Success of Machine Learning algorithms in Dynamical Mass Measurements of Galaxy Clusters

Abstract

In recent years, machine learning (ML) algorithms have been successfully employed in Astronomy for analyzing and interpreting the data collected from various surveys. The need for new robust and efficient data analysis tools in Astronomy is imminently growing as we enter the new decade. Astronomical data sets are growing both in size and complexity at an exponential rate and ML methodologies can revolutionize our ability to interpret observations and provide new means of discovery. In this essay we focus on recent success of ML algorithms in predicting the dynamical mass of galaxy clusters. We discuss the results of the study performed by Ho et al. [1] and their implications, where it was found that ML algorithms outperform conventional statistical methods and can offer a robust and accurate tool for dynamical mass estimation.