Clusters are under intensive study nowadays, from the detection of
galaxy clusters in deep space to that of open and globular clusters in the
Magellanic Clouds and to faint, embedded clusters deep in our Galaxy's
molecular clouds. Only by studying a large number
of a type of cluster can we obtain statistically sound conclusions as to
their properties.
My second year project can be divided into two major steps:
The first step has been to make the detection of embedded clusters a systematic
process so as to:
i) Eliminate any possible biases which may appear due to
human intervention in the cluster detection;
ii) Make the detection process more rapid and efficient;
iii) Determine the number of embedded clusters in several molecular clouds;
The second step has been to extract some of the fundamental cluster parameters
from the detections. The parameters which are considered are:
i) Structure vs. central condensation (possibly even degrees of structure);
ii) The distribution of luminosity with K-band magnitude (the KLF);
iii) The physical size of the clusters (if possible), attempting to correlate
this to properties such as their age;
iv) The number of cluster members;
v) The Initial Mass Function (IMF) of the cluster: is this universal?
With the above data I will have more knowledge about the environment in which clusters
form and how the environment parameters influence the cluster parameters.
This project does not end here and its following steps are clearly delineated:
i) One logical step is to make the program capable of analyzing large amounts of
data. With this in place we can study entire molecular clouds.
ii) Apply the program to other types of clusters such as galaxy clusters in deep space
images or star clusters in the Magellanic Clouds.
