF galaxies in the 1st two columnsof boxes. A similar behaviour is also seen for RGs and PGs8, butshifted in time tot ∼ 109 yr.

For this study, the more relevant trend in Fig.8 is that in everybox the lines corresponding to our different classes appear in thesame order, with recent star formation decreasing from SF galax-ies to sAGN, wAGN, RGs and PGs. These last two, in fact, havemedian SFHs which are very similar and sometimes indistinguish-able. Both types retired from forming stars over 108 yr ago. The

8 Recall that the “mini bursts” att . 107 yr are not real, but artifacts ofspectral fits with an incomplete base (Ocvirk 2010). The fact that these fakebursts correlate with galaxy mass, as seen in Fig.8, is likely an indirectconsequence of the connection between horizontal branch morphology andZ⋆ coupled to theM⋆–Z⋆ relation.

How to tell true from fake AGN? 11

Figure 9. As Fig.7, but for the observational color magnitude diagram.

plot also shows that the recent SSFR of wAGN never reaches lev-els as low as those of RGs and PGs. Despite their generally lowlevels of star formation compared to sAGN of sameM⋆ andµ⋆,wAGN are not retired, reinforcing the idea that star formation andAGN activity are connected even in the weakest cases, at least in astatistical sense.

Fig. 9 shows another example. The plot shows the observa-tional color magnitude diagram, with the familiar red sequence,blue cloud and green valley pattern. This diagram is meant topro-vide an empirical representation of the age-mass relation.As ex-pected, systems in the blue cloud are more actively forming starsnowadays than in their past average, while red sequence galaxies(with the exception of AGN) tend to be retired. Again, the SF toPG sequence in levels of recent SSFR and the similarity betweenRGs and PGs seen in Figs.7 and8 is present in every box.

In summary, the extended emission line classification schemeproposed here bears an excellent correspondence with the SFHs ofgalaxies. RGs and PGs have very similar SFHs for similar globalproperties. wAGN are more similar to sAGN in terms of SFHsthan to RGs or PGs. Traditional classification schemes whichig-nore RGs, would, on the contrary, find wAGN to be more like PGs.

7 DISTRIBUTIONS OF GLOBAL GALAXY PROPERTIES

Comparing the distributions of global properties of different galaxyclasses can provide a further test of the pertinence of our classifi-cation scheme. This is done in Fig.10, which shows normalizedhistograms of PG, RG, wAGN, sAGN and SF galaxies in terms ofa series of properties related to their structure, stellar populations,gas and dust content.

A striking feature of these histograms is that for most proper-ties PG and RG have similar distributions, reinforcing our interpre-tation that PG and RG are essentially the same thing, or at least thatthese classes overlap heavily. On the other hand, the distributionsfor wAGN are more similar to the ones of sAGN, suggesting that

these galaxies belong to the same family. This is a salutary apos-teriori validation of our splitting of LINERs into two very differentclasses: RGs and wAGN.

Except for SF galaxies, all other galaxy types have masseswithin roughly±0.5 dex of 1011 M⊙ (Fig. 10a). This is mostly dueto the luminosity cut for the V sample, which imposes a mass com-pleteness limit of∼ 1010.8M⊙. In the median,MsAGN

⋆ < MwAGN⋆ ∼

MPG⋆ < MRG

⋆ . In terms of surface densities and velocity dispersions(panels b and c), PGs and RGs are statistically similar, occupyingthe high end of the distributions. Despite the substantial overlap,it is clear that wAGN and sAGN tend to have smallerσ⋆ andµ⋆than RGs and PGs. The same is true for the escape velocity, com-puted from (GM⋆/R)1/2 with Rgiven by the half-light z-band radius(panel d). PGs and RGs also differ from AGN in terms of structuralproperties like the concentration index (e) and the SDSS deVAB rindex (f). Inspection of SDSS images corroborates the similaritiesbetween PGs and RGs, most of which are ellipticals, althoughsomeof the RGs turn out to be spirals (as can be deduced by the tailtowards low CI in Fig.10e), including an apparently higher thannormal rate of edge-on systems. An investigation of this issue isbeyond the scope of this paper, but we note that this “contaminant”population tends to haveWHα close to the RG/wAGN frontier, andthus may be related to the unavoidable fuzziness of classificationschemes.

In terms of luminosity-weighted mean stellar ages (Fig.10g),sAGN are younger than wAGN, which in turn are younger thanRGs and PGs. From Fig.7 we infer that the tail of RGs towardssmaller mean ages is due to systems at the RG/wAGN border. Themean stellar metallicities (panel h) are similar for all types, with atail of sAGN reaching lower values.

