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    Mon. Not. R. Astron. Soc. 000, 110 (2014) Printed 17 December 2014 (MN LATEX style file v2.2)

    Prospects for gravitational-wave detection and

    supermassive black hole astrophysics with pulsar timing


    V. Ravi1,2, J. S. B. Wyithe1, R. M. Shannon2 and G. Hobbs21School of Physics, University of Melbourne, Parkville VIC 3010, Australia2CSIRO Astronomy and Space Science, Australia Telescope National Facility, P.O. Box 76, Epping, NSW 1710, Australia


    Large-area sky surveys show that massive galaxies undergo at least one majormerger in a Hubble time. Ongoing pulsar timing array (PTA) experiments are aimedat measuring the gravitational wave (GW) emission from binary supermassive blackholes (SMBHs) at the centres of galaxy merger remnants. In this paper, using thelatest observational estimates for a range of galaxy properties and scaling relations,we predict the amplitude of the GW background generated by the binary SMBHpopulation. We also predict the numbers of individual binary SMBH GW sources. Wepredict the characteristic strain amplitude of the GW background to lie in the range5.1 1016 < Ayr < 2.4 10

    15 at a frequency of (1 yr)1, with 95% confidence.Higher values within this range, which correspond to the more commonly preferredchoice of galaxy merger timescale, will fall within the expected sensitivity ranges ofexisting PTA projects in the next few years. In contrast, we find that a PTA consistingof at least 100 pulsars observed with next-generation radio telescopes will be requiredto detect continuous-wave GWs from binary SMBHs. We further suggest that GWmemory bursts from coalescing SMBH pairs are not viable sources for PTAs. Boththe GW background and individual GW source counts are dominated by binariesformed in mergers between early-type galaxies of masses & 5 1010M at redshifts. 1.5. Uncertainties in the galaxy merger timescale and the SMBH mass galaxybulge mass relation dominate the uncertainty in our predictions.

    Key words: black hole physics galaxies: evolution gravitational waves pulsars: general


    Astrophysical gravitational waves (GWs) affectlong-term timing measurements of radio pulsars(Estabrook & Wahlquist 1975; Sazhin 1978; Detweiler1979). GW-induced metric perturbations at the Earthcause variations in pulse arrival times that differ betweenpulsars only by geometric factors. Hence, a specific GWsignal may be directly detected in contemporaneoustiming measurements of multiple pulsars. Three suchpulsar timing arrays (PTAs; e.g., Foster & Backer 1990)

    E-mail: [email protected]

    are currently in operation: the European Pulsar TimingArray (EPTA; Kramer & Champion 2013), the NorthAmerican Nanohertz Observatory for Gravitational Waves(NANOGrav; McLaughlin 2013), and the Parkes PulsarTiming Array (PPTA; Manchester et al. 2013). Thesegroups also share data as part of the International PulsarTiming Array (IPTA; Hobbs et al. 2010) consortium.Currently, at least 50 millisecond pulsars are observedevery 2 4 weeks, and data sets stretching for 5 30 yrexist for 34 of these pulsars (Manchester & IPTA 2013).Together, these timescales imply that PTAs are sensitiveto GWs in the frequency band 109 107 Hz, which iscomplementary to other GW detection experiments.

    c 2014 RAS

  • 2 Ravi et al.

    The best-studied sources of GWs within the PTA fre-quency band are binary supermassive black holes (SMBHs).Stellar- or gas-dynamical evidence exists for SMBHs atthe centres of 87 nearby galaxies at the time of writing(Kormendy & Ho 2013), with masses M ranging between106 1011M. Phenomenological models of the buildup ofthe cosmological mass density in SMBHs during luminousquasar phases for redshifts z < 5 (e.g., Yu & Tremaine 2002;Shankar et al. 2013) suggest short quasar lifetimes. This,considered together with local correlations between M and,for example, galaxy bulge mass Mbul (e.g., Kormendy & Ho2013), suggests that all massive galaxies (M > 10

    10M)which formed since the z 2 peak of quasar activity hostSMBHs (see also Miller et al. 2014).

    In the context of hierarchical structure formation, merg-ers are integral to the formation histories of massive galax-ies, and evidence for interacting galaxies is seen across mostof cosmic time (Barnes & Hernquist 1992). Multiple SMBHsare expected to be found in galaxy merger products. Indeed,pairs of active galactic nuclei (AGN) are observed in galaxiesin the late stages of mergers (Merritt & Milosavljevic 2005),with projected separations as small as 7 pc (Rodriguez et al.2006). The central SMBHs in a pair of merging galaxiesare likely to sink in the merger remnant potential wellthrough dynamical friction and form a bound binary (e.g.,Begelman et al. 1980; Khan et al. 2012). Dynamical frictionbecomes inefficient once stars within the binary orbit areejected, and the slingshot scattering of stars on radial or-bits (Frank & Rees 1976; Quinlan 1996; Yu 2002) or frictionagainst circumbinary gas (Escala et al. 2004; Dotti et al.2007; Roedig et al. 2011) is required to drive further or-bital decay. At a binary component separation of 1 pc,energy and angular momentum losses to gravitational wave(GW) emission can lead to SMBH-SMBH coalescence withina Hubble time (e.g., Peters & Mathews 1963). A few candi-date binary SMBHs with such separations have been iden-tified (e.g., Valtonen et al. 2008; Boroson & Lauer 2009;Eracleous et al. 2012).

    The existence of a large cosmological population of bi-nary SMBHs is thus inferred, some of which emit GWs inthe PTA frequency band. If the orbits of all binary SMBHsradiating GWs in the PTA frequency band are circular andevolving under GW emission alone, the summed GW signalsfrom all binaries together have the expected spectral form(e.g., Phinney 2001):

    hc(f) = Ayr





    . (1)

    Here, hc(f) is the GW characteristic strain per logarith-mic frequency unit, f is the GW frequency at the Earth,fyr = (1 yr)

    1 and Ayr is the characteristic spectral ampli-tude at fyr. The summed signals from binary SMBHs maybe collectively modelled as a GW background (GWB). GWsfrom individual binaries may be detectable as continuous-wave (CW) sources (e.g., Sesana et al. 2009; Ravi et al.2012). Coalescing pairs of SMBHs also emit GW memorybursts (Braginskii & Thorne 1987; Favata 2009), which are

    abrupt, propagating metric changes that also affect pulsartiming measurements (e.g., Madison et al. 2014).

    Of these three types of GW signal, searches for a GWBprovide the best constraints on models for the binary SMBHpopulation (Shannon et al. 2013) and are perhaps the clos-est to yielding a successful detection (Siemens et al. 2013).Shannon et al. (2013) used the first PPTA data release(Manchester et al. 2013) to show that Ayr < 2.4 10


    with 95% confidence. Siemens et al. (2013) suggest that theNANOGrav PTA is likely to be able to detect a GWBwith Ayr = 10

    15 by around the year 2020. Constraintson GWs from individual binary SMBHs with the currentPPTA data set showed that binaries with component massesM > 10

    9M and with separations of less than 0.02 pcare unlikely to exist within 30Mpc of the Earth (Zhu et al.2014). No search for memory bursts with PTA data has sofar been published. Wang et al. (submitted) describe an un-successful search for memory bursts using current PPTAdata.

