  Making mock simulated catalogs is an important component of astrophysical
data analysis. Selection criteria for observed astronomical objects are often
too complicated to be derived from first principles. However the existence of
an observed group of objects is a well-suited problem for machine learning
classification. In this paper we use one-class classifiers to learn the
properties of an observed catalog of clusters of galaxies from ROSAT and to
pick clusters from mock simulations that resemble the observed ROSAT catalog.
We show how this method can be used to study the cross-correlations of thermal
Sunya'ev-Zeldovich signals with number density maps of X-ray selected cluster
catalogs. The method reduces the bias due to hand-tuning the selection function
and is readily scalable to large catalogs with a high-dimensional space of
astrophysical features.
