A New Superbubble Finding Algorithm: Description and Testing

TL;DR

This paper introduces a novel, minimally parameterized algorithm for detecting superbubbles in HI maps, validated through comparison of simulated and observed galactic disks, aiding in testing stellar feedback models.

Contribution

The paper presents a new superbubble detection algorithm with automated background separation, tested on both simulated and observed galaxy data, advancing systematic superbubble analysis.

Findings

Superbubbles in simulated and observed disks are qualitatively similar in size and distribution.

Simulated superbubbles have lower central HI column densities than observed ones.

The algorithm effectively identifies superbubbles, facilitating standardized stellar feedback testing.

Abstract

We present a new algorithm for identifying superbubbles in HI column density maps of both observed and simulated galaxies that has only a single adjustable parameter. The algorithm includes an automated galaxy-background separation step to focus the analysis on the galactic disk. To test the algorithm, we compare the superbubbles it finds in a simulated galactic disk with the ones it finds in 21cm observations of a similar galactic disk. The sizes and radial distribution of those superbubbles are indeed qualitatively similar. However, superbubbles in the simulated galactic disk have lower central HI column densities. The HI superbubbles in the simulated disk are spatially associated with pockets of hot gas. We conclude that the algorithm is a promising method for systematically identifying and characterizing superbubbles using only HI column density maps that will enable standardized tests of stellar feedback models used in galaxy simulations.