    Predictions for GW signals from binary SMBHs arebased either on physical models for galaxy formation andevolution which predict the binary SMBH population, oron directly observed quantities such as the galaxy mergerrate and SMBH-galaxy scaling relations. The former predic-tions typically combine dark matter halo merger rates in thecold dark matter paradigm, analytic or numerical estimatesof galaxy merger and binary SMBH formation timescales,prescriptions for the cosmic evolution of the galaxy andSMBH population and the assumption of GW-driven bi-nary orbital evolution (Wyithe & Loeb 2003; Enoki et al.2004; Sesana et al. 2008; Ravi et al. 2012; Kulier et al. 2013;Ravi et al. 2014). Once tuned to reproduce observables suchas local SMBH-galaxy relations, the galaxy stellar massfunction (GSMF) and colour distribution, and the quasarluminosity function, these models result in estimates of Ayrin the range 101621015. The exact value depends on,for example, the assumed cosmological parameters, galaxymerger timescales and the specific models for SMBH forma-tion and growth. Models for the binary SMBH populationwhich more directly incorporate observational information(Jaffe & Backer 2003; Sesana 2013; McWilliams et al. 2014)result in similar values of Ayr. While various studies suggestthat individual binary SMBHs may be viable CW sources ofGWs for PTAs (Sesana et al. 2009; Ravi et al. 2012) as wellas viable sources of memory bursts (van Haasteren & Levin2010; Cordes & Jenet 2012; Madison et al. 2014), quantita-tive predictions of source counts have only been calculatedfor CW sources using theoretical galaxy formation models(Sesana et al. 2009).

    In this paper, we adopt an observations-based approachtowards modelling the binary SMBH population, in order topredict the range of GW signals in the PTA frequency band.A similar study by Sesana (2013), hereafter S13, combineda plethora of observational estimates of the merger rate ofmassive galaxies, the GSMF and local SMBH-galaxy rela-tions to derive a range of possible GWB amplitudes. How-ever, some of the observational quantities included in theS13 study do not represent the best current knowledge. Fur-

    c 2014 RAS, MNRAS 000, 110

  • Gravitational-wave detection and SMBH astrophysics 3

    thermore, some relevant uncertainties, such as in the pos-sible redshift evolution of the SMBH-galaxy relations, werenot accounted for by S13, and no predictions for individualGW sources were made. Besides addressing these issues, thispaper builds on previous studies in the following ways:

    We quantify the impacts of different observational un-certainties on the amplitude of the GWB generated by bi-nary SMBHs. We focus in particular on aspects of our modelfor the binary SMBH population for which little observa-tional information currently exists.

    We highlight the redshifts, masses and types of merginggalaxies which result in binary SMBHs which dominate theGWB amplitude.

    We provide new, observations-based predictions for thecounts of individual GW sources. We further present the firstestimates for the expected numbers of detectable individualGW sources, given different PTA configurations, that arerobust with respect to pulsar parameter fitting.

    In 2, we outline our model, and present our results in3. We state the key implications of this work for PTAsin 4. Finally, we discuss these results in 5, and sum-marise our conclusions in 6. Throughout this work, weadopt a concordance cosmology based on results from thePlanck satellite (Planck Collaboration et al. 2013), includ-ing H0 = 67.8 kms

    1 Mpc1, = 0.692 and M = 0.308.



    2.1 The SMBH-SMBH coalescence rate

    The cosmological population of binary SMBHs emittingGWs in the PTA frequency band can be characterised usingthe SMBH-SMBH coalescence rate. The numbers of binarySMBHs in different orbits are related to the coalescence ratethrough a continuity equation (Phinney 2001; Ravi et al.2014) that includes assumptions about the rate of binarySMBH orbital evolution.

    We assume that the SMBH-SMBH coalescence rate isequivalent to the galaxy merger rate. This is justified be-cause massive galaxy mergers are typically completed withina few galaxy dynamical times, whereas the timescales fortwo SMBHs to form a gravitationally-bound binary andthen coalesce through losses of energy and angular mo-mentum to their environments and GWs are much shorter(e.g., Begelman et al. 1980; Roedig & Sesana 2012). In thiswork, we neglect systems of more than two gravitationally-interacting SMBHs resulting from multiple galaxy mergers,because we expect these to be rare for the high mass ratio( > 1/3) mergers between massive (M > 10

    10M) galax-ies that we consider. We further assume that each galaxycontains a central SMBH with a mass related to the galaxybulge mass. We use measured quantities to determine theall-sky coalescence rate of pairs of SMBHs. For each quan-tity, we define a fiducial prescription, and also describe thepossible ranges over which the prescription can vary.

    Similarly to S13, we express the galaxy merger rate as

    mrg(M, , z) =d4Nmrg

    d log(M)d log()dzdt(2)



    d2Ngald log(M)dz


    d log()


    whereNmrg is the number of mergers between two galaxies ofcombined stellar mass M(1 + ), is the ratio betweenthe smaller and larger galaxy stellar masses and z is thecosmological redshift. The merger rate, mrg, is defined asthe number of mergers per units M, , z and observer timet. In Equation 3, Ngal is the number of galaxies across theentire sky with a given M at a given z. This is related tothe standard GSMF, (M, z), as

    d2Ngald log(M)dz

    = 4d2Vcddz

    , (4)

    where 4d2Vc

    ddzis the sky-integrated comoving volume shell

    between redshifts z and z + dz. In Equation 4, dPd log()

    Mis the probability density function for a galaxy mergerevent with mass M at redshift z having a mass ratio ,

    (M, z) =(



    is the average proper time between

    major mergers for a galaxy with a mass M at redshift z anddtpdt

    = (1+ z)1. Also,dnmrgdtp

    M, zis the number of mergers,

    nmrg, per unit proper time, tp, for a single galaxy with amass M at redshift z.

    In order to convert galaxy stellar masses to bulge masses(Mbul) we distinguish between quiescent, red-sequence early-type galaxies and star-forming, blue-cloud late-type galax-ies. We write the total GSMF as a sum of the GSMFs ofearly- (, early) and late-type (, late) galaxies:

    (M, z) = , early +, late. (5)

    We relate M to Mbul for early- and late-type galaxies usinga scheme described in 2.1.3.

    To convert between Mbul and the central SMBHmasses (M) we use the widely-known M Mbul relation(Kormendy & Ho 2013; Scott et al. 2013). In contrast toS13, we express this relation as


    d logM= N (+ logMbul,

    2) (6)

    where N (, 2) denotes a normal probability density func-tion with centre and variance 2 and , and the intrin-sic scatter, , are observationally-determined constants. It isimportant to account for intrinsic scatter in the M Mbulrelation when inferring the SMBH mass function from thebulge mass function (e.g., Aller & Richstone 2002), becauseto not do so would lead to the SMBH mass function beingunderestimated.

    2.1.1 The times between galaxy mergers

    Observational estimates of (M, z) require knowledge ofthe fraction of galaxies, fgm, within a mass-complete sam-ple at a given redshift that are undergoing mergers, and the

    c 2014 RAS, MNRAS 000, 110

  • 4 Ravi et al.

    proper time m during which merger events can be observa-tionally identified (for a review, see Conselice 2014). Then,(M, z) = m/fgm. In this work, we focus on major merg-ers with stellar mass ratios > 1/3, because these systemsare likely to dominate the GW signal (e.g., Sesana et al.2004, S13).

    We consider three recent measurements of fgm for majormergers at different redshifts in wide-area galaxy surveys,which are largely complete for galaxy stellar masses M >1010M. These three studies fit their data to the functionfgm = agm(1+z)

    bgm , where agm and bgm are free parameters.