The distributions of stellar extinctions (panel i) show a dis-tinct PG< RG< wAGN < sAGN pattern. To double check this re-sult, panel l shows the distribution of Hα/Hβ, but only for sourceswhere this ratio is reliable, a criterion which excludes PGsand mostRGs. Despite its incompleteness, Fig.10l confirms that wAGNhave higher extinction than RGs and lower than sAGN. The classwith the lowest extinction is the SF class, in spite of the intensestar formation which is usually associated with high extinction. Theunderlying reason for a smaller extinction in SF than the rest ofgalaxies lies in their lower masses (seeStasinska et al. 2004; Garn& Best 2010). Regarding the PG→ RG→ wAGN → sAGN ex-tinction sequence, its interpretation is not straightforward, since itinvolves the dust mass and distribution, as well as its composition(dust grains recently produced by the progenitors of HOLMESandexpected to be found in RGs do not have the same extinction prop-erties as grains processed in the interstellar medium and associatedwith regions of star formation).

Fig. 10j shows the distribution ofξ uncorrected for dust. ThePG→ RG→ wAGN→ sAGN→ SF sequence reflects the centralrole ofWHα in our classification scheme. This sequence is mirroredin the distribution of observed Hα luminosities (Fig.10k). PGs in-cluded in these panels have, by definition, very weak Hα (medianWHα = 0.23 Å, and medianS NHα = 2.4 counting only those withan Hα flux in our database). Still, their locations in these diagramsare consistent with them not being completely line-less.

8 DISCUSSION AND SUMMARY

The goal of this paper was to provide a comprehensive classifica-tion of galaxies according to their emission line properties whichwould be able to cope with the large population of weak line

12 Cid Fernandes et al

Figure 10.Normalized histograms of properties for different galaxy classes. Colours code the SF, sAGN, wAGN, RG andPG classes as in Fig.6. Horizontalbars trace the 16 to 84 percentile ranges, with the median marked by a circle, with the vertical shifts ordered as SF (top),sAGN, wAGN, RG and PG (bottom).All galaxies from our volume limited sample are included, except for panel l, for which a reliable Hα/Hβ was required, hence excluding all PGs and most RGs.

galaxies that do not appear in traditional diagrams (e.g., BPT) be-cause they lack some of the diagnostic lines. This is possible us-ing the WHAN diagram (WHα vs [N ii]/Hα) that we proposed inearlier work (CF10). An additional problem with the BPT dia-gram and other similar line-ratio diagrams is that, as shownbyStasinska et al.(2008), the LINER region contains a mix of twocompletely different families of galaxies: galaxies that contain aweak AGN and “retired galaxies”, i.e. galaxies that have stoppedforming stars and whose emission lines are powered by their hotevolved low-mass stars (HOLMES). A truly comprehensive classi-fication scheme must account for this phenomenon, and the WHANdiagram allows so.

To distinguish true AGN from fake ones (i.e., the RGs), wehave shown that a useful criterion is the value ofξ, which measuresthe intrinsic extinction-corrected Hα luminosity in units of the Hαluminosity expected from stellar populations older than 108 yr un-covered by our stellar population analysis softwarestarlight. Inprinciple, ξ should be equal to one if the galaxy absorbs all the

ionizing photons provided by its HOLMES, and> 1 if any othersource of ionization is present. We have shown that this index is bi-modally distributed, with aξ ≫ 1 population composed by galax-ies undergoing vigorous star-formation and/or nuclear activity, anda population of galaxies centred right at the predicted value forRGs. Computingξ for each galaxy, however, is not a trivial task:it requires a stellar population analysis able to identify the popu-lations of HOLMES and a recipe to obtain the ionizing radiationfield from these HOLMES. The latter strongly depends on how the(yet quite uncertain) evolutionary tracks for post-asymptotic giantbranch stars are incorporated into the stellar population codes aswell as on other issues such as the IMF and the metallicities of thestellar populations.

It is therefore preferable to base the distinction between trueand fake AGN on the Hα equivalent width, an easy to obtain obser-vational parameter which provides an excellent proxy forξ. In viewof the strong dichotomy in the observedWHα distribution for galax-ies in the right wing of the BPT diagram (i.e., the wing previously

How to tell true from fake AGN? 13

supposed to contain only AGN), we propose a practical separationbetween weak AGN and RGs based on a class frontier atWHα = 3Å. This physically inspired but data-guided separation correspondsto the minimum between the two modes in theWHα distribution.

