The SAMI Galaxy Survey: On the importance of applying multiple selection criteria for finding Milky Way Analogues

TL;DR

This study emphasizes the importance of applying multiple selection criteria, including stellar mass and star formation rate, to accurately identify Milky Way Analogues in galaxy surveys, revealing biases in previous methods.

Contribution

It introduces a multi-parameter selection approach for MWAs and demonstrates its effectiveness in reducing biases compared to previous single-parameter methods.

Findings

Including stellar mass and SFR minimizes biases in MWA properties.

Selection without effective radius leads to larger, less accurate MWAs.

Most MWAs are barred spirals with cold rotating disks across various environments.

Abstract

Milky Way Analogues (MWAs) provide an alternative insight into the various pathways that lead to the formation of disk galaxies with similar properties to the Milky Way. In this study, we explore different selection techniques for identifying MWAs in the SAMI Galaxy Survey. We utilise a nearest neighbours method to define MWAs using four selection parameters including stellar mass ($M_{\star}$), star formation rate ($SFR$), bulge-to-total ratio ($B/T$) and disk effective radius ($R_{\rm{e}}$). Based on 15 different selection combinations, we find that including $M_{\star}$ and SFR is essential for minimising biases in the average MWA properties as compared to the Milky Way. Furthermore, given the Milky Way's smaller-than-average size, selection combinations without $R_{\rm{e}}$ result in MWAs being too large. Lastly, we find that $B/T$ is the least important parameter out of the four tested parameters. Using all four selection criteria, we define the top 10 most Milky Way-like galaxies in the GAMA and Cluster regions of the SAMI survey. These most Milky-Way-like galaxies are typically barred spirals, with kinematically cold rotating disks and reside in a wide range of environments. Surprisingly, we find no significant differences between the MWAs selected from the GAMA and Cluster regions. Our work highlights the importance of using multiple selection criteria for finding MWAs and also demonstrates potential biases in previous MWA studies.