    Conselice et al. (2009) used structural analyses of con-centration, asymmetry and clumpiness (the CAS param-eters) to identify systems in the process of merging among 22000 galaxies in the COSMOS and Extended Groth Stripsurveys with M > 10

    10M at z < 1.2. This technique issensitive to major mergers in particular, with mass ratios & 1/3 (Conselice 2003). Conselice et al. (2009) foundfgm = (0.022 0.006)(1 + z)

    1.60.6. Xu et al. (2012) counted galaxy pairs with projected

    separations between 5 h1 kpc and 20 h1 kpc from the COS-MOS survey to estimate fgm for z < 1 and > 0.4.They scaled their results to include galaxy pairs for all > 1/3 using the argument that fgm is inversely pro-portional to the logarithm of minimum mass ratio of theobserved galaxy pair sample. Xu et al. (2012) found fgm =(0.013 0.001)(1 + z)2.20.2.

    Both the above works may suffer from incorporatingsmall galaxy samples at low redshifts. This issue was ad-dressed by Robotham et al. (2014) using a large sample ofgalaxy pairs from the Galaxy and Mass Assembly (GAMA)survey in the redshift interval 0.05 < z < 0.2. When stan-dardised to the same projected separation, galaxy mass andmass ratio windows as Xu et al. (2012), they found a sub-stantially higher value of fgm at these redshifts. By com-bining their results with all recent measurements of fgm atredshifts up to 1.2, and normalising to the same projectedpair separations of Xu et al. (2012), Robotham et al. (2014)found fgm = (0.021 0.001)(1 + z)


    Other works have estimated fgm with varying levels of ac-curacy. S13 included results from the galaxy pair stud-ies of Bundy et al. (2009), de Ravel et al. (2009) andLopez-Sanjuan et al. (2012). However, Bundy et al. (2009)and de Ravel et al. (2009) had significantly smaller pairsamples than were utilised by either Xu et al. (2012) orRobotham et al. (2014), and Lopez-Sanjuan et al. (2012)only considered major mergers of galaxies with M >1011M.

    In the absence of observational estimates of the galaxymerger timescale (m) used in calculating (M, z) fromdifferent measurements of fgm, we make use of theoreti-cal predictions. However, the range of possible predictionsspans a factor of three. Kitzbichler & White (2008) used amock galaxy catalogue from a semi-analytic model imple-mented within the Millennium simulation (Springel et al.2005) to estimate m for galaxies with different massesat different stages of merging assuming circular galaxy

    orbits and angular momentum loss through dynamicalfriction. However, a suite of hydrodynamic simulationsof galaxy mergers conducted by Lotz et al. (2008) andLotz et al. (2010), hereafter collectively L08, resulted in sig-nificantly shorter merger timescales. While some authors(e.g., Bundy et al. 2009; Robotham et al. 2014) use the esti-mates of Kitzbichler & White (2008) to calculate (M, z),others (e.g., Conselice et al. 2009; Xu et al. 2012; Conselice2014) argue that these estimates are incorrect, at least formajor mergers (see footnote 15 of Hopkins et al. 2010).Analyses of cosmological hydrodynamical simulations com-bining dark matter and baryonic components suggest thatthe merger timescales assumed in semi-analytic models ofgalaxy formation and used by Kitzbichler & White (2008)are overestimated for major mergers (Jiang et al. 2008). Fur-thermore, L08 presented estimates of m specifically cali-brated to the CAS technique of Conselice et al. (2009) us-ing mock galaxy images. Here, as a fiducial case, we onlyuse the estimates of m from L08, specific to estimates offgm from both galaxy pair counts and CAS analyses. Thesemerger timescales were averaged over both field and clusterenvironments, and hence account for environmental depen-dencies. However, the simulation suite of L08 was not largeenough to reveal significant mass- or redshift-dependence ofm. Hence, we consider it possible that the weak dependen-cies on these quantities identified by Kitzbichler & White(2008) may be present.

    We therefore have the three following estimates of thetimes between galaxy mergers:

    (M, z) = (13.8 3.1)(1 + z)1.60.6 Gyr (7)

    (M, z) = (19.2 1.5)(1 + z)2.20.2 Gyr (8)

    (M, z) = (14.3 0.6)(1 + z)1.60.6 Gyr, (9)

    based on the work of Conselice et al. (2009), Xu et al. (2012)and Robotham et al. (2014) respectively. While we con-sider each of Equations 79 to be equally possible, wechoose Equation 7 (Conselice et al. 2009) as a fiducial pre-scription. The possible mass- and redshift-dependence of(M, z) is given by the factor (M/10


    (Kitzbichler & White 2008); we further consider it equallylikely that this factor is present or absent, while choosingits absence as fiducial. Together, there are then six differ-ent possibilities for (M, z) that we consider, each withobservational uncertainties. We also demonstrate the ef-fects on the GW signal from binary SMBHs of using sys-tematically larger values of m that are consistent withKitzbichler & White (2008).

    The fitting formulae in Equations 79 are consistentwith results at higher redshifts (Conselice 2014). We henceadopt these equations for z < 3, and also assume dP

    d log()=

    constant (Xu et al. 2012). Uncertainties in (M, z) forz & 1 do not significantly affect our predictions for GWsignals from binary SMBHs, because, as we demonstrate, itappears that these signals are dominated by contributionsfrom binary SMBHs at lower redshifts.

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  • Gravitational-wave detection and SMBH astrophysics 5

    2.1.2 The GSMF

    We use the latest measurements of the GSMF for z 1010M, less than

    10% have no bulge component (Mendel et al. 2014); in thiswork, we assume a conservative value of 10%. Of the others,Mbul/M is in the range 0.2 0.1 (Lackner & Gunn 2012;Mendel et al. 2014; Meert et al. 2014).

    Early-type galaxies with M > 1010M consist of a sig-

    nificant fraction that are best modelled with both bulgesand disks, which are identified with the S0 (lenticular)galaxy population (Lackner & Gunn 2012; Mendel et al.2014; Meert et al. 2014). These galaxies have values ofMbul/M which are approximately log-normally distributedwith mean 0.7 and log-deviation 0.07 dex. A mild correlationbetween Mbul/M and M may be present for S0 galaxies(Mendel et al. 2014), which we neglect in this work.

    For 1010M < M . 1011.25M, approximately 75%

    of early-type galaxies are S0s and 25% are true ellipticals(Emsellem et al. 2011). These fractions change to 55% and45% respectively for larger stellar masses.

    While these results are quite approximate, and only derivedfor a low-redshift (z . 0.3) galaxy sample, we adopt themas a fiducial scheme for relating M to Mbul for z < 3. Thisscheme is roughly consistent with that used by S13.

    In the same way as accounting for scatter in the M Mbul relation raises the inferred SMBH mass function (e.g.,Aller & Richstone 2002), the bulge mass function inferredfrom the GSMF will be raised given scatter in relating Mto Mbul. Scatter in the Mbul M relations can be simplycombined with the scatter in the M Mbul relation bymodifying Equation 6 as follows:


    d logM= N


    log+ log(Mbul(M)), 2 + 22bul



    (10)where we assume bul = 0.1 for both early- and late-typegalaxies. In summary, the function Mbul(M) in our fiducialmodel is defined by

    Mbul(M) =

    0.2M, for 90% of late types,

    0.7M, for S0s

    M, for ellipticals


    We demonstrate the effects of possible errors in the fractionof early-type galaxies which are ellipticals by consideringcases where this fraction is reduced and increased by 50%.

