Abstract
The cessation of star formation (quenching) in galaxies is an essential stage in the evolution of a galaxy. Satellite galaxies (SGs) are galaxies in orbit around a central galaxy (CG), larger in size by several orders of magnitude. The properties and interactions of SGs with their environment make them an important topic for learning, both in terms of our understanding of the galaxy’s structure and understanding the environment.
In this work we present a prediction of the quenching of SGs and their main features, according to initial information of satellite galaxies before entering the sphere of influence of the CG. The analysis was performed by a qualitative sample of 118 satellite galaxies from the high-resolution cosmological simulation VELA, which includes a unique data set of satellite galaxies. Using machine learning (ML), we find 3 features needed for SG quenching prediction: orbital eccentricity, SG/CG mass ratio, and SG radius, with another correlative and important feature being SG stellar component shape. Additionally we find the shape evolution of SGs varies dramatically from CG evolution - an important distinction that requires further research.