Decision Tree Classifiers for Star/Galaxy Separation
  We study the star/galaxy classification efficiency of 13 different decision
tree algorithms applied to photometric objects in the Sloan Digital Sky Survey
Data Release Seven (SDSS DR7). Each algorithm is defined by a set of parameters
which, when varied, produce different final classification trees. We
extensively explore the parameter space of each algorithm, using the set of
$884,126$ SDSS objects with spectroscopic data as the training set. The
efficiency of star-galaxy separation is measured using the completeness
function. We find that the Functional Tree algorithm (FT) yields the best
results as measured by the mean completeness in two magnitude intervals: $14\le
r\le21$ ($85.2%$) and $r\ge19$ ($82.1%$). We compare the performance of the
tree generated with the optimal FT configuration to the classifications
provided by the SDSS parametric classifier, 2DPHOT and Ball et al. (2006). We
find that our FT classifier is comparable or better in completeness over the
full magnitude range $15\le r\le21$, with much lower contamination than all but
the Ball et al. classifier. At the faintest magnitudes ($r>19$), our classifier
is the only one able to maintain high completeness ($>$80%) while still
achieving low contamination ($\sim2.5%$). Finally, we apply our FT classifier
to separate stars from galaxies in the full set of $69,545,326$ SDSS
photometric objects in the magnitude range $14\le r\le21$.