    2.1.4 Relating Mbul to M

    Despite intense interest in evincing the M Mbul rela-tion over the last 15 years, the form of the relation re-mains uncertain (Kormendy & Ho 2013; Scott et al. 2013).Kormendy & Ho (2013) argue that the MMbul relation iswell modelled by a single power law for all galaxies contain-ing classical bulges which include ellipticals, S0s and spiralswith bulges displaying steep central light gradients. How-ever, Scott et al. (2013) find, using an extended version ofthe galaxy sample of Graham et al. (2011) and independentmeasurements of Mbul, that two power laws are required,with a break at Mbul = 3 10

    10M. A physical distinc-tion between the two power laws was identified by splitting

    c 2014 RAS, MNRAS 000, 110

  • 6 Ravi et al.

    the sample into cusp galaxies with steep power-law centrallight gradients and galaxies where cores, or light-deficitswith respect to a cusp, are present. Cusp galaxies are typi-cally of lower masses than core galaxies, and were found byScott et al. (2013) to have a steeper log-linear M Mbulrelation than core galaxies.

    While we the consider the M Mbul relations ofKormendy & Ho (2013) and Scott et al. (2013) equallylikely, we choose the simpler relation of Kormendy & Ho(2013) as a fiducial case. In Equation 10, Kormendy & Ho(2013) find = 4.07 0.05, = 1.16 0.08 and = 0.29.Scott et al. (2013) instead find = 15.37 0.18 and = 2.220.58 for Mbul 6 310

    10M and = 1.860.09and = 0.97 0.14 for Mbul > 3 10

    10M. As Scott et al.(2013) do not estimate the intrinsic scatter, we assume = 0.29 for the entire range of Mbul.

    We do not consider estimates of the M Mbul rela-tion made substantially prior to Kormendy & Ho (2013) andScott et al. (2013). Previous estimates are thought to be in-correct because of systematic errors in SMBH and bulgemass estimates, the absence of recently-measured SMBHmasses in brightest cluster galaxies, and the presence ofgalaxies without classical bulges in samples used to fit therelations (for details, see Kormendy & Ho 2013). Variousauthors infer modest redshift evolution in the M Mbulrelation such that the typical ratio M/Mbul may be upto a factor of 3 larger at z & 2 than the local value(Kormendy & Ho 2013, and references therein). This can beapproximately represented by letting = 0+log((1+z)

    K)with K = 1 and 0 as above. As a fiducial case, however,we assume the conservative value of K = 0.

    2.2 GW signals from binary and coalescing


    In this paper, we assume that all binary SMBHs are in circu-lar orbits that evolve only under losses of energy and angu-lar momentum to GWs. While the effects of binary SMBHenvironments and non-zero orbital eccentricities could mod-ify the GW characteristic strain spectrum from the form inEquation 1 at frequencies up to 108 Hz at the Earth, theseeffects are highly uncertain (Ravi et al. 2014, and referencestherein). For frequencies f > 108 Hz within the PTA band(e.g., at f = fyr), the characteristic strain spectrum doesindeed take the form of Equation (1), because the orbits ofall binaries radiating GWs at these frequencies are likely tohave circularised because of GW-driven evolution. Our as-sumption allows for direct comparison with the majority ofstudies on this topic (Jaffe & Backer 2003; Wyithe & Loeb2003; Enoki et al. 2004; Sesana et al. 2008; Ravi et al. 2012;Kulier et al. 2013; Sesana 2013), and for the GWB spectrumto be characterised by a single amplitude (Ayr).

    A circular binary SMBH radiates monochromatic GWsat twice its orbital frequency. We use standard expressionsfrom the literature for the rms GW strain amplitude, hs(e.g., Equation 7 of Sesana et al. 2008) radiated by a cir-cular binary, and the rms GW-induced sinusoidal variationsto the pulse times of arrival (ToAs) from radio pulsars, R

    (e.g., Equation 20 of Sesana et al. 2009). Both hs and R areaveraged over all binary orientation parameters. FollowingCordes & Jenet (2012), we approximate the strain ampli-tude of a memory burst from a coalescing binary SMBHas

    hmem = 3.3 1016







    , (12)

    where = (M, 1M, 2)/(M, 1 +M, 2) is the reduced massof the coalescing binary system.

    To calculate the GWB amplitude for a population ofbinary SMBHs, consider a multivariate density function, fX,for the observed binary SMBH coalescence rate, R, in termsof a k-component parameter vector X with components Xiindexed by an integer i:

    fX =




    Xi. (13)

    Following, e.g., Sesana et al. (2008), Ayr is given by

    Ayr =













    , (14)

    Here, dtdf



    )1for the domains of t and f under consid-


    2.3 Assembling the model

    Mergers between galaxies containing bulges with masses Mand M come in nine types, because the galaxies witheach mass may be either elliptical, S0 or late-type. In eachcase, a different prescription is required to identify the bulgemasses of the merging galaxies, and hence the masses of theSMBHs in the merging galaxies. Consider a merger betweena galaxy of type i, with mass M, and a galaxy of typej, with mass M, where i and j each denote either anelliptical, S0 or late-type galaxy. The fraction of cases wherethis merger will occur is given by

    , j

    , where is given byEquation 5 and the mass functions are evaluated at a massM. For early-type galaxies, , j is specified according tothe fractions of ellipticals and S0s at different masses, and forlate-type galaxies, , j is simply the fraction which containbulges. The SMBH masses corresponding to the galaxies oftypes i and j, M, i and M, j respectively, are describedby the probability density function in Equation 10 for Mbulgiven by Mbul, i(M) and Mbul, j(M) respectively. Hence,in order to calculate Ayr, we combine Equations 3, 4, 5, 10

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  • Gravitational-wave detection and SMBH astrophysics 7

    and 14 as follows:





    d logM






    d log

    d logM, i

    d logM, j




    d log()








    d logM, i



    d logM, j


    i, j

    , i, j

    h2s(M, i, M, j , z, fyr).


    We evaluate this integral numerically by summing over theintegrand in bins of logM, z, log , logM, i, and logM, j .To determine the predicted numbers of CW sources, wecount the numbers of individual binary SMBHs in each binwith different values of hs radiating GWs at fyr within anominal bandwidth of f = (10 yr)1. We also record therate of memory bursts in each bin with corresponding am-plitudes hmem. These latter operations are equivalent to nu-merically evaluating the conditional densities of GW sourcesin terms of hs and hmem.

    Equation 15 builds on the approach of S13 in two ways.First, we account for the effects of intrinsic scatter in theM Mbul relation and in relating M to Mbul. We alsoattempt to match the numbers of galaxy mergers of differenttypes to the measured GSMFs, rather than assuming thesame galaxy pair fractions for all types of mergers.


    3.1 The GWB amplitude

    We first calculated Ayr using Equation 15 given the fidu-cial prescriptions for (M, z), the GSMF, the scheme re-lating M and Mbul and the MMbul relation, as detailedin sections The resulting fiducial value for Ayrwas 1.3 1015. We then identified the possible ranges ofAyr consistent with the observational uncertainties in eachof (M, z), the GSMF and the M Mbul relation alone.This was accomplished by generating 600 realisations of Ayrwith the parameters of a single one of these quantities ran-domised and with the other terms in Equation 15 held fixedat their fiducial values. The process was then repeated withrandomisation individually in the other two quantities.

