Segmenting Superbubbles in a Simulated Multiphase Interstellar Medium using Computer Vision

TL;DR

This paper presents a novel computer vision method using 3D transformer models to accurately segment and analyze superbubbles in simulated interstellar medium data, revealing their growth and interactions.

Contribution

The study introduces a new 3D segmentation and tracking technique for astrophysical structures using advanced transformer-based computer vision models.

Findings

Successfully generated detailed 3D segmentation masks of superbubbles

Revealed insights into superbubble growth, energy retention, and interactions

Demonstrated the method's effectiveness in complex astrophysical simulations

Abstract

We developed a computer vision-based methodology to achieve precise 3D segmentation and tracking of superbubbles within magnetohydrodynamic simulations of the supernova-driven interstellar medium. Leveraging advanced 3D transformer models, our approach effectively captures the complex morphology and dynamic evolution of these astrophysical structures. To demonstrate the technique, we specifically focused on a superbubble exhibiting interesting interactions with its surrounding medium, driven by a series of successive supernova explosions. Our model successfully generated detailed 3D segmentation masks, enabling us to visualize and analyze the bubble's structural evolution over time. The results reveal insights into the superbubble's growth patterns, energy retention, and interactions with surrounding interstellar matter. This interdisciplinary approach not only enhances our understanding of superbubble dynamics but also offers a robust framework for investigating other complex phenomena in the cosmos.