We thus identify 5 classes of galaxies in the WHAN diagram:

• SF: Pure star forming galaxies: log [Nii]/Hα < −0.4 andWHα > 3 Å• sAGN: Strong AGN (i.e., Seyferts): log [Nii]/Hα > −0.4 and

WHα > 6 Å• wAGN: Weak AGN: log [Nii]/Hα > −0.4 and 3<WHα < 6 Å• RG: Retired galaxies (i.e., fake AGN):WHα < 3 Å• PG: Passive galaxies (actually, line-less galaxies):WHα and

W[N ii] < 0.5 Å

Note that the border between RGs and PGs is somewhat arbi-trary, but this is not really a concern.

We have then analysed the star formation histories of the dif-ferent classes of galaxies, as well as the distribution of a dozenof physical or observational properties, such as the galaxystellarmassM⋆, surface densitiesµ⋆, velocity dispersionsσ⋆, and stellarextinctionsA⋆V obtained throughstarlight. All these comparisonscorroborate our proposed differentiation between RGs and wAGNin the LINER-like family. We also find that wAGN have propertiesoverlapping with those of sAGN, while RGs line up with PGs. Inother words, wAGN are the low activity end of the AGN family,while RGs are passive galaxies with emission lines. As a matterof fact, both RGs and PGs are “retired” or “passive” galaxies, thereal spectroscopic difference between them being the presence orabsence of emission lines. It is only the inherited custom whichimposed the adopted nomenclature.

Now that these emission line classes have been established,further work could be to understand what is at the root of thestrength of the AGN phenomenon or why some passive galaxiespresent emission lines and others do not.

ACKNOWLEDGEMENTS

Thestarlight project is supported by the Brazilian agencies CNPq,CAPES, by the France-Brazil CAPES-COFECUB program, and byObservatoire de Paris.

Funding for the SDSS and SDSS-II has been provided bythe Alfred P. Sloan Foundation, the Participating Institutions, theNational Science Foundation, the U.S. Department of Energy,the National Aeronautics and Space Administration, the JapaneseMonbukagakusho, the Max Planck Society, and the Higher Ed-ucation Funding Council for England. The SDSS Web Site ishttp://www.sdss.org/.

The SDSS is managed by the Astrophysical Research Con-sortium for the Participating Institutions. The Participating Institu-tions are the American Museum of Natural History, AstrophysicalInstitute Potsdam, University of Basel, University of Cambridge,Case Western Reserve University, University of Chicago, DrexelUniversity, Fermilab, the Institute for Advanced Study, the JapanParticipation Group, Johns Hopkins University, the Joint Institutefor Nuclear Astrophysics, the Kavli Institute for ParticleAstro-physics and Cosmology, the Korean Scientist Group, the ChineseAcademy of Sciences (LAMOST), Los Alamos National Labora-tory, the Max-Planck-Institute for Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico StateUni-versity, Ohio State University, University of Pittsburgh,University

of Portsmouth, Princeton University, the United States Naval Ob-servatory, and the University of Washington.

REFERENCES

Abazajian K. N. et al., 2009, ApJS, 182, 543Annibali F., Bressan A., Rampazzo R., Zeilinger W. W., Vega O.,Panuzzo P., 2010, ArXiv e-prints

Asari N. V., Cid Fernandes R., Stasinska G., Torres-Papaqui J. P.,Mateus A., Sodre L., Schoenell W., Gomes J. M., 2007, MN-RAS, 381, 263

Baldwin J. A., Phillips M. M., Terlevich R., 1981, PASP, 93, 5Bamford, S. P., Rojas, A. L., Nichol, R. C., Miller, C. J., Wasser-man, L., Genovese, C. R., & Freeman, P. E. 2008, MNRAS, 391,607

Binette L., Magris C. G., Stasinska G., Bruzual A. G., 1994,A&A,292, 13

Blanton M. R., Moustakas J., 2009, ARA&A, 47, 159Bloecker T., 1995, A&A, 299, 755Bressan A., Fagotto F., Bertelli G., Chiosi C., 1993, A&AS, 100,647

Brinchmann J., Charlot S., White S. D. M., Tremonti C., Kauff-mann G., Heckman T., Brinkmann J., 2004, MNRAS, 351, 1151

Bruzual G., Charlot S., 2003, MNRAS, 344, 1000Cardelli J. A., Clayton G. C., Mathis J. S., 1989, ApJ, 345, 245Chabrier G., 2003, PASP, 115, 763Charbonnel C., Meynet G., Maeder A., Schaerer D., 1996, A&AS,115, 339

Cid Fernandes R., Gonzalez Delgado R. M., 2010, MNRAS, 403,780

Cid Fernandes R., Mateus A., Sodre L., Stasinska G., GomesJ. M., 2005, MNRAS, 358, 363

Cid Fernandes R., Stasinska G., Schlickmann M. S., Mateus A.,Vale Asari N., Schoenell W., Sodre L., 2010, MNRAS, 403, 1036