    Histograms of the resulting three samples of realisationsof Ayr are shown in the middle three panels of Figure 1,along with the fiducial value of Ayr (as a vertical dashedline). While the possible ranges of Ayr given observationaluncertainties in (M, z) and the GSMF are roughly equiv-alent, observational uncertainty in the M Mbul relationresults in a slightly larger range of possible Ayr values.

    We also considered the effects of adopting four modifica-tions to the fiducial model relating to parameters for whichcurrent observational constraints are poor. These modifica-

    Figure 1. Depiction of uncertainties in the value of Ayr cal-culated using Equation 15. The standardised histograms labelled(M, z), GSMF and MMbul show the distributions of 600realisations of Ayr given randomisation over the prescriptions forthe respective quantities alone. The vertical dashed line indicates

    the value of Ayr = 1.31015 resulting from the fiducial prescrip-tions for all quantities in Equation 15. The three arrows at the topof the Figure show how much this fiducial value varies given pos-sible systematic uncertainties in our model. From the bottom, thearrowheads indicate the values of Ayr corresponding to a possiblycontaminated early-type GSMF, galaxy merger timescales con-sistent with Kitzbichler & White (2008) and redshift-evolution inthe normalisation of the MMbul relation respectively (see textfor details). The standardised histogram labelled All shows thedistribution of 600 realisations of Ayr given randomisations overall uncertainties considered in this paper. For all four histograms,the 2.5% and 97.5% percentiles are shown as thick vertical bars.

    tions, which were introduced in Subsections, re-sult in the following values of Ayr:

    (i) When we decrease the early-type GSMF by a fac-tor of 1/3 to simulate an extreme case of contamination ofcolour-selected early-type galaxy samples by late-type galax-ies (e.g., edge-on spirals), we obtain Ayr = 10

    15. This rep-resents a decrease of 0.12 dex over the fiducial model.

    c 2014 RAS, MNRAS 000, 110

  • 8 Ravi et al.

    Figure 2. Comparison between our predictions for Ayr and thosefrom other works. The histogram is identical to that labelledAll in Figure 1. The vertical dashed line indicates our fidu-cial prediction of Ayr = 1.3 1015, and the arrows indicatesystematic uncertainties in Ayr (see the caption of Figure 1 fordetails). The dark grey horizontal bars show 68% confidence in-

    tervals for Ayr predicted by S13, Kulier et al. (2013), labelledK13, Ravi et al. (2014), labelled R14, and McWilliams et al.(2014), labelled M14. The light grey shaded area indicates the95% confidence PPTA upper limit on the GWB amplitude, set atAyr = 2.4 1015.

    (ii) When we adopt the massive galaxy merger timescalefrom Kitzbichler & White (2008) with the associated mass-and redshift-dependence, rather than from numerical simu-lations of galaxy mergers (L08), we obtain Ayr = 7.410

    16.This represents a decrease of 0.24 dex over the fiducialmodel.

    (iii) When we introduce a redshift-dependent normalisa-tion, , of the M Mbul relation with K = 1 such that thenormalisation is a factor of three greater at z = 2, we obtainAyr = 1.8 10

    15. This is an increase of 0.14 dex over thefiducial model.

    (iv) Finally, when we explore the effects of either reducingor increasing the fraction of early-type galaxies which areellipticals by 50%, we obtain an associated variation in Ayrof 5% (0.02 dex).

    The first three modifications are clearly significant, as com-pared to the fourth: we depict the resulting values of Ayrin the top panel of Figure 1. While the effects of modifica-tions (i) and (iii) are comparable in magnitude, adoptingthe galaxy merger timescales of Kitzbichler & White (2008)makes a large difference to the prediction of Ayr. This isexpected, because the Kitzbichler & White (2008) mergertimescales are roughly a factor of three longer than those ofL08. Indeed, modification (ii) results in a value of Ayr thatis lower than the 2.5% percentile of the distributions of Ayrvalues given the three observational uncertainties consideredso far.

    As an illustration of the full range of possible values

    of the GWB amplitude given the uncertainties consideredin (M, z), the GSMF and the M Mbul relation com-bined with modifications (1) (3) listed above, we gener-ated a new sample of 600 realisations of Ayr. In this case,we simultaneously randomised over (M, z), the GSMF andthe M Mbul relation as described above, and also (i) de-creased the early-type GSMF by a factor uniformly drawnfrom the interval [0, 1/3], (ii) set the galaxy merger timescaleat a value uniformly drawn between the predictions of L08and Kitzbichler & White (2008) (neglecting any mass- orredshift- dependence), and (iii) set the redshift-evolutionindex K of the normalisation of the M Mbul relationto a number uniformly drawn from the interval [0, 1]. Ahistogram of the resulting sample of realisations of Ayr isshown in the bottom panel of Figure 1, labelled All. Thelong tail to lower values of Ayr, which is not reflected inthe other histograms, is caused specifically by the inclusionof uncertainties in the galaxy merger timescale and in theearly-type GSMF. The magnitudes of these effects on Ayrare indicated by the arrows at the top of Figure 1. The 95%confidence interval on Ayr, considering all uncertainties, is5.1 1016 < Ayr < 2.4 10

    15.We next compare our results for Ayr with earlier pre-

    dictions. In Figure 2, we again show the histogram of re-alisations of Ayr corresponding to randomisation over alluncertainties, as well as the values of Ayr correspondingto modifications (i) to (iii) listed above. Above these, weshow the 68% confidence intervals on Ayr from four re-cent, independent models for the binary SMBH population(S13; Kulier et al. 2013; Ravi et al. 2014; McWilliams et al.2014). The predictions that we consider all account forthe most recent determinations of the M Mbul relation(Kormendy & Ho 2013; Scott et al. 2013).

    The range of possible values of Ayr predicted by S13 isconsistent with (albeit somewhat broader than) the rangewe predict given all uncertainties that we consider in thispaper. Both the present work and S13 attempt to synthe-sise all uncertainties in quantities relevant to characterisingthe SMBH-SMBH coalescence rate, and use the same un-derlying model assumptions to predict the GWB amplitude.Our range of predictions is less extended than that of S13because of the greater uncertainty assumed by S13 in theGSMF and the galaxy merger rate.1

    A semi-analytic approach (Guo et al. 2011) was used byRavi et al. (2014) to predict SMBH-SMBH coalescence rateswithin the Millennium simulation (Springel et al. 2005),coupled with prescriptions for binary SMBH orbital evo-lution in stellar environments (Sesana 2010). The resultsof Ravi et al. (2014) indicate that the characteristic strainspectrum may be attenuated relative to the case of circu-lar binary orbits and GW-driven evolution at frequenciesf . 108 Hz. However, the 68% confidence interval on thecharacteristic strain spectral amplitude at a frequency of fyris consistent with the range of values of Ayr we find.

    1 Some methodological differences also exist between the presentwork and S13 in how different realisations of Ayr were obtained.

    c 2014 RAS, MNRAS 000, 110

  • Gravitational-wave detection and SMBH astrophysics 9

    Figure 3. Left: Values of Ayr from binary SMBHs created in major mergers involving galaxies of different stellar masses. The squaresindicate the fiducial model result, and the vertical error bars indicate 95% confidence intervals from 600 realisations of the model withall uncertainties that we account for. Right: Values of Ayr from binary SMBHs in six redshift bins in the interval 0 < z < 3. Thesquares and error bars are as in the left panel. The redshift intervals correspond to the ranges within which the GSMF was evaluated byMuzzin et al. (2013).

