Machine Learning Etudes in Astrophysics: Selection Functions for Mock Cluster Catalogs
This addresses the challenge of accurately simulating astrophysical catalogs for researchers analyzing galaxy cluster data, though it is an incremental application of existing machine learning techniques.
The paper tackles the problem of creating realistic mock catalogs of galaxy clusters by using one-class classifiers to learn selection functions from observed ROSAT data, then applying them to simulations. This method reduces bias from manual tuning and enables scalable analysis of cross-correlations between thermal Sunyaev-Zeldovich signals and cluster density maps.
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.