2019/12/11 by Muhammad Haider Abbas, Abbas, Muhammad Haider
Physics and Astronomy · #Astronomy and Astrophysical Research #Astrophysics of Galaxies (astro-ph.GA) #FOS: Physical sciences #Galaxies: Formation, Evolution, Phenomena #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Stellar, planetary, and galactic studies
paper · pdf · doi:10.48550/arxiv.1912.05316
openalex publication_date 2019/12/11 · openalex created_date 2019/12/26 · openalex updated_date 2026/07/28
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.