Connolly A. J., Szalay A. S., Bershady M. A., Kinney A. L.,Calzetti D., 1995, AJ, 110, 1071

Coziol R., Ribeiro A. L. B., de Carvalho R. R., Capelato H. V.,1998, ApJ, 493, 563

de Grijp M. H. K., Keel W. C., Miley G. K., Goudfrooij P., Lub J.,1992, A&AS, 96, 389

de Vaucouleurs G., 1960, AJ, 65, 51Eracleous M., Hwang J. A., Flohic H. M. L. G., 2010, ApJ, 711,796

Fioc M., Rocca-Volmerange B., 1997, A&A, 326, 950Folkes S. R., Lahav O., Maddox S. J., 1996, MNRAS, 283, 651Garn T., Best P., 2010, ArXiv e-printsGonzalez Delgado R. M., Cid Fernandes R., Perez E., MartinsL. P., Storchi-Bergmann T., Schmitt H., Heckman T., LeithererC. 2004, ApJ, 605, 127

Heckman T. M., 1980, A&A, 87, 142Harris W., 1996, AJ, 112, 1487Hills J. G., 1972, A&A, 17, 155Hubble E. P., 1936, Realm of the Nebulae, Hubble, E. P., ed.Kauffmann G. et al., 2003, MNRAS, 346, 1055Kaviraj S., 2010, MNRAS, 406, 382Kewley L. J., Groves B., Kauffmann G., Heckman T., 2006, MN-RAS, 372, 961

Kroupa, P. 2001, MNRAS, 322, 231Koleva M., Prugniel P., Ocvirk P., Le Borgne D., Soubiran C.,2008, MNRAS, 385, 1998

Le Borgne J. et al., 2003, A&A, 402, 433

14 Cid Fernandes et al

Lejeune T., Cuisinier F., Buser R., 1997, A&AS, 125, 229Lintott C. et al., 2010, ArXiv e-printsLyon J., 1975, ApJ, 201, 168Macchetto F., Pastoriza M., Caon N., Sparks W. B., GiavaliscoM., Bender R., Capaccioli M., 1996, A&AS, 120, 463

Masters K. L. et al., 2010, MNRAS, 405, 783Mateus A., Sodre L., Cid Fernandes R., Stasinska G., SchoenellW., Gomes J. M., 2006, MNRAS, 370, 721

Miller C. J., Nichol R. C., Gomez P. L., Hopkins A. M., BernardiM., 2003, ApJ, 597, 142

Molla M., Garcıa-Vargas M. L., 2000, A&A, 359, 18Molla M., Garcıa-Vargas M. L., Bressan A., 2009, MNRAS, 398,451

Morgan W. W., Mayall N. U., 1957, PASP, 69, 291Ocvirk P., 2010, ApJ, 709, 88Pastoriza M., 1968, Boletin de la Asociacion Argentina de As-tronomia La Plata Argentina, 14, 7

Rauch T., 2003, A&A, 403, 709Salpeter, E. E. 1955, ApJ, 121, 161Sanchez Almeida J., Aguerri J. A. L., Munoz-Tunon C., deVi-cente A., 2010, ApJ, 714, 487

Sanchez Almeida J., Munoz-Tunon C., Amorın R., Aguerri J. A.,Sanchez-Janssen R., Tenorio-Tagle G., 2008, ApJ, 685, 194

Sanchez-Blazquez, P., et al. 2006, MNRAS, 371, 703Sarzi M. et al., 2010, MNRAS, 402, 2187Schaller G., Schaerer D., Meynet G., Maeder A., 1992, A&AS,96, 269

Schawinski K. et al., 2010, ApJ, 711, 284Schoenberner D., 1983, ApJ, 272, 708Smith H. E., Lonsdale C. J., Lonsdale C. J., 1998, ApJ, 492, 137Sodre Jr. L., Cuevas H., 1994, Vistas in Astronomy, 38, 287Sokolowski J., Bland-Hawthorn J., 1991, PASP, 103, 911Stasinska G., Cid Fernandes R., Mateus A., Sodre L., AsariN. V.,2006, MNRAS, 371, 972

Stasinska G., Mateus Jr. A., Sodre Jr. L., Szczerba R., 2004, A&A,420, 475

Stasinska G., Vale Asari N., Cid Fernandes R., Gomes J. M.,Schlickmann M., Mateus A., Schoenell W., Sodre Jr. L., 2008,MNRAS, 391, L29

Strauss M. A. et al., 2002, AJ, 124, 1810Terzian Y., 1974, ApJ, 193, 93York D. G. et al., 2000, AJ, 120, 1579