    The prediction of Kulier et al. (2013) is derived fromhydrodynamic numerical galaxy formation simulations incluster and field environments, but may be biased relativeto semi-analytic galaxy formation models implemented inlarge-volume numerical dark matter simulations because ofthe specific choice of overdense and underdense regions tostudy. However, the prediction of Kulier et al. (2013) nat-urally includes a particiularly sophisticated treatment ofgalaxy merger timescales.

    McWilliams et al. (2014) suggest a model for the bi-nary SMBH population which includes the assumption thatall evolution in the early-type GSMF at z < 1 is driven bygalaxy mergers; however, their predicted GWB amplitudeappears to be inconsistent with current PTA constraints(Shannon et al. 2013). This model would necessarily includea shorter galaxy merger timescale than that predicted byL08 in order to maintain consistency with the observednumbers of merging galaxies. Overall, besides the study ofMcWilliams et al. (2014), it is encouraging that differentmodels appear to agree on the amplitude of the characteris-tic strain spectrum from binary SMBHs. In particular, theupper ends of most predicted ranges of Ayr all appear to beconsistent.

    In Figure 2, we also depict the best existing 95% con-fidence upper limit on Ayr from Shannon et al. (2013) as ashaded region. Some realisations of Ayr given observationaluncertainties in our model are inconsistent with this upperlimit. However, the upper limit is generally consistent withour model given all uncertainties.

    In Figure 3, we plot the values of Ayr predicted by thefiducial model in different ranges of M (left panel) andz (right panel). We also show the 95% confidence inter-vals on these values given all uncertainties we consider. Thegalaxy mass ranges correspond to the values of M of thelarger galaxies in mergers. The dominant contributions to

    the GWB are from binary SMBHs formed in mergers involv-ing galaxies withM & 510

    10M, and from binary SMBHsat redshifts z . 1.5. The confidence intervals that we providefurther suggest that contributions to the GWB from outsidethese ranges are not significant.2 Finally, binary SMBHs cre-ated in mergers involving at least one late-type galaxy corre-spond to Ayr = 4.7 10

    16 , whereas mergers involving onlyearly-type galaxies correspond to Ayr = 1.2 10

    15. Hence,within our model, the GWB is likely to be dominated bygalaxy mergers involving only early-type galaxies (S0s andellipticals).

    3.2 Individual GW sources: continuous waves and

    memory bursts

    In the process of evaluating Equation 15, we also calcu-lated the numbers of individual binary SMBHs that producemonochromatic (CW) GW signals, along with the numbersof GW memory bursts emitted during SMBH-SMBH co-alescence events. We counted individual binaries emittingGWs at frequencies f = fyr in a frequency bin of widthf = (10 yr)1 and evaluated the numbers of binaries withdifferent GW strain amplitudes hs. These results are shownin the left panel of Figure 4 for the fiducial model as well asfor two variations to the fiducial model (modifications (ii)and (iii) listed above). We also show results for the fiducialmodel while restricting the source counts to binaries at red-shifts z < 1 and with the more massive progenitor galaxy

    2 While the most massive galaxies do not appear to contributesignificantly to the GWB, it is apparent from, e.g., Figure 6of Muzzin et al. (2013) (see also Baldry et al. 2012) that theSchechter function fits to the early-type GSMFs under-predictthe observed GSMF at masses M & 1011.5.

    c 2014 RAS, MNRAS 000, 110

  • 10 Ravi et al.

    Figure 4. Left: the counts of individual sources at and above given GW strain amplitudes (hs) at a GW frequency of fyr in a frequency binof width f = (10 yr)1. Right: the numbers of GW memory bursts per year at and above given strain amplitudes (hmem, Equation 12).In both panels, the results of the fiducial model are shown as thick black solid curves, the results from a model with maximal redshiftevolution in the MMbul relation (K = 1 corresponding to in Equations 6 and refeq:c7e10 increased by a factor of three at z = 2) areshown as dotted red curves, and the results from a model with galaxy merger timescales consistent with Kitzbichler & White (2008) areshown as blue dashed curves. The green thin solid curves represent source counts for the fiducial model evaluated with the restrictionsz < 1 and M > 1011M.

    mass M > 1011M. The restricted source counts are iden-

    tical to the full source counts for hs & 2 1015. From

    Sesana et al. (2009), the characteristic amplitude of the si-nusoidal ToA variations induced by a binary SMBH withstrain amplitude hs at f = fyr, over a 10 yr observation, isR = 21(hs/10

    15) ns.

    Scaling these CW source counts to other GW frequen-cies is non-trivial. The GW strain amplitude of a binarySMBH radiating at a frequency f can be expressed as hs =hs, yr(f/fyr)

    2/3, where hs, yr is the strain amplitude radiatedby that binary at a frequency fyr. Furthermore, the totalnumber of binaries per unit frequency radiating GWs at afrequency f is related to the number of binaries per unit fre-quency radiating GWs at fyr by the factor (f/fyr)

    11/3, as-suming GW-driven binary orbital evolution. Then, the num-ber of binaries per unit frequency emitting GWs at or abovea strain amplitude of hs, at a frequency f , may be written asn(f, hs) = n(fyr, hs(f/fyr)

    2/3)(f/fyr)11/3. For example,

    while the fiducial model predicts 102 CW sources withhs > 10

    15 in a frequency bin of width f = (10 yr)1 atf = fyr, this prediction changes to 0.1 sources at f = fyr/5with hs > 10

    15 in the same frequency bin width.

    We can hence directly compare our predicted CWsource counts with the work of Sesana et al. (2009). Theseauthors considered a wide variety of SMBH growth scenar-ios within the framework of a semi-analytic model for galaxyformation (Bertone et al. 2007) implemented in the Millen-nium simulation results (Springel et al. 2005). We directlycompare predictions for the number of binary SMBHs in-ducing ToA variations with characteristic amplitudes R >30 ns. For consistency, we consider an observation time spanof T = 5 yr and GW frequencies f > 3 109 Hz, and in-

    tegrate over the number of sources per unit frequency withR > 30 ns in the range 3 10

    9 107 Hz (integrating tohigher frequencies does not significantly alter our results).We neglect the issue of whether these signals are resolvablegiven the presence of a GWB. We predict 0.6 CW sourceswith R > 30 ns for our fiducial model, 0.1 CW sources fora pessimistic model assuming the galaxy merger timescalesof Kitzbichler & White (2008), and 1.2 CW sources for ouroptimistic model with significant redshift-evolution in theM Mbul relation. Sesana et al. (2009) predict between0.05 and 3 such sources (their Figure 3), which is consistentwith our results.

    We also predicted the numbers of binary SMBH co-alescence events per observed year at or above a givenGW memory burst amplitude, hmem (see Equation 12) forhmem > 10

    16. The results are shown in the right panel ofFigure 4, again for the fiducial model and two variationsto this model. We also again show results for the fiducialmodel with the restrictions of z < 1 and M > 10

    11M;for hmem & 6 10

    16, the restrictions make no significantdifference.

