Morgan Fouesneau, Ariane Lançon, Rupali Chandar, Bradley C. Whitmore
The majority of clusters in the Universe have masses well below 10^5 Msun.
Hence their integrated fluxes and colors can be affected by the random presence
of a few bright stars introduced by stochastic sampling of the stellar mass
function. Specific methods are being developed to extend the analysis of
cluster SEDs into the low-mass regime. In this paper, we apply such a method to
observations of star clusters, in the nearby spiral galaxy M83. We reassess
ages and masses of a sample of 1242 objects for which UBVIHalpha fluxes were
obtained with the HST/WFC3 images. Synthetic clusters with known properties are
used to characterize the limitations of the method. The ensemble of color
predictions of the discrete cluster models are in good agreement with the
distribution of observed colors. We emphasize the important role of the Halpha
data in the assessment of the fraction of young objects, particularly in
breaking the age-extinction degeneracy that hampers an analysis based on UBVI
only. We find the mass distribution of the cluster sample to follow a power-law
of index -2.1 +/-0.2, and the distribution of ages a power-law of index -1.0
+/-0.2 for M > 10^3.5 Msun and ages between 10^7 and 10^9 yr. An extension of
our main method, that makes full use of the probability distributions of age
and mass of the individual clusters, is explored. It produces similar power-law
slopes and will deserve further investigation. Although the properties derived
for individual clusters significantly differ from those obtained with
traditional, non-stochastic models in ~30% of the objects, the first order
aspect of the age and mass distributions are similar to those obtained
previously for this M83 sample in the range of overlap of the studies. We
extend the power-law description to lower masses with better mass and age
resolution and without most of the artifacts produced by the classical method.
View original:
http://arxiv.org/abs/1202.3135
No comments:
Post a Comment