NVO Study of Super Star Clusters in Nearby Galaxies

1. NVO Study of Super Star Clusters in Nearby Galaxies Ben Chan, Chris Hanley, and Brad Whitmore • OUTLINE • Science Background and Goals • A Feasibility Study – M51 • Automation

5. Are They Really Globular Clusters ? The young clusters we see in the Antennae (and other galaxies with massive young clusters) have the: • Colors (-0.2 < V-I < 0.6) • Luminosities (-15 < Mv < ?, power law LF with index ~ -2) • Sizes (Reff ~ 4 pc) • Distributions (similar to the field stars) • Spectra (~ 10 objects age dated at 3 - 20 Myr) • Vel. Dispersions (10 - 15 km/s) • Masses (104 - 106) • to be globular clusters with ages in the range 1 to ~ 500 Myr.

6. Mergers, Starbursts, Bars, Rings, and Spirals - (cont.) Roughly 20 gas-rich mergers have now been observed in detail by HST. All show young star clusters. In addition, we find young, massive, compact clusters in: starburst dwarf galaxies (e.g., Meurer et al., 1995), barred galaxies (Barth et al., 1995), spiral galaxies (Larsen & Richtler, 1999) Milky Way and LMC (e.g., Walborn 2000) These clusters have properties similar to those seen in the mergers, but always fewer in number, and generally fainter in luminosity. Science Question # 1 – Is “violent” star cluster formation different than “quiescent” star formation ?

7. Whitmore, 2000 If there are two different modes of star cluster formation we might expect a bimodal distribution in a plot of the magnitude of thebrightest cluster in a galaxy vs. the log of the number of clusters. Violent star formation ? Quiescent star formation ?

8. Whitmore, 2000 • The data appear to support a universalmodel rather than a bimodal model, with the correlation being due to statistics,not physics. • However, this dataset, and reductions, were very inhomogeous. • Our goal is to redo this diagram: • - with a uniform data set (e.g., SDSS, HST) • with uniform analysis (e.g., WESIX) • - for larger dataset (e.g., N~ 100) Best fit M51 Predicted if universal power-law, index = -2

9. Science Question # 2 – What fraction of clusters are hidden by dust ? Neff & Ulvestad (2000) found that their radio sources were “near but not coincident” with the young clusters in the Antennae”. It appears that this was due to a 1.2” positional offset. Once the offset was made we found that 85 % (11 of 13) of the strong radio sources have optical counterparts

10. Initial Program Galaxies

11. Feasibility Study – M51 (using WESIX) DataScope - SDSS g-band image from WESIX - Source extraction and cross matching ALADIN – visualization Voplot – analysis Following Holtzman et al. (1992) observations of “proto-globular” clusters in NGC 1275,we observed the two extremes of the Toomre Sequence of merging galaxies using HST in Cycle 2 and Cycle 5.

12. Photometric Calibration Compared SDSS g-mag from sextractor to HST V-mag (Rupali Chandar) Scatter ~ 0.1mag

13. Analysis with VOplot Source classification with flux concentration index (aperture mag – isophot mag) VOTables exported back to Aladin for various source types

14. Diffuse sources Nucleus Clusters Saturatedstars Compact objects (stars)

16. Compact objects (stars)

17. Nucleus Saturatedstars

18. Diffuse sources

19. Clusters

20. Fraction of missing clusters: Red crosses = 2 mass Blue squares = clusters Fraction hidden by dust (outside center) = < 45 % (15/33) = ~15 % (eyeballing) NOTE: - Something different near center ! Position offsets = TBD

21. Software Tools Development How can this work be done more efficiently?

22. I need images of my target local galaxies? • Single object or list driven application. • Astronomer can either give target names or known coords of target galaxies. • ObjectExtractor will provide a list of services from which images can be extracted. Initial implementation will contain a set list of known SIAP image services. A potential enhancement would be to allow for new service discovery. • FITS images will be saved to local disk. • Ties together multiple services. ObjectExtractor

23. We need catalogs of objects in our images? • CatalogMatch • We have the FITS images, we need to catalog the objects in the image and match to some external catalogs. • Path 1: WESIX: • Best for exploratory studies of small number of images of limited size. • Requires the writing of a Python WESIX interface client. • Path 2: Future PyRAF Implementation: • Catalog generation done in client app. • Smaller bandwidth usage with only query to OpenSkyQuery • More efficient generation of input image object catalogs. • Both paths hide ADQL queries from Astronomers.

24. Finally, we need to find the Super Star Clusters!!!

25. Future Work Additional Tool Development

26. Fixing the WCS • fixWCS • Takes advantage of existing IRAF, PyRAF, and Python applications. • Requires the use of CatalogMatch application output. • Can have updated WCS based upon any of the external catalogs used in cross match. • This software will also give us our position offsets.

27. Conclusions • SDSS images can be used for this project (though will probably also try HST preview images) • M51 will fit nicely on the Mv(brightest) vs. log N diagram > further support for universal model. • NVO tools will be very useful for the project (e.g., datascope, WESIX, Aladin, VOPLOT). • Automating the program (e.g., SIAP services and OPENSKYQUERY) is feasible, but will take additional work (e.g., developing a python client for WESIX)