    In summary, the expected numbers of individual GWsources predicted by our empirical binary SMBH model aresmall. At most 1 CW source is expected to induce ToAvariations with characteristic amplitudes > 30 ns over a 5 yrobservation time span. Also, approximately one GW mem-ory burst with hmem > 5 10

    16 is expected every 1000 yr.

    c 2014 RAS, MNRAS 000, 110

  • Gravitational-wave detection and SMBH astrophysics 11



    4.1 The GWB from binary SMBHs

    The future sensitivities of PTAs to the GWB are thesubjects of ongoing research (e.g., Siemens et al. 2013;Moore et al. 2014; Hobbs et al. 2014). For example, futurepulsar observing systems and cadences, new pulsar discover-ies, the effects of the interstellar medium and pulsar timingnoise characteristics, all of which significantly affect PTAsensitivities, are difficult to forecast because of a lack ofquantitative, predictive models. An idealised treatment ofthe problem by Siemens et al. (2013) suggests that, for theNANOGrav collaboration, a GWB with amplitude Ayr =1015 may be detectable before the year 2020. We note thatSiemens et al. (2013) assumed that the GWB characteristicstrain spectrum has the power law form given in Equation 1.We find in this paper that the GWB amplitude is likely tobe in the range 5.1 1016 < Ayr < 2.4 10

    15 with 95%confidence. If the GWB amplitude were to lie in the upperpart of this range, as is expected given the more commonlypreferred major galaxy merger timescale (L08), we suggestthat detecting a GWB from binary SMBHs is indeed anattainable, short-term goal for PTAs.3

    What can PTA upper limits on or detections of theGWB reveal about the determinants of the GWB ampli-tude? The GWB may be parameterised by a single number,Ayr (at least at GW frequencies f & 10

    8 Hz; Ravi et al.2014), the value of which is dependent on myriad quanti-ties. Useful information can be gleaned if one of these quan-tities is particularly unconstrained otherwise. For example,if we remain agnostic with respect to the galaxy mergertimescale, a particular value of this timescale would cor-respond to a range of possible GWB amplitudes given ourknowledge of all the other determinants of Ayr. Then, a PTAconstraint on Ayr would correspond to a constraint on thegalaxy merger timescale, given the assumptions inherent inour model. Through such exercises, PTAs could directly im-pact our understanding of galaxy and SMBH growth, in amore general sense than by testing specific GWB models us-ing PTA data. We leave a demonstration of such techniquesfor future work.

    4.2 CW signals from individual binary SMBHs

    Future PTA observations with planned telescopes suchas the Five Hundred Metre Aperture Spherical Telescope(FAST, Li, Nan & Pan 2013) and the Square Kilometre Ar-ray (SKA, Cordes et al. 2004) may include up to 100 pul-sars with timing noise standard deviations of 100 ns (Lazio2013; Hobbs et al. 2014). Ellis et al. (2012) constructed the-oretical PTA sensitivity curves using simulated data setswith both 100 arbitrarily-located pulsars or 17 pulsars at

    3 If we calculate the range of possible Ayr values given all un-certainties, while assuming the L08 galaxy merger timescales, wefind Ayr > 9 1016 with 95% confidence.

    the locations of the best-timed pulsars observed by theNANOGrav collaboration, in all cases with timing noisestandard deviations of 100 ns and 5 yr observation times.These sensitivity curves, shown in their Figure 5, representthe values of hs at different frequencies at which the prob-ability of a false detection was less than 104 in 95% ofrealisations of their simulated data sets. Importantly, thesensitivity curves were averaged over all source positions andorientations, and account for pulsar parameter fitting. Wepredict the numbers of detectable sources for PTAs withthese sensitivity curves by evaluating the following integral:

    Ndetect =

    107 Hz

    (10 yr)1

    dF [hsens(f)]

    dfdf, (16)

    where hsens(f) is the sensitivity curve anddF (hsens)

    dfis the

    predicted number of sources with strain amplitudes hs >hsens(f) per unit frequency at a frequency f . The sensitivi-ties of PTAs to CW sources are generally poor for frequen-cies f & 107 Hz and few sources are expected at thesefrequencies.

    Using our predictions for the numbers of CW sources,we evaluate dF (hsens)

    dfby scaling the predictions as described

    in 3.2. Then, for the fiducial model and for the two sen-sitivity curves of Ellis et al. (2012) corresponding to theircoherent F-statistic, we obtain predictions of 0.07 and 1.3detectable sources for the 17- and 100-pulsar cases respec-tively. For the restricted fiducial model, corresponding onlyto sources with z < 1 and M > 10

    11M these reducemarginally to 0.06 and 1 source respectively. For the opti-mistic case with strong redshift-evolution of the M Mbulrelation, we obtain predictions of 0.2 and 2.8 detectablesources for the 17- and 100-pulsar cases respectively. Incontrast, the current PPTA sensitivity curve produced byZhu et al. (2014) corresponds to . 104 detectable sources.Noise caused by the summed GW signal from the bi-nary SMBH population will further increase the difficultyof detecting individual binaries (e.g., Sesana et al. 2009;Ravi et al. 2012).

    4.3 GW memory bursts from coalescing binary


    A PTA data set with 20 pulsars timed with a precisionof 100 ns for 10 yr is sensitive to memory bursts with am-plitudes hmem > 5 10

    15 over 70 80% of the dataspan (van Haasteren & Levin 2010; Cordes & Jenet 2012).As the sensitivity of such an idealised PTA to memorybursts scales roughly as the square root of the number ofpulsars (van Haasteren & Levin 2010), a PTA with 100 pul-sars timed with 100 ns precision for 10 yr may be sensitive tomemory bursts with hmem > 2 10

    15. However, our modelsuggests that only 105 bursts with hmem > 5 10


    and 103 bursts with hmem > 2 1015 are expected over

    10 yr. Thus, under the model presented here, GW memorybursts from coalescing binary SMBHs do not represent vi-able sources for PTAs.

    c 2014 RAS, MNRAS 000, 110

  • 12 Ravi et al.


    Our predictions for the GWB amplitude, Ayr, are conser-vative within their respective scenarios, for a number ofreasons. (i) We do not account for minor galaxy merg-ers with stellar mass ratios < 1/3, or for mergerswhere the more massive galaxy has a mass M < 10

    10M.(ii) We do not consider the possibility of gas accretiononto SMBHs prior to coalescence during galaxy mergers(e.g., Van Wassenhove et al. 2012), which would raise theSMBH masses and hence the emitted GW amplitudes (e.g.,Sesana et al. 2008). (iii) The most massive galaxies are typ-ically found in cluster environments, where times betweengalaxy mergers may be shorter (cf. Lotz et al. 2013), im-plying a higher merger rate for these galaxies and hence ahigher GW signal. However, we do not expect the inclusionof these factors to significantly affect our predicted GWBamplitudes. We reiterate that the effects of interactions be-tween binary SMBHs and their environments are unlikely toaffect the predictions for the GWB amplitude at frequenciesf & 108 Hz (Sesana 2013b; Ravi et al. 2014), such as at fyr.This is because the orbital evolution of binary SMBHs radi-ating GWs at these frequencies is expected to be predomi-nantly GW-driven, which further leads to the circularisationof the orbits.

    Of all sources of uncertainty we consider in pre-dicting the GWB amplitude given relevant observationalquantities, the choice of galaxy merger timescale domi-nates the range of possible GWB amplitudes. Furthermore,the merger timescale may be even more uncertain thanthe range spanned by the predictions we consider (L08;Kitzbichler & White 2008). The simulations of L08 wereconducted only for mergers between gas-rich disk galaxies,some of which contained small bulges, whereas we find thatthe GWB is likely dominated by binary SMBHs formed inmergers solely between early-type galaxies. Further theoret-ical studies of galaxy merger timescales for early-type sys-tems are clearly required in order to better predict the GWBamplitude. The dominance of low-redshift (z . 1.5) early-type major galaxy mergers of massive (M & 5 10

    10M)galaxies in determining the GWB amplitude is a further im-portant consequence of our work for both theoretical andobservational studies of galaxy mergers aimed at informingPTA research.

