The Origins of the Intracluster Light 1. Introduction The discovery and quantification of the diffuse intracluster starlight (ICL) in clusters (e.g. Zwicky 1951; Uson et al. 1991; Feldmeier et al. 2002, 2004; Gal-Yam et al. 2003; Aguerri et al. 2006) has provided a new tool with which to study the history and structure of galaxy clusters in greater detail than has hitherto been possible. ICL consist of stars in galaxy clusters which have been gravitationally stripped from their parent galaxies via cluster galaxies interacting with other galaxies or with the cluster potential (e.g. Napolitano et al. 2003; Willman et al. 2004; Arnaboldi et al. 2004; Rudick et al. 2006, hereafter Paper I). As a product of the dynamical interactions within the cluster, the ICL has the potential to reveal a great deal of information about the cluster s accretion history and evolutionary state, as well as the mass distribution of the cluster galaxies and the cluster as a whole. The quantity, morphology, and kinematics of the ICL each hold potentially useful information on the cluster s evolution, and processes affecting individual galaxies can be traced using individual ICL streams. In Paper I we used N-body simulations to study the evolution of ICL in clusters as they evolve in a ΛCDM universe. In that paper we were able to isolate close encounters between galaxies and groups of galaxies within the cluster as an important mechanism driving the production of ICL. We now propose to continue our analyses of these clusters in order to further understand the nature of the ICL. Our primary motivation is to better relate the observable features of the ICL to physical mechanisms which produce it, in order to better understand the structure and evolution of galaxy clusters and their constituent galaxies. Our objectives will be to study the kinematics of the ICL as could be sampled observationally by planetary nebulae, to study the relationships between the various observational and theoretical definitions of ICL currently in use, and to study how the properties ICL are effected by the intrinsic properties of its host cluster. 2. Simulated PNe Observations of ICL Kinematics 2.1. Introduction A recent advance in the study of ICL has come with spectroscopic observations of planetary nebulae from the intra cluster stellar population (ICPNe), providing a tool with which to study the kinematics of the ICL (Arnaboldi et al. 2004; Gerhard et al. 2005). While currently these studies consist of only small samples of ICPNe, covering a small fraction of the angular extent of the clusters, they have already had some success in identifying intracluster stars as having velocities distinct from neighboring cluster galaxies, even when these stars are projected onto the face of a bound galaxy. As sample sizes and areal coverage improve, these data will provide valuable information on the relaxation state, distribution, and origins of the ICL.
2 With these new observations of ICL kinematics have come corresponding efforts to model the ICL dynamics in cluster simulations. Both Napolitano et al. (2003) and Willman et al. (2004) measure the velocity distribution of the ICL along individual lines of sight at z = 0, and find significant substructure, indicated by non-gaussian distributions. Each concludes that the substructure exists due to incomplete phase mixing of the tidal streams produced by strong interactions between galaxies and groups of galaxies within the cluster, which are the source of the ICL. Additionally, Willman et al. (2004) attempt to relate the ICL velocity distribution to the underlying mass distribution of the cluster, and find that the ICL velocity distribution yields systematically low estimates of the cluster mass. Sommer-Larsen et al. (2005) also studied the kinematics of the ICL and found that the ICL is dynamically colder than the cluster galaxies. However, Sommer-Larsen (2006) found that dynamical state of the ICL is related to the dynamical state of the cluster itself. In an effort to explore this burgeoning new field, we propose to extend our analyses of the galaxy cluster simulations initially presented in Paper I to study the kinematic properties of the ICL. An important result of Paper I is the conclusion that the dominant mechanism driving the production of ICL is the tidal stripping of material during strong encounters between groups of galaxies within the cluster. It was also shown that the morphology of the ICL evolves from one dominated by long, thin filaments early in the cluster s history to becoming a more diffuse halo around the cluster center at later times. Both of these observations are consistent with an evolutionary scenario in which ICL is tidally stripped from galaxies and groups in dynamically cold tidal streams, and becomes slowly phase mixed as the cluster evolves. This is a similar situation to the one studied by Harding et al. (2001) in which dwarf galaxies are tidally disrupted in a static Milky Way potential, and is precisely the scenario suggested by both theoretical (Napolitano et al. 2003; Willman et al. 2004) and observational (Arnaboldi et al. 2004) studies of ICL. While there is now broad agreement on the general mechanism which gives rise to the ICL, many questions remain about how the phase space structure evolves and how it relates to the dynamical state of cluster, as well as how the interactions which create the ICL affect the specific evolution of the galaxies from which it is stripped. If the the ICL evolves from a complex of cold tidal streams into a single phase mixed halo, on what time scales does this mixing occur? Can we use the cold tidal streams to map the orbits, and thus the interaction histories, of the cluster galaxies? Is the relaxation state of the ICL related to the accretion history and dynamical age of the cluster? With potentially thousands of discrete kinematic tracers, can we use the ICL to determine the underlying gravitational potential of the cluster? We intend to focus our efforts on three major areas: identifying ICL substructures; following the detailed origins and evolution of individual substructures; and using the dynamical state of the ICL to determine cluster mass and evolutionary state. Detailed descriptions of these and other analyses are provided below. 2.2. Simulated Observations A key element of any analysis of astronomical simulations is devising methods to observe the simulated data, in order to extract the desired information. Furthermore, our group in particular puts a prime
