National Virtual Observatory Summer School 2005:
Science Theme: Galaxy Properties within Cluster Environments

Observational work of recent years has demonstrated that there has been a significant decline in the global star-formation rate of the Universe with time. In models of hierarchical structure formation (where the masses of galaxies and clusters of galaxies increase with time due to accretion), the cosmic star formation history is driven partly by internal processes (i.e., gas consumption) and partly by environmental effects (i.e., ga;axy interactions). It is believed that these processes affect the amount of gas in galaxies, which is the basic fuel for both the star formation activity as well as the ability to feed any massive central black hole and create an Active Galactic Nucleus (AGN).

Galaxies in groups and clusters are assumed to have no hot gas reservoir with which to refuel the cold gas available for star formation. As a consequence, the star formation rates of galaxies in dense environments quickly declines with time. Similarly, AGN could be fueled by the same cold gas in the disk component of galaxies that is driving the star-formation rate (SFR). In this case, the existence of an AGN might diminish in these dense regions as well. Galaxies in dense environments may experience a variety of other effects which are not included in these models, such as ram pressure stripping of the cold gas from the intracluster medium; galaxy harassment from high velocity encounters with other galaxies; galaxy evolution via mergers and close encounters. For example, several authors have proposed that galaxy-galaxy collisions fuel AGN activity by driving gas into the cores of galaxies and thus onto the black hole. The interpretation of the global decline in star formation and AGN activity, then, requires us to distinguish between internal effects and the effects of the local or large scale environment on star formation.

The NVO provides the means to conduct this type of science, which requires:

• Large samples of multi-wavelength image, catalog, and spectroscopic data.

• Tools to visualize the data in hand (i.e., what is a galaxy cluster? What do galaxies with strong star-formation look like? What do galaxies with active nuclei look like in their spectra? On the sky? Etc.)

• Tools to cross-match data from a variety of archives.

• Tools to conduct scientific analysis on the data in hand and interpret the results (e.g., make histograms, statistical summaries, high-dimensional analyses, etc.).

In this NVO Summer School 2005, we will utilize a large, modern, and well defined galaxy cluster catalog to define the local environments of galaxies. We will extract, analyze, and define galaxy properties that inform us about star-formation, AGN activity, and morphology. We will then study these properties with respect to their environments and physically interpret the results.

The main cluster sample we will utilize throughout the course is based on the spectroscopic data from the Sloan Digital Sky Survey. Specifically, the SDSS C4 Cluster catalog contains 748 groups/clusters over ~2500 square degrees in the Northern Hemisphere (to z ~ 0.15).

A full description of the catalog, a link to the paper describing the catalog, and the catalog itself are available here.

1. VOTables: The cluster data contain over 100 columns. It is available in binary FITs table format, or ascii. Convert these to VOTable. Which column headings can be given UCDs? Which cannot? Why? Create a new VOTable with the galaxy members of each cluster as a sub-table under each cluster.
   
   

2. Data Access Protocols : Make cone calls and SIAP calls for galaxy (or whatever) data and images around the cluster centers. What radius? What happens if the radius is too big? Too small? Get spectra of the cluster Bright Cluster Galaxies (BCGs). Download images where the cluster is near the center (how would you get this information from a SIAP server?). Using NED or other naming services, find all other cluster names for these C4 clusters (e.g., Abell, Zwicky, etc.)
   
   

3. Building a VO Client: Build a client that creates C4 Cluster Summaries. For each cluster, download all SDSS galaxies, 2MASS galaxies(XSC), XMM point sources, etc. Find which space telescopes have pointed image observations of the clusters (HST, Spitzer, etc). Make a VOTable containing lists of image URLs for each cluster. For one (or more cluster), create a multi-wavelength view of the cluster and overplot the galaxies. Discover the color/density relation in clusters.
   
   

4. Skynodes : Upload a VOTable of the cluster positions and search radii to the SkyPortal. Have SkyPortal recreate what was done earlier using the Cone Services (and cross-matching yourself). Compare the hard way (Lesson 3: Cone Services) to the easy way (Lesson 4: SkyPortal). What are their advantages /disadvantages? Build a SkyNode of SDSS galaxy member information (each galaxy has it's associated cluster properties).
   
   

5. ADQL: Searches for data around and in clusters of galaxies require specialized queries. Show how the Region command is useful for clusters.
   
   

6. Correlation Services: Many properties of galaxies within clusters are correlated (amongst themselves as well as to global measures of the cluster). Determine correlations between some physically meaningful galaxy parameters (brightness, H_alpha line-strength, OIII line strength, shape (ellipticity, concentration index, sersic profile, etc) and some cluster properties (velocity dispersion, luminosity, shape, sub-structure, etc). Visualize these correlations.
   
   

7. Integrating Existing Tools: Miller will show an example of calling the SkyNode for the C4 clusters and correcting for missing BCGs. These galaxies must also have k-corrected magnitudes, which will be shown. The Blanton k-correction code can also be wrapped as a service. Fitzpatrick could show a high-level morphology IRAF webservice and apply it to a few large and nearby clusters. If not, GALFIT (a non -IRAF legacy tool for galaxy “shapes” could be wrapped into a stand alone (i.e., non-IRAF) service. We will learn about completeness of the SDSS spectroscopy, the importance of the k-correction, and discover the density/morphology (or sersic profile) relationship. WESIX (which wraps the legacy code Sextractor) will be discussed.

8. Building a VO Service: [IF GIM2D/IRAF webservice is used in 7.---Then the example CONE and SIAP service could serve up the GIM2D catalog outputs (as the coneservice) and the image cutouts (and/or residuals, models) in the SIAP Service.] Another option is to serve up cluster galaxy members with their associated Cluster data as the Cone Service.
   
   

9. Use the VO for Cross-Correlation: Find clusters and their associated images for upload to WESIX (or use the WESIX URL method). Run WESIX and repeat what was done in Exercise 4 (which was first done in Exercise 3). Compare Lesson 3,4,9. Advantages/Disadvantages?
   
   

10. UCDs : Examples of the cluster descriptors and their UCDs. The obvious ones are the positions. But there is also density, src.class.(richness,, distance, etc). Are these useful? What's missing? Galaxies: B/T, asymmetry? Etc. Model paramaters like age; metallicity. Vs. “measured” parameters.