    The other significant source of uncertainty in our pre-dictions is in the M Mbul relation, both in its local formand in its possible redshift-evolution. In contrast to uncer-tainty in the galaxy merger timescale, it is likely that thisuncertainty will only be resolved through further observa-tions which significantly expand the sample of known SMBHmasses. Promisingly, Davis et al. (2013) report that hun-dreds of SMBH mass measurements may be possible withthe Atacama Large Millimetre Array (ALMA).

    Under what circumstances could the GWB amplitudelie outside the range we predict given all uncertainties thatwe consider? The predicted range of GWB amplitudes,5.1 1016 < Ayr < 2.4 10

    15, encompasses all purelyobservational uncertainties, as well as uncertainty ranges

    that we set for other quantities for which observational con-straints are poor, such as the galaxy merger timescale. Itmay be possible that these latter ranges are incorrect. Fur-thermore, not all galaxies may host a central SMBH, as wehave assumed. The interaction between a binary SMBH anda third SMBH would likely cause the least massive SMBH tobe ejected (e.g., Gerosa & Sesana 2014), lowering the num-ber of coalescing SMBHs. If not every massive galaxy atz 1 formed with a central SMBH, the GWB amplitudewould again be lowered. It may also be possible that binarySMBHs do not always coalesce on timescales less than thetimes between galaxy mergers.

    The presence of a few strong GW emitters among thebinary SMBH population implies that some excess, non-Gaussian scatter will be present in the GW signals producedby this population. The magnitude of this excess scatter inAyr depends on exactly how many binary SMBH systemscontribute significantly to the GWB. Using a semi-analyticgalaxy formation model implemented in the Millennium sim-ulation (Guo et al. 2011), Ravi et al. (2012) suggested thatthe statistics of ToA variations induced by GWs from binarySMBHs are mildly non-Gaussian for frequencies f > fyr/5because of appreciable contributions to the squared charac-teristic strain spectrum, h2c(f), from individual binaries atevery GW frequency. Figure 5 shows the number of binarySMBHs in our fiducial model corresponding to galaxy merg-ers with primary stellar masses greater than or equal to agiven M (top), as well as the fractions of A

    2yr contributed

    by these binaries (bottom). We show in particular binariesradiating at a GW frequency of fyr in a frequency bin ofwidth f = (10 yr)1. Our fiducial model suggests that thecontributions of individual GW sources to h2c(f) are lowerthan estimated by Ravi et al. (2012). For example, the mod-elling in Ravi et al. (2012) found that one source contributed 50% of h2c(f) at a frequency of 2fyr/3 in a frequencybin of width (5 yr)1 (their Figure 2). In contrast, our em-pirical modelling in this paper suggests that the strongest 400 sources in such a frequency bin contribute 50% ofh2c(2fyr/3).

    This work and Ravi et al. (2012) clearly predict differ-ent numbers of the most massive binary SMBHs. While thisdiscrepancy will only be resolved with GW observations,we point out that the Schechter functions for the GSMFsthat we use under-predict observed galaxy counts at thehighest masses and the lowest redshifts (Baldry et al. 2012;Muzzin et al. 2013). Hence, it is possible that our modelunder-represents the contributions of the most massive bi-nary SMBHs to the total GW signal. Differing typical galaxymerger mass ratios in cluster and field environments (e.g.,Lotz et al. 2013) are a further complicating factor.


    In this paper, we predicted the strength of the GWB from bi-nary SMBHs and the occurrence of individual binary SMBHGW sources. Our approach was to use a selection of recentobservational estimates for the average times between major

    c 2014 RAS, MNRAS 000, 110

  • Gravitational-wave detection and SMBH astrophysics 13

    Figure 5. Top: The numbers of binary SMBH sources predictedby our fiducial model radiating at a GW frequency of fyr in a fre-quency bin of width f = (10 yr)1 at and above given values ofM. Bottom: The fractions of A2yr contributed by binary SMBHsat and above given values of M.

    mergers for galaxies with M > 1010M and z < 3 and for

    the GSMFs of early- and late-type galaxies in this mass andredshift range. We combined these quantities with empiri-cal relations between galaxy and bulge stellar masses andbetween bulge and SMBH masses.

    We find that while current PTAs are unlikely to be sen-sitive to individual binary SMBHs, a PTA consisting of 100pulsars timed with 100 ns precision for 5 yr will be sensitiveto up to 3 binary SMBHs. Such a PTA may be achiev-able with the SKA (Lazio 2013), but is possibly beyond thecapabilities of FAST (Hobbs et al. 2014). Even such a PTAwill, however, have a less than 0.1% chance of detecting aGW memory burst from a coalescing binary SMBH. Thus,we conclude that while individual binary SMBHs may bedetectable with a PTA based on next-generation radio tele-scopes, memory bursts from coalescing SMBHs are not likelyto be detectable with any envisaged PTA.

    We predict that the characteristic strain amplitude ofthe GWB lies in the range 5.1 1016 < Ayr < 2.4 10


    with 95% confidence, accounting for a variety of uncertain-ties. The upper end of the predicted amplitude range is

    equivalent to the best published 95% confidence upper limiton the GWB amplitude (Shannon et al. 2013). This rein-forces the conclusion of Shannon et al. (2013) that somemodels for the binary SMBH population that are consis-tent with current electromagnetic observations are alreadyinconsistent with PTA constraints on the GWB.

    The dominant uncertainty in predicting the GWB am-plitude appears to be caused by differences in theoreticalpredictions for the major merger timescale of massive galax-ies. Higher values within our predicted range for Ayr cor-respond to the more commonly preferred choice of galaxymerger timescale (L08); GWB amplitudes Ayr > 10

    15 arewithin the sensitivity ranges of current and future PTAs.We strongly urge further work on quantifying the galaxymerger timescale, in particular for the mergers between mas-sive early-type galaxies at redshifts z < 1.5 which are likelyto host the dominant contributors to the GWB. The othersignificant uncertainty in our predictions is in the local formand possible redshift-evolution of the M Mbul relation.PTA upper limits on or detections of the GWB may be ableto meaningfully improve our knowledge of such otherwisepoorly constrained facets of the formation and evolution ofgalaxies and SMBHs.


    The authors thank Alberto Sesana for useful discussionsand Justin Ellis for sharing results on predicted sensitivitycurves. The authors also acknowledge the comments of theanonymous referee, which helped to significantly improvethe manuscript. V.R. is a recipient of a John Stocker Post-graduate Scholarship from the Science and Industry Endow-ment Fund and J.S.B.W. acknowledges an Australian Re-search Council Laureate Fellowship. GH is supported byan Australian Research Council Future Fellowship. Thiswork was performed on the swinSTAR supercomputer atthe Swinburne University of Technology.


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    1 Introduction2 An empirical model for GWs from binary SMBHs2.1 The SMBH-SMBH coalescence rate2.2 GW signals from binary and coalescing SMBHs2.3 Assembling the model

    3 Results3.1 The GWB amplitude3.2 Individual GW sources: continuous waves and memory bursts

    4 Implications for GW detection with PTAs4.1 The GWB from binary SMBHs4.2 CW signals from individual binary SMBHs4.3 GW memory bursts from coalescing binary SMBHs

    5 Discussion6 Conclusions