NVO Study of Super Star Clusters in Nearby Galaxies

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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

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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 lt V-I lt 0.6)
• Luminosities (-15 lt Mv lt ?, 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.
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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 ?

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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 the brightest
cluster in a galaxy vs. the log of the number of
clusters.
Violent star formation ?
Quiescent star formation ?
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Whitmore, 2000

• The data appear to support a universal model
  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
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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
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Initial Program Galaxies
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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.
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Photometric Calibration
Compared SDSS g-mag from sextractor to HST V-mag
(Rupali Chandar) Scatter 0.1mag
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Analysis with VOplot
Source classification with flux concentration
index (aperture mag isophot mag) VOTables
exported back to Aladin for various source types
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Diffuse sources
Nucleus
Clusters
Saturated stars
Compact objects (stars)
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Compact objects (stars)
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Nucleus
Saturated stars
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Diffuse sources
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Clusters
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Fraction of missing clusters Red crosses 2
mass Blue squares clusters Fraction hidden by
dust (outside center) lt 45 (15/33) 15
(eyeballing) NOTE - Something different near
center ! Position offsets TBD
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Software Tools Development

• How can this work be done more efficiently?

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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
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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.

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Finally, we need to find the Super Star
Clusters!!!
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Future Work

• Additional Tool Development

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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.

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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 gt 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)