3 focus on understanding the simulations using observationally tractable data, i.e. data which could actually be observed in astronomical objects using current or near-future technology. In this case in which we are studying the dynamics of ICL, this means observing the projected positions and line-of-sight velocities of ICPNe. We have already begun work on developing several tools with which to study our simulated clusters, given these observational constraints. 2.2.1. ICL Phase Space Evolution In Paper I we presented surface brightness maps of our clusters using an arbitrary projection, and showed that the observed evolution of the clusters is independent of projection angle. Using the same projections, Figures 1 3 show preliminary maps of the mean velocity and velocity dispersion of the particles at each position in one of these simulated clusters. In this way we can examine the phase space structure of the ICL throughout the cluster. Furthermore, we can create sequences of these maps at consecutive timepoints in the clusters evolution in order to watch the phase space structure of the ICL evolve. Using the surface brightness maps presented in Paper I, we will be able to correlate the phase space evolution of the cluster with the observed dynamical history of the cluster, much as we did with the ICL production rate in Paper I. Furthermore, we intend to create Fabry-Pérot style data cubes in velocity space, in which we will observe the particles in a series of small velocity intervals, in order to get a more detailed view of the phase space structure of the cluster at any given time point. Because clusters can only be observed in twodimensional projection, measurements of the velocity distribution provide a unique method with which to deproject the cluster, if only in phase space rather than physical space. However, this technique is useful for separating dynamically distinct sub-groups within the cluster, and Arnaboldi et al. (2004) showed that phase space measurements can separate ICL from bound cluster galaxies projected onto the same line of sight. A final technique that will prove useful in studying the evolution of ICL will be a scheme of particle tagging, whereby we can trace the evolution of individual particles. This will allow us to understand the origins of the ICL in much more detail and trace the evolution of individual streams. For example, we can identify tidal streams consisting of particles from a single galaxy, and plan to develop tools with which to discriminate these streams from the the broader ICL distribution, since the particles of the stream should be clustered in phase space. Not only could we measure the evolution and lifetimes of these streams, but could also attempt to use them to trace out the orbits of their parent galaxies within the cluster. 2.2.2. Line-of-Sight Velocity Distribution Another technique with which we will study the phase space structure of the ICL is by measuring the velocity distribution of particles along individual lines of sight through the cluster. The first step in the analysis of the velocity distributions will be to develop tests for the presence of sub-structure, building on the work of Harding et al. (2001). Additionally, we can compare the velocity mean and dispersion of ICL structures to the velocity field of the cluster galaxies and the underlying dark matter, as well as to that
4 predicted for a virialized ICL population. These measurements will not only give us information on the origins and evolution of the ICL structures, but also allow us to relate the ICL to the dynamical state of the cluster as a whole. We can also use this data to investigate the possibility of using the ICL as an estimator of the cluster virial mass, with which to complement other mass estimation methods, such as galaxy velocity dispersion or X-ray gas temperatures. Observations of ICPNe are a potentially unique tool for mass estimation because they provide a very large number of independent kinematic tracers (Ford 2002; Romanowsky 2006a). For example, using the scaling between number of observed PNe and luminosity calculated for the Virgo cluster ICL by Durrell et al. (2002), the simulated clusters from Paper I would contain as many as 4300 observable ICPNe. 2.2.3. Relating the Simulations to Observations Observations of ICL kinematics are limited to line-of-sight velocities of ICPNe, which sample only a very small fraction of ICL stellar population. In order to make our observations of the simulations more directly applicable to actual astronomical observations, we can use the planetary nebulae luminosity function (PNLF; Durrell et al. 2002), along with our surface brightness maps, to normalize the number of ICL particles to the number of ICPNe which we would expect to observe. Thus we can sample the velocity distribution of the cluster along specific lines of sight in order to make our measurements directly comparable to observations of ICPNe. In order to further relate our simulations to current ICPNe observations, we will investigate the effects of limited areal coverage on our estimates of cluster properties, in order to determine optimal observational coverage needed in order to study real clusters. A related question which we will also seek to answer is how many ICPNe velocities are needed to fully sample the phase space distribution of the cluster. This has important implications for determining the optimal strategies of observational studies of clusters, since the number of ICPNe observed is directly related to the limiting magnitude of the survey, i.e. the number of observed PNe increases as the limiting magnitude of the survey increases, according to the PNLF. 2.3. Summary Recent measurements of the line-of-sight velocities of ICPNe provide a unique new tool for measuring the kinematic structure of the ICL. Using the cluster simulations presented in Paper I, we can attain a better understanding of how these observations can be used to identify phase-space substructure in the ICL. We will attempt to use this substructure to probe the dynamical history and relaxation state of the cluster, including tracing the orbits of individual galaxies and measuring the cluster mass distribution. Finally, we will be able to influence future observational strategies by determining the optimal areal coverage and depth of observations needed to provide the number of planetary nebulae necessary to fully describe the phase space distribution of the ICL.
5 3. Comparison of ICL Measurement Methodologies 3.1. Introduction A major issue which has arisen as the quantitative study of ICL has evolved is that there is no definitive definition of what exactly ICL is and how it should be measured. Perhaps the most physically appealing definition of ICL relates to its characteristic energy: ICL consists of stars which are bound to the gravitational potential of the cluster as a whole, but to no particular galaxy in the cluster. While this method of ICL identification has been used in several theoretical studies (Murante et al. 2004; Willman et al. 2004; Sommer-Larsen et al. 2005; Sommer-Larsen 2006), it requires not only six-dimensional phase space data for each ICL stellar tracer, but the detailed gravitational potential of the cluster, neither of which is observationally accessible. Most observational treatments use the surface brightness distribution of the cluster to define ICL. In rich clusters of galaxies, especially those dominated by a central cd galaxy, a common technique is to fit and subtract surface brightness profiles to the galaxies, with the residual luminosity taken to be the ICL (e.g. Vilchez-Gomez et al. 1994; Gonzalez et al. 2000; Feldmeier et al. 2002). However, different researchers have devised many different schemes with which to perform this galaxy subtraction, meaning that this technique does not yield a unique separation between galactic and ICL luminosity. A more welldefined definition of ICL that has been used is to separate ICL from galaxies using a surface brightness limit (Feldmeier et al. 2004; Paper I). Thus, all luminosity brighter than the surface brightness limit is classified as galactic, and all detected luminosity below the limit is the ICL. While a well-defined observable, this definition remains overly simplistic. The chosen chosen surface brightness limit is essentially arbitrary, and it is quite unlikely that a single surface brightness limit would be equally applicable to all clusters in all evolutionary stages. Yet another definition of ICL that has been used in the literature discriminates between galaxies and ICL based on their observable kinematic properties. As discussed in 2, Arnaboli et al. (2004) were able to distinguish ICPNe from galactic PNe projected onto the same line of sight by selecting ICPNe as having velocities inconsistent with that of the bound galaxy. Uniquely discriminating ICL from galactic luminosity projected onto the same line of sight is clearly impossible from surface brightness data alone. However, the one-dimensional velocity information available from ICPNe is not sufficient to determine the binding energy of the objects. Additionally, measuring the kinematics of ICPNe on large scales is a quite difficult, observationally intensive task, and will not be possible beyond clusters in the local universe. As the number of clusters studied for ICL continues to climb, the need to compare data sets and the difficulty in doing so grows correspondingly. We thus propose to undertake a systematic study of the various definitions of ICL in order to better understand how each relates to the others. Using the simulated clusters presented in Paper I, we will measure the ICL using each of the methods described above, and possibly new methods as well. The goal is to better understand the characteristics of the stellar populations measured by each of the methods, allowing us to better understand exactly what each definition is measuring. Furthermore, we will be able to make observational and simulation data measuring ICL using these varying
6 methods more directly comparable, eliminating much of the current uncertainty arising from the diverse array of measurement techniques currently in use. 3.2. Measuring ICL 3.2.1. Unbound Particles The most physically meaningful definition of ICL is to classify it as stars which are not gravitationally bound to any individual cluster galaxy. Because stars are initially formed bound to their parent galaxies, and processes internal to the galaxies capable of ejecting stars are rare (e.g. Holley-Bockelmann et al. 2005), the vast majority of unbound stars within the cluster must have been stripped from their parent galaxies by interaction effects occurring as a result of the cluster environment. In this way, ICL is a unique product of the cluster environment, providing insight into how this environment effects the evolution of the galaxies within it. Unfortunately, the binding energy of individual stellar tracers is not observationally feasible, as discussed above. In N-body simulations, however, both complete phase space data data for all particles, and the detailed gravitational potential are readily available. Determining the gravitational potential will require specialized tools, such as the program SKID 1. SKID requires the input of several free parameters (most notably, the linking length used to aggregate particles) in order to function properly, and we will need to perform a series of focused tests in order to optimize these parameters. Such tests will consist primarily of assuring that SKID obtains the correct results in simple test situations, such as a fully bound isolated galaxy, two separate nearby galaxies, galaxies of both high and low mass and physical size, etc. 3.2.2. Surface Brightness In Paper I we created surface brightness maps of our clusters, analogous to broadband imaging, and defined ICL as all luminosity at surface brightness fainter than 26.5 mag arcsec 2 in the V -band, in order to make our definition fully observationally tractable. While informed by a qualitative inspection of the ICL morphology, the choice of limiting surface brightness was essentially arbitrary, reflecting our particular bias toward expected properties of the ICL. However, using the data on particle binding energies described above, we will be able to re-assess our choice of limiting surface brightness and attempt to relate the observable quantity surface brightness to the unobservable binding energy. For instance, we will be able to determine the surface brightness limit which best separates bound from unbound particles by measuring the fraction of unbound particles as a function of surface brightness or by finding the surface brightness limit which gives the same ICL fraction as the binding energy definition. Additionally, we will be able to see how these quantities change as the cluster evolves in order to determine how the dynamical state of the cluster affects 1 http://www-hpcc.astro.washington.edu/tools/skid.html
7 the relationship between binding energy and surface brightness. Using the same surface brightness maps from Paper I, we can also use a galaxy profile subtraction technique to measure the ICL content of our clusters. The exact fitting procedure to be used is still to be determined, but will most likely be based on fitting r 1/4 profiles to the large elliptical galaxies in the cluster and exponential profiles to the disk galaxies.. A key feature of this analysis will be to use only the 2-dimensional surface brightness distributions, exactly analogous to observational data. Once again, we will be interested in determining how well the ICL distribution obtained from such a technique is able to match the distribution of unbound stars and how the measured ICL fractions compare. 3.2.3. Line-of-Sight Velocities The final method of ICL detection we will explore is using the line-of-sight velocities of ICPNe to separate galactic luminosity from ICL. This analysis will be highly informed by the results of the ICPNe analysis described in 2. One of our main priorities will be to determine how to use line-of-sight velocities in order to distinguish bound from unbound ICPNe. Arnaboli et al. (2004) found PNe with velocities markedly distinct from that of any nearby cluster galaxy and classified these as ICPNe, while those with velocities similar to galaxies velocities were considered galactic. We would like to quantify how well such procedures work by finding false positive and negative rates. Additionally, we will attempt to develop more refined tools which utilize the full 3-dimensional data available to distinguish bound and unbound particles. One possible method includes using the available incomplete phase space data for each particle to determine the probability that the particle is bound. A novel method for determining the boundary between galactic and intracluster luminosity comes from the preliminary results of the velocity dispersion maps described in 2. This suggests that galaxies embedded in a diffuse ICL halo show a characteristic velocity dispersion profile. That is, within a few effective radii from the galaxy s center, the luminosity of the galaxy dominates that of the ICL, thus the velocity distribution is also dominated by the galaxy s characteristic declining velocity dispersion. However, at the outskirts of the galaxy, the ICL luminosity becomes comparable to the galaxy s, and the velocity distribution becomes dominated by the higher velocity dispersion ICL. A similar velocity dispersion profile was found in the globular cluster systems of elliptical galaxies at the center of massive groups in the local universe by Romanoswky (2006b). This transition from galactic to ICL velocity distribution could potentially be a useful way of separating galaxies from ICL, but much further testing is needed. 3.3. Summary While currently there are a many different functional definitions of ICL in use, there is very little understanding about how these definitions relate to one another. Theoretical studies tend to focus on using binding energy arguments to define ICL. However, this is unobservable in practice, so observational studies rely on the surface brightness distribution of the cluster, or on the line-of-sight velocities of ICPNe to deter-
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10 Fig. 1. The surface brightness distribution of one of our simulated clusters, first presented in Paper I as cluster C2, at z=0. The colorbar at the bottom shows the surface brightness scale in mag/arcsec 2 in the V band. Fig. 2. The mean velocity distribution of the same cluster as shown in Fig. 1. The colorbar at the bottom shows the velocity scale in km/s.
11 Fig. 3. The velocity dispersion distribution of the same cluster as shown in Fig. 1. The colorbar at the bottom shows the velocity dispersion scale in km/s.