Machine Learning Etudes in Astrophysics: Selection Functions for Mock Cluster Catalogs

TL;DR

This paper introduces a machine learning approach using one-class classifiers to accurately model selection functions for galaxy cluster catalogs, improving the realism of mock simulations in astrophysics.

Contribution

It presents a novel application of one-class classifiers to derive selection functions from observed data, reducing bias and enabling scalable analysis of large, high-dimensional astrophysical catalogs.

Findings

Method effectively reproduces observed cluster properties.

Reduces bias compared to traditional selection criteria.

Facilitates analysis of Sunya'ev-Zeldovich and X-ray cluster correlations.

Abstract

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.