Segmenting Superbubbles in a Simulated Multiphase Interstellar Medium using Computer Vision

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.

Figures (15)

• Figure 1: Segmentation results demonstrating superbubbles isolated from the input density ($n$) cube using our proposed tracking framework. The upper row features a supernova remnant case at $t=191.8$ Myr, while the lower row shows a superbubble identified at $t=213.0$ Myr in the same simulation.
• Figure 2: Density ($n$), temperature ($T$), and vertical velocity ($v_z$) from the original MHD simulation dataset hill2012vertical, captured at $t = 191.9$ Myr. The leftmost column represents $z = -256$ pc, the second column $z = 0$ (the Galactic plane), and the rightmost column $z = +256$ pc.
• Figure 3: Visualization presenting 3D density cubes of hot gas extracted from the magnetohydrodynamic simulation dataset hill2018effect. The cubes are thresholded at $T > 10^{5.5}$ K to highlight hot gas structures at $t = 209$ Myr. The rotation around the $z$-axis provides different perspectives, offering a clearer view of the spatial distribution and morphology of these high-temperature regions.
• Figure 4: Model architecture of the proposed Astro-UNETR model. The 3D data cubes are input to the Astro-UNETR, which employs swin-transformer blocks liu2021swin to learn the 3D semantic representation of superbubble morphology. High-level features are refined by a bottleneck layer and then upsampled to their original dimensions via deconvolution layers and ResNet blocks, producing a 3D semantic segmentation of all bubbles.
• Figure 5: SAM2 model architecture illustration. To isolate a specific bubble, the semantic segmentation output from Astro-UNETR is fed into SAM2 ravi2024sam, a video object segmentation model that identifies the bubble corresponding to the superbubble designated by the supernova (SN) log, which yields an isolated 3D instance segmentation of the target bubble. Subsequent data cubes of the same bubble are segmented over